Optimization of Wireless Body Area
Networks for Medical Applications
using the Selective Transmission
Algorithm
Soh Brendard Nji
A thesis submitted in fulfillment of the requirements for the degree of
Master of Science (by Research)
Performed at
Swinburne University of Technology
2017
ii
ABSTRACT
If a supermarket existed where humans could top up years to their lives, it would sure
operate 24 hours a day all 365/366 days in a year. Wireless Body Area Networks
(WBANs) capture vital signs from our bodies and transmit to medical personnel real
time, so they can take remedial action against life-threatening ill health conditions. This
technology has not been adopted widely partly due to a congested wireless transmission
channel which leads to low packet reception rate, high packet failure rate, high end-to-
end delay, and high energy consumption by resource-constrained sensors. In this study,
I propose the channel-decongesting Selective Transmission Algorithm (STA), which
employs the 0.0033 Hz body temperature signal and the 0.0167 Hz higher frequency
heart rate signal. Using conceptual, experimental and analytical quantitative and
qualitative methods, I compared the performance of STA with Value Reporting (VR)
from which it was developed, using Castalia Simulator. Results revealed that the STA
body temperature and heart rate variants decongested the transmission channel by 44.4
% and 36 % respectively; increased packet reception by 16.91 % and 17.11 %
sequentially; curbed packet loss rate by 16.61 % and 16.66 % in that order; curtailed
end-to-end delay by 0.77 % and 1.07 % successively; lowered energy consumption by
16.64 % and 16.65 % consecutively across payload sizes relative to Value Reporting. In
addition, reducing the duty cycle to 20 % caused the STA body temperature and heart
rate variants to increase channel decongestion by up to 10.95 % and 12.85 %
respectively; as well as curb energy consumption by up to 64.36 % and 63.75 %
consecutively. However, this was at the expense of packet reception and end-to-end
delay. While the former declined by up to 26.17 % and 26.10 %, the latter hiked by up
to 97.40 % and 97.75 % for the body temperature and heart rate STA variants
respectively, across payload sizes. The STA body temperature and heart rate variants
decongested the channel 1.80 and 1.56 times with no duty cycling, and between 1.57
and 2.24 times with duty cycling respectively relative to periodic transmissions, while
the related Send-on-delta scheme did so between 1.57 and 5.85 times. These results add
to the body of knowledge on WBANs, constitute resource material for equipment
manufacturers in this sector, and contribute towards improving effectiveness, efficiency,
and adoption of the WBAN technology, ultimately leading to a healthier people.
iii
ACKNOWLEDGEMENT
It would be a showcase of ingratitude if I take total credit for the realization of this
study. It came to fruition as a result of contributions from many in different capacities.
My sincere gratitude goes to the Research & Consultancy Office (RCO) at Swinburne
Sarawak under the patronage of Associate Professor Wallace Wong. It helped to grease
some frictional components of my research experience.
In addition, many thanks go to my family - Mr. & Mrs. Nji, Julius, Evira, Edmond,
Ganye, Magrine, & Ikatata for their relentless effort in ensuring that I had a conducive
environment to carry out this research.
Next, the guidance and direction of my supervisory team led by Dr. Chua HongSiang
cannot be overemphasized.
To all those who contributed in one way or the other, I say “Thank you!”
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DECLARATION
I hereby declare that this dissertation – “Optimization of Wireless Body Area Networks
for Medical Applications using the Selective Transmission Algorithm” contains no
material that has been accepted for the award of any other degree or certificate in any
educational institution and, to the best of my knowledge and belief, it contains no
material previously published or written by another person, except where due reference
is made in the text of the thesis.
Name: Soh Brendard Nji
Signature:
Date: 10 July 2017
v
TABLE OF CONTENT
ABSTRACT ............................................................................................................................. II
ACKNOWLEDGEMENT ............................................................................................................. III
DECLARATION ........................................................................................................................... IV
TABLE OF CONTENT .................................................................................................................. V
LIST OF FIGURES AND TABLES ............................................................................................. VII
LIST OF FIGURES ...................................................................................................................... VII
LIST OF TABLES ......................................................................................................................... IX
GLOSSARY ............................................................................................................................. X
CHAPTER 1: INTRODUCTION ................................................................................................... 1
1.1 RATIONALE ............................................................................................................................ 1
1.2 SCOPE OF RESEARCH .......................................................................................................... 2
1.3 RESEARCH OBJECTIVES ............................................................................................................... 2
1.4 RESEARCH QUESTIONS ............................................................................................................... 2
1.5 BRIEF SUMMARY OF FINDINGS ......................................................................................... 3
1.6 CONTRIBUTION AND SIGNIFICANCE OF RESEARCH ................................................... 4
1.7 THESIS OUTLINE ................................................................................................................... 5
CHAPTER 2: LITERATURE REVIEW ....................................................................................... 7
CHAPTER 3: THEORETICAL FRAMEWORK ......................................................................... 12
3.1 HYPOTHESIS ............................................................................................................................ 12
3.2 OVERVIEW OF WIRELESS BODY AREA NETWORKS ................................................... 12
3.2.1 WIRELESS BODY AREA NETWORK ARCHITECTURE.................................................................. 12 3.2.2 NODE ARCHITECTURE ............................................................................................................. 13 3.2.3 WBAN DESIGN REQUIREMENTS AND CHALLENGES ............................................................... 14 3.2.5 WBAN APPLICATIONS ............................................................................................................. 16
3.3 STANDARD USED: ...................................................................................................................... 17
3.4 METRICS RATIONALE......................................................................................................... 25
3.4.1 PACKET RECEPTION RATE AND PACKET FAILURE ................................................... 29 3.4.2 END-TO-END DELAY PRESUMPTION ............................................................................. 33 3.4.3 ENERGY CONSUMPTION PRESUMPTION ...................................................................... 36 3.4.4 SIGNAL-TO-NOISE RATIO, BIT ERROR RATE, AND PACKET ERROR RATE ........... 38 3.4.5 PATH LOSS (FADE DEPTH) ....................................................................................................... 40 3.4.6 DUTY CYCLE SUPPOSITION ............................................................................................. 41 3.4.7 CONFIDENCE INTERVAL .................................................................................................. 43
3.5 THE SELECTIVE TRANSMISSION ALGORITHM UNDERPINNING ................................................... 44
3.6 CHOICE OF SIMULATOR ............................................................................................................. 52
CHAPTER 4: RESEARCH METHODOLOGY ........................................................................... 55
4.1 CONCEPTUALIZATION AND MODELING ..................................................................................... 56
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4.1.1 HYPOTHESIS ........................................................................................................................... 56 4.1.2 STRENGTH OF CHOSEN RESEARCH METHOD .......................................................................... 57 4.1.3 NODE TOPOLOGY AND EXPERIMENTAL PARAMETERS ........................................................... 57
4.2 PERFORMANCE MEASURES ....................................................................................................... 59
4.2.1 CHANNEL DECONGESTION ..................................................................................................... 60 4.2.2 PACKET RECEPTION RATE ....................................................................................................... 60 4.2.3 PACKET LOSS RATE ................................................................................................................. 61 4.2.4 END-TO-END DELAY ................................................................................................................ 62 4.2.5 ENERGY CONSUMPTION......................................................................................................... 62 4.2.6 BIT ERROR RATE ...................................................................................................................... 63 4.2.7 SIGNAL-TO-NOISE RATIO ........................................................................................................ 63 4.2.8 FADE DEPTH DISTRIBUTION .................................................................................................... 64
4.3 EXPERIMENTATION ................................................................................................................... 64
4.4 ANALYSIS ............................................................................................................................ 65
CHAPTER 5: RESULTS AND DISCUSSION .............................................................................. 67
5.1 RESULTS ............................................................................................................................ 67
5.1.1 CHANNEL DECONGESTION ..................................................................................................... 67 5.1.2 PACKET RECEPTION RATE (PRR) .............................................................................................. 67 5.1.3 PACKET LOSS RATE ................................................................................................................. 68 5.1.4 END-TO-END DELAY ................................................................................................................ 69 5.1.5 ENERGY CONSUMPTION......................................................................................................... 71 5.1.6 EFFECT OF DUTY CYCLING ON THE SELECTIVE TRANSMISSION ALGORIGHM ......................... 72 5.1.7 STA PERFORMANCE MEASURES ............................................................................................. 85
5.2 DISCUSSION ........................................................................................................................... 91
5.2.1 CHANNEL DECONGESTION ..................................................................................................... 91 5.2.2 PACKET RECEPTION RATE ....................................................................................................... 92 5.2.3 PACKET LOSS RATE ................................................................................................................. 93 5.2.4 END-TO-END DELAY ................................................................................................................ 93 5.2.5 ENERGY CONSUMPTION......................................................................................................... 94 5.2.6 EFFECT OF DUTY CYCLING ON THE SELECTIVE TRANSMISSION ALGORIGHM ......................... 95
CHAPTER 6: CONCLUSION AND FUTURE WORK................................................................ 98
6.1 CONCLUSION ............................................................................................................................ 98
6.2 FUTURE WORKS ....................................................................................................................... 100
REFERENCES .......................................................................................................................... 101
APPENDIX .......................................................................................................................... 111
APPENDIX A: DETAILED SLOTTED CSMA/CA FLOWCHART ............................................................. 111
APPENDIX B: SLOTTED AND UNSLOTTED CSMA/CA FLOWCHART .................................................. 112
APPENDIX C: POSITION AND RANGE OF WBANS WITH RESPECT TO OTHER WIRELESS NETWORKS ................................................................................................................... 113
APPENDIX D: DATA RATES AND POWER CONSUMPTION OF WBAN WITH RESPECT TO OTHER 802.15 VARIANTS. ............................................................................................................. 114
LIST OF PUBLICATIONS ......................................................................................................... 115
vii
LIST OF FIGURES AND TABLES
LIST OF FIGURES
Figure 1: Wireless Body Area Network architecture [24]. ............................................. 13
Figure 2: Sensor node architecture. ................................................................................. 14
Figure 3: IEEE 802.15.4 architecture [42]. ..................................................................... 17
Figure 4: IEEE 802.15.4 PHY layer frequency bands [41]............................................. 19
Figure 5: IEEE 802.15.4 MAC frame and PHY packet structure [41]. .......................... 19
Figure 6: (a) Star, (b) Mesh, and (c) Tree topologies in IEEE 802.15.4 standard [41]... 20
Figure 7: IEEE 802.15.4 superframe [50]. ...................................................................... 21
Figure 8: IEEE 802.15.4 slotted CSMA/CA mechanism [50]. ....................................... 22
Figure 9: Hidden node problem. ..................................................................................... 23
Figure 10: Packet transmission sequence of IEEE 802.15.4 [61]. ................................. 25
Figure 11: IEEE 802.15.4 Markov chain model for the CSMA/CA algorithm [58]. ..... 27
Figure 12: IEEE 802.15.4 PHY and MAC model flowchart, courtesy of [64]. .............. 28
Figure 13: Reliability per payload for a WPAN, courtesy of [63]. ................................. 32
Figure 14: Failure probability per payload for a WPAN, courtesy of [63]. .................... 32
Figure 15: Delay per payload for a WPAN, courtesy of [63]. ........................................ 36
Figure 16: Energy consumption per payload in a WPAN, courtesy of [58]. .................. 38
Figure 17: Relationship between BER, SER, and PER with deteriorating SNR,
courtesy of [69]. .............................................................................................................. 40
Figure 18: Packet loss vs. path loss per payload, courtesy of [72]. ................................ 41
Figure 19: Relationship between throughput; delay; packet loss; power consumption;
and duty cycle for a constant BO of 5 and varying SO, courtesy of [76]. ...................... 42
Figure 20: Selective Transmission algorithm flowchart. ............................................... 45
Figure 21: Pseudocode of Selective Transmission algorithm. ........................................ 46
Figure 22: Communication within the node composite module of Castalia [80]. .......... 54
Figure 23: Engineering research methodology (adapted from [58]). .............................. 55
Figure 24: Body sensor placement for experiments. ....................................................... 58
Figure 25: Channel decongestion comparison per payload between VR and STA. ....... 67
Figure 26: Comparison of percentage packet reception between VR and
STA using body temperature and heart rate signal types. ............................................... 68
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Figure 27: Comparison of failed packets distribution per payload between
VR, STA (Body Temperature), and STA (Heart Rate). .................................................. 69
Figure 28: End-to-end delay comparison between VR & STA, and percentage
delay reduction of STA body temperature and heart rate variants relative to VR. ......... 70
Figure 29: Comparison of energy consumption per payload between VR and
STA variants (body temperature and heart rate). ............................................................ 72
Figure 30: Percentage channel decongestion of STA variants
(body temperature and heart rate) per payload per duty cycle. ....................................... 73
Figure 31: Percentage packet reception per payload per duty cycle of
STA variants (body temperature and heart rate). ............................................................ 76
Figure 32: Comparison of percentage failed packets per payload per duty cycle of
STA variants. .................................................................................................................. 83
Figure 34: Comparison of end-to-end delay per duty cycle between STA variants. ...... 84
Figure 35: Comparison of energy consumption per duty cycle per payload
between STA variants. ................................................................................................... 84
Figure 36: Comparison of average BER per duty cycle per payload
between VR and STA variants. ....................................................................................... 86
Figure 37: Comparison of SNR per payload per duty cycle between
VR and STA variants. ..................................................................................................... 89
Figure 38: Comparison of Fade depth distribution histograms between
VR and STA variants per payload. ......................................................................... 91
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LIST OF TABLES
Table 1: Major WBAN application parameters [37]....................................................... 16
Table 2: IEEE 802.15.4 constants and attributes courtesy of the
IEEE 802.15.6-2006 standard [41].................................................................................. 26
Table 3: Experimental parameters .................................................................................. 59
Table 4: Minimum, maximum, average delay comparison; percentage reduction of
end-to-end delay by STA body temperature and heart rate variants relative to VR. ...... 70
Table 5: Comparison of energy consumed between VR and STA variants (body
temperature and heart rate).............................................................................................. 71
Table 6: Comparison of channel decongestion per duty cycle per payload
between STA variants (body temperature and heart rate). .............................................. 73
Table 7: Comparison of the number and percentage of packets received per
payload per duty cycle between STA variants (body temperature and heart rate). ........ 75
Table 8 (a), (b), (c), (d): Number and percentage of failed packets
per payload per duty cycle of STA variants. ................................................................... 79
Table 9: Comparison of average BER per duty cycle per payload between
VR and STA variants. ..................................................................................................... 85
Table 10: Comparison of average SNR per payload per duty cycle between
VR and STA variants. ..................................................................................................... 88
x
GLOSSARY
ACK: A packet transmitted by a receiver to the transmitter in order to confirm
successful frame reception from the transmitter. It contains the sequence
number of the frame it is acknowledging, and has no payload.
AWGN: Additive White Gaussian Noise. It is a channel model consisting of a
Gaussian amplitude distribution and a linear addition of wideband or
white noise with a constant spectral density (Watts per Hertz of
bandwidth) as the only source of disturbance.
BAN/BANs: Body Area Network. A number of nodes placed on, in, or around the
body to sense and transmit vital signs to medical doctors, caregivers, and
other stakeholders.
BER: Bit Error Rate. It is the ratio of the number of erroneous bits to the total
number of bits transmitted per unit time, expressed as a percentage. It
very much influences the throughput and quality of transmission.
BI: Beacon Interval. The space between two adjacent beacons, considered as
the length of a superframe in beacon-enabled CSMA/CA.
BO: Beacon Order. Together with SO, it is responsible for the IEEE 802.15.4
superframe structure, and used to implement duty cycling.
CCA: Clear Channel Assessment is a CSMA/CA mechanism used by nodes
(sensors) to determine if the channel is free or not prior to transmitting, in
order to prevent signals from two or more sensors colliding.
CAP: Contention Access Period. The initial stage of the CSMA/CA channel
access mechanism in which all nodes in the BAN “fight” to access the
channel in order to transmit their data.
Channel: Percentage ratio of packets transmitted by one protocol/algorithm
decongestion (Selective Transmission in this case) to those transmitted by other
protocols/algorithms, for the same number of packets generated.
CSMA/CA: Carrier Sense Multiple Access with Collision Avoidance. It is a wireless
channel access mechanism which prevents collision from nodes using the
same channel to transmit data, by proposing algorithms which let them
transmit only when the channel is sensed as idle.
xi
Coordinator: A device/sensor, which aggregates data from other sensors and either
gives feedback to the patient, transmits the data to a base station, or both.
It also synchronizes and schedules the other sensors or nodes.
CPU: Central Processing Unit. This is a small integrated circuit chip which
handles computations of a node.
CW: Contention Window. It is the number of back-off periods for which the
channel must be sensed idle prior to being accessed. It is initialized to a
value of 2, corresponding to two CCAs. Channel access is done during
the first 8 symbols of each back-off period.
dB: Decibel. This is a logarithmic ratio comparing power to some reference
power. It can also compare other parameters such as current, voltage,
amongst others.
DSSS: Direct Sequence Spread Spectrum. A spread spectrum technique wherein
the original signal is multiplied with a pseudo random noise spreading
code, which has a higher chip rate, and reduces the signal’s interference.
Fade depth: Represented as Fade Depth Distribution, this is a histogram which
portrays an estimation of the deviation of the attenuation affecting a
signal over the propagation medium. It affects the strength of the signal
arriving the receiver.
FCC: Federal Communications Commission. An independent US based body
charged with the regulation of radio communication transmissions,
amongst other communication types.
FEC: Forward Error Correction. It is a data coding technique used to control
erroneous data communications over noisy error-prone transmission
channels. The transmitter transmits a redundant error-correcting code
which the receiver uses to correct some erroneous bits without requiring
a retransmission.
GTS: Guaranteed Time Slots. Up to 7 slots reserved in the active portion of the
superframe for direct transmission of priority data without going through
the contention process.
IFS: Interframe Space. It is the time required by the MAC layer to process a
frame received from the PHY layer, before transmitting an ACK packet
to acknowledge it.
xii
IoT: Internet of Things. It is the interconnection of physical devices embedded
with electronics to enable them collect and exchange data with each
other.
ISS: Interference Signal Strength. It is a measure of the power of an
interfering signal from an external source or network in WBAN
communications.
Latency: A measure of the time elapsed from when data is sensed by the sensors
until when it is received by the coordinator.
MAC: Media Access Control. Second layer of the IEEE 802.15.4 protocol stack
used to define rules for fair and orderly access to the shared wireless
medium, enabling efficient bandwidth utilization and avoiding packet
collisions.
macMinBE: Minimum back-off exponent value used in the calculation of back-off
time. It has a default value of 3.
mW: Unit of power measurement, stands for milliwatt. It is more suitable for
use in WBAN applications, due to the small power involved.
NB: Number of back-offs. The number of times the CSMA/CA algorithm is
required to back-off in the course of accessing the channel. It is
initialized to 0 prior to every new transmission attempt.
NED: A network description language used by OMNET++ and associated
programs to create their topology, which comprises modules, parameters,
interfaces, and messages.
Node: A device/sensor worn on, in, or around the body, sampling physiological
signals for onward transmission to a coordinator, which either gives
feedback to the subject or passes the data to a base station, or other
device.
PER: Packet Error Rate. Used to evaluate the transmitted data, and is the ratio
of erroneous transmitted data packets (having at least one erroneous bit)
to the total number of packets transmitted.
PHY: Physical layer. The WBAN layer which activates and deactivates the
radio transceiver, performs CCA, modulates and transmits data.
PRR: Packet Reception Rate. The ratio of total number of packets received to
the total number of packets sent by nodes in the Body Area Network.
xiii
PSDU: Protocol Service Data Unit. It is the frame which is received from the
MAC layer by the physical layer in IEEE 802.15.4, which becomes the
physical layer’s payload.
QoS: Quality of Service. Comprising of metrics such as error rate, bit rate,
throughput, latency and jitter, this is a means to measure the performance
of WBAN communication as perceived by users of the network.
RSS: Received Signal Strength (Received Signal Strength Indicator for RSSI).
It is a measure of the power of a received signal at the radio of a receiver
in WBAN transmissions.
SAR: Specific Absorption Rate. A measure of power absorbed per unit mass by
body tissue. It is the initial rate of temperature rise as a function of the
specific heat capacity of the tissue. The FCC sets a safe value of 1.6
W/kg for devices communicating within 20 cm of the human body.
SD: Superframe Duration. The active portion of a superframe consisting of 16
equally sized slots for the transmission of data by nodes in a WBAN. It
consists of the CAP and an optional CFP.
Send-on-delta: An event-based data capturing concept which captures data from the
environment if its value deviates by delta, defined as a significant change
in its value.
SIFS: Short Interframe Space. Time required by the MAC to process a frame of
up to 18 bytes received from the PHY layer, prior to transmitting an
ACK packet to acknowledge it.
S-ALOHA: Slotted ALOHA. An improvement of the original ALOHA, introducing
discrete time slots, increasing throughput, and decreasing collisions since
a node can only transmit at the beginning of a time slot.
SNR: Signal to Noise Ratio. It is the difference in decibels (dB) between the
received signal and the background noise (noise floor), and a measure of
the quality of received signals. A higher SNR symbolizes better signal
quality. Lower SNR forces the node to communicate at lower data rates,
hence low throughput.
SO: Together with BO, it is responsible for the IEEE 802.15.4 superframe
structure, and used to implement duty cycling.
xiv
STA: Selective Transmission Algorithm. A channel decongestion algorithm
aimed at optimizing packet reception, packet failure, end-to-end delay,
and energy consumption in Wireless Body Area Networks.
TDMA: Time Division Multiple Access. A channel access mechanism wherein
many devices share the wireless medium by being assigned individual
slots on a single channel, and transmit one after the other.
Throughput: Percentage ratio of packets received by the coordinator to packets
transmitted by the sensor nodes in the course of the simulation
((𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑏𝑦 𝑐𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑜𝑟
𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑏𝑦 𝑠𝑒𝑛𝑠𝑜𝑟𝑠) ∗ 100)
UWB: Ultra-wideband. A wireless communication technology which transmits
ultra-low power radio signals with very short electrical pulses at speeds
up to 1 gigabit per second across the entire frequency spectrum.
VR: Value Reporting. An integrated application in Castalia Simulator to sense
and transmit physiological signals from sensors to a coordinator.
Vital signs: Disease-indicating symptoms, e.g. temperature, blood glucose level.
WBAN: Wireless Body Area Network (BAN). An interconnection of sensors
place in, on, or around the body to capture symptoms of ill health
conditions and transmit to doctors/stakeholders for curative measures.
WPAN: Wireless Personal Area Network. A network interconnecting devices on
a person’s body, or his workspace via wireless means.
WBANs: More than one WBAN.
WSN/WSNs: Wireless Sensor Network. It constitutes a network of interconnected
sink(s) and nodes which are usually deployed in their numbers to monitor
physical conditions across vast terrains.
1
CHAPTER 1: INTRODUCTION
1.1 Rationale
Thanks to the Internet of Things (IoT) concept, Wireless Body Area Network (WBAN)
technology is geared at completely revolutionizing the manner in which healthcare is
administered, giving it a mobile perspective. With this technology, patients are not
required to displace themselves over long distances, only to follow seemingly endless
queues at the hospital in order to consult a medical doctor, aggravating their ill health
condition in the process. This inconvenience can be curbed by wearing sensor nodes on,
around, or in the body. Such sensors on a single body together constitute a Wireless
Body Area Network. They capture vital signs (physiological signals) from the body and
transmit them real-time to medical doctors, who in turn respond with feedback to
reverse lethal ill health conditions in a timely manner. This remedial function by
medical doctors can be achieved on several patients within a short period of time, and
without any need for displacement on the patient’s part. As enticing and therapeutic as
this technology seems, it has not received the extensive adoption one would have
expected, due to certain bottlenecks. Nodes suffer from limited power which in many
cases is battery supplied, reducing their lifespan, hence effectiveness and efficiency.
Their processing and storage capabilities are also limited, meaning that they cannot hold
data of considerable size at any one time. Since space must exist to accommodate
transmitted data, packets on the wireless medium waiting to be received sometimes
hang around for too long, increasing the latency [1]. In a worst-case scenario, they are
discarded pending retransmission, which wastes even more energy. These waiting
packets also collide with other packets, leading to data loss and retransmissions once
more. In order to address these resource constraints, I propose the Selective
Transmission Algorithm (STA), which is a channel decongestion algorithm aimed at
optimizing packet reception, curbing packet loss, reducing end-to-end delay, and
limiting the waste of energy in wireless body area networks (WBANs). To measure the
gains of STA, I used Value Reporting (VR), which is an application that senses and
transmits physiological signals [2], for control experiments, employing bit error rate
(BER) and signal-to-noise ratio (SNR) as benchmarking criteria in a lossy environment
characterized by fade depth distribution. VR also served as the building block of STA.
2
1.2 Scope of research
This research focuses on the optimization of Wireless Body Area Network parameters –
decongestion, packet reception rate (PRR), packet failure rate, end-to-end delay, and
energy consumption as used for medical applications, in a wireless channel
characterized by path loss and temporal variability. It is restricted to a fixed WBAN
void of mobility. In addition, it is also constrained to the definition of a Wireless
Personal Area Network as stipulated by the IEEE 802.15.4 standard [3].
1.3 Research objectives
The objectives of this research are as follows:
To conceive, model, and implement a channel decongestion algorithm - the
Selective Transmission Algorithm in order to curb congestion in WBANs.
To evaluate performance gains of the Selective Transmission Algorithm against a
standard - Value Reporting in terms of channel decongestion, packet reception,
packet failure, end-to-end delay, and energy consumption with focus on medical
applications.
To analyze the effects of payload size and duty cycling on performance of the
Selective Transmission Algorithm.
To validate the Selective Transmission Algorithm with respect to bit error rate
(BER) and signal-to-noise ratio (SNR) in a lossy wireless channel characterized by
fade depth distribution.
1.4 Research questions
In the course of this study, I provide answers to the following questions:
How effective is the channel decongesting Selective Transmission Algorithm in
optimizing packet reception, packet failure, end-to-end delay, and energy
consumption in wireless body area networks for medical applications?
What effect does payload size have on performance of the Selective Transmission
Algorithm?
How does duty cycle affect performance of the Selective Transmission Algorithm?
How valid is the Selective Transmission Algorithm with reference to bit error rate
and signal-to-noise ratio in a lossy wireless channel?
3
In order to answer the above questions, I adopted conception, experimental, and
analytical quantitative and qualitative methods to develop, scrutinize and evaluate the
Selective Transmission Algorithm using Castalia Simulator. These methods suit the
nature of the research. Furthermore, the choice of simulator-based experiments as
opposed to physical experiments on human subjects, offers convenience.
1.5 Brief summary of findings
As per experiments conducted, the proposed Selective Transmission Algorithm (STA)
body temperature and heart rate variants decongested the transmission channel by 44.40
% and 36.00 % respectively; increased packet reception by 16.91 % and 17.11 % in that
order; reduced packet loss by 16.61 % and 16.66 % consecutively; reduced end-to-end
delay by 0.77 % and 1.07 % successively; reduced energy consumption by 16.64 % and
16.65 % respectively across payload sizes, relative to Value Reporting (VR). This
optimized the reception of updated as opposed to redundant signals. In addition,
reducing the duty cycle to 0.2 (20 %) which was the lowest adopted for this study,
caused the STA body temperature and heart rate variants to increase channel
decongestion by up to 10.95 % and 12.85 % respectively; reduce packet reception and
hence increase packet failure by up to 26.17 % and 26.10 % successively; increase end-
to-end delay by 97.40 % and 97.75 % consecutively; reduce energy consumption by up
to 64.36 % and 63.75 % in that order, relative to VR. Furthermore, the STA body
temperature and heart rate variants reduced BER by 94.48 % and 77.03 % respectively
relative to VR. This reduction in BER changed to 83.52 % and 91.11 % respectively
with a 20 % duty cycle. An 8.9736 % and 8.9313 % increase in signal-to-noise ratio
(SNR) was achieved by the body temperature and heart rate STA variants respectively.
This increase went up to 9.4757 % and 9.8165 % successively when a 20 % duty cycle
was adopted. Moreover, STA packets experienced increased fading between -5 dBm
and 5 dBm relative to Value Reporting packets. While more packets of the STA heart
rate variant experienced fading with a 25-byte payload, more STA body temperature
variant packets experienced fading with a 100-byte payload at this duty cycle. The STA
body temperature and heart rate variants decongested the channel 1.80 and 1.56 times
with no duty cycling, and between 1.57 and 2.24 times with duty cycling respectively
relative to periodic transmissions, while the Send-on-delta scheme (event-based data
scanning and dispatching concept) did so between 1.57 and 5.85 times.
4
1.6 Contribution and significance of research
Though a number of researchers have done some work on the optimization of WBANs,
most of them implemented their scenarios in a flat environment, void of the
complexities associated with a real world environment. The main contribution of this
research is the design and implementation of the Selective Transmission Algorithm,
which decongests the channel and reduces energy depletion in a wireless environment
characterized by path loss and temporal channel variation. The algorithm segregates
packets, only allowing the transmission of medically significant packets as opposed to
other mechanisms which capture and transmit physiological signals periodically,
regardless of their relevance. The proposed Selective Transmission Algorithm
consequently comes up with a mechanism to have a fewer number of packets on the
transmission medium at any one time, which reduces collisions and retransmissions,
conserving channel and energy resources which are limited in resource-constrained
wireless body area network settings. The potential medical implication of this study
cannot be overemphasized.
This research does not only dwell on traditional metrics such as packet reception
rate, packet failure rate, end-to-end delay and energy consumption, but delves into
performance characteristics such as the bit error rate (BER) and signal-to-noise
ratio (SNR), in a near-real-world environment which is characterized by path loss
abstracted as fade depth distribution, and randomized by its temporal variation. The
trends obtained from the culmination of these parameters and metrics are invaluable
to equipment manufacturers in the domain of wireless body area networks, since
they help in the matching of physical properties of the environment with intended
functional properties of sensing equipment for different physiological signals.
Good health and longevity are amongst the most cherished assets of humans. The
role of medical doctors to sustain life through consultation of ailing patients is
hampered by the inability of the latter to effectively or wholly report the vital signs
which lead doctors to administer effective healthcare. While some patients lack the
ability to describe their vital signs to medical personnel succinctly, some exaggerate
them, and others do not even perceive the most indicative of these signs.
Consequently, some patients are treated for the wrong illnesses, or receive
substandard treatment for the ailments which plague them. Using the Selective
Transmission Algorithm to decongest the channel, improve packet reception rate,
5
reduce packet reception delay, and curb energy consumption (which extends sensor
lifespan), will give a new level of reliability to sensors. This improves their
effectiveness and adoption.
The fact that doctors can receive signals from many patients within a short time
interval can enable them prioritize the treatment of patients on the basis of the
criticality of their ailments rather than on a first-come-first-serve basis, a practice
which abounds in many hospitals nowadays. A decongested wireless transmission
channel reduces the probability of collisions and packet loss, increasing reliability
of the data transmission process, the treatment patients receive, and the efficiency
of medical doctors. Reliable sensors reduce the “white coat” effect which
momentarily instills fear in some patients on the sight of a medical doctor, resulting
in elevated measures of physiological signals such as blood pressure. Patients are
also saved from usually painful displacements, adding a level of convenience.
My research is also significant to other researchers. Though some results from the
experiments I conducted matched results of experiments carried out by other
researchers, some were very different, and others only partly matched results of
extant work. This highlights the uniqueness of each research, and acts as a base for
further research.
1.7 Thesis outline
This thesis is segregated into a number of sections, as detailed below:
Chapter 1: Introduction
This chapter begins with the rationale or motivation and scope of this research. It then
examines the objectives and research questions around which the research is centered,
wrapping up with the contribution and significance of the study, and introducing
subsequent sections.
Chapter 2: Literature review
I performed an analytical review of domain-related work in this chapter. It also acts as a
springboard for this research.
6
Chapter 3: Theoretical framework
In this chapter, I examined the hypothesis, underlying principles, and theories, which
helped formulate this research. I also analyzed metrics and the standard around which
the research revolves.
Chapter 4: Research methodology
The preference of methods used for this research is analyzed in this section. In this
chapter, I explain how the research was carried out, and the reason why I chose some
parameters and procedures with respect to others.
Chapter 5: Results and discussion
In this chapter, I report results obtained from a series of experiments conducted in
Castalia Simulator to investigate how well the proposed Selective Transmission
Algorithm performed in optimizing channel decongestion, packet reception, packet
failure, end-to-end delay, and energy consumption both with and without duty cycling. I
also attempted an explanation of trends obtained from the experiments.
Chapter 6: Conclusion and future work
A recap of the work done, concluding remarks and recommendations for the future are
presented in this section.
References:
This section contains bibliographic material used in this study.
Appendix:
Relevant material to enable better understanding of the study, which was not included in
the thesis body, is showcased in this section.
List of publications:
This section enlists the publications I realized during the course of this research.
7
CHAPTER 2: LITERATURE REVIEW
Considerable work has been done to optimize WBANs with respect to latency,
reliability, PER and energy consumption, be it in the form of full-fledge protocols, or
algorithms which enhance some portion or functionality of already existing protocols.
These enhancements have in their own way improved on, and added to the WBAN body
of knowledge. However, no one protocol or algorithm does it all. There is always room
for improvement, or conception of new functional constructs, especially in a field yet to
attain maturity, such as that of WBANs [4]. In this chapter, I analyze existing work
pertaining to WBAN protocol enhancement, which acts as a springboard for the design,
development, and implementation of the proposed Selective Transmission Algorithm.
In [5], the authors sought to curb energy consumption in multi-hop WSNs by
optimizing physical (PHY) layer parameters. They used transmission energy,
modulation scheme, transmission channel, and FEC as opposed to retransmissions to
reduce the energy consumed per successfully received bit (ESB) as a function of hop
distance, amidst channel noise and fading. Their scheme capitalized on energy gains as
a function of distance and a multi-hop scenario. However, a WBAN is localized to the
human body with distances between sensing nodes and coordinators usually below 2 m.
Besides, the IEEE 802.15.4 standard specifies a maximum of 2 hops for nodes in a
WBAN [3]. Consequently, such an approach would yield less appreciable results
relative to the computational resources used to implement it. The authors also proposed
that for M-ary modulation, the maximum transmit energy must increase as M increases.
However, for higher order modulation, the transmitter is on for a shorter duration owing
to the ability to transmit more data per unit time with this setting, and thus more energy
efficient [6].
Using TelosB motes running the TinyOS operating system, Despaux et al. [7] studied
the impact of varying BO and SO per duty cycle on the throughput and energy
consumption in WSNs. They found out that increasing the length of the active and
inactive portions of the superframe resulted in fewer packets received, more latency,
and more energy consumed for the same duty cycle, due to longer waiting periods
8
because of a lengthier inactive period. Higher packet rates improved performance
momentarily, which dwindled again as the buffers filled up. They then concluded that
for optimum performance, the buffer must exceed the product of the inactive period and
packet arrival rate. Though the authors of this paper advanced some useful proposals to
optimize performance, newer packets were dropped at the expense of older packets.
Considering the importance of recent packets to WBANs relative to older packets, the
algorithm would perform better if it rather caused the latter to be dropped off the buffers
in preference to the former.
Rout and Ghosh in [8] carved out a 60 m resource hungry bottleneck zone closer to the
sink. It contained relay nodes which routed packets from other nodes including theirs to
the sink, and network coder nodes which aggregated packets from other nodes prior to
transmission. Out of the bottleneck zone, leaf nodes sensed packets, while intermediate
nodes relayed them to the bottleneck zone. Using 1000 MICA nodes in an area of 200 m
x 200 m, experiments with the different node types at varying duty cycles prove that the
duty cycling and network coding algorithm improved packet delivery ratio, network
lifetime, and latency. The duty cycling and network coding solution is best suited for
WSN applications with many nodes spread across an extensive area, not for WBANs
with a few nodes around the body. In addition, though network coding reduced energy
consumption, hence prolonging network lifetime, it increased latency at lower traffic
levels due to coder nodes waiting to receive the minimum number of bits required per
packet from intermediate sensor nodes, prior to transmitting the packet. This increase in
latency is negative to WBANs, especially considering the requirement of time-critical
packet delivery in medical applications.
Li et al. proposed the Fairness-based Throughput Maximization Heuristic (FTMH)
algorithm to optimize throughput in WSNs through which they prove that inter-BAN
interference affected the functioning of intra-BANs, despite their respective use of
different channels [9]. The coordinator of each BAN used its received signal strength
(RSS) and the external interference signal strength (ISS) as input for the algorithm,
optimizing throughput and sorting, one BAN after the other until the throughput
between successive cycles was sufficiently small based on a given threshold. Nodes
with stronger QoS requirements were then placed in slots with stronger ISS and vice
9
versa, to achieve fairness. Though FTMH resulted in better throughput for higher inter-
BAN separation, it capitalized on fairness between co-located BANs, so it is of more
significance to WSN scenarios where all the nodes have equal priority. In a practical
WBAN scenario, different BANs have different priorities. Furthermore, even nodes in
the same BAN have different priorities based on the type of physiological signal they
sense. It is thus very practical for the transmission of a temperature signal to be delayed
in preference to a signal from an electrocardiograph [10]. In addition, the extra
complexity characteristic of the FTMH algorithm does not suit well for WBANs,
considering their constrained resources [11].
Zhou et al. coined the global energy minimization model (GEM) wherein a coordinator
attributed varying power and time resources to energy-constrained nodes based on their
throughput and delay, to curb energy waste in the WBAN [12]. The frame transmission
time of sensor nodes was based on the minimum latency requirement of all nodes in the
BAN, nodes going to sleep when not transmitting. Though some performance gains
were realized, a WBAN can comprise of nodes to sense physiological signals with
varying requirements. Consequently, determination of frame transmission time based on
the blanket minimum requirements of all nodes in the BAN could result in unnecessary
delays for less resource hungry nodes. In addition, though the TDMA access method
used prevented interference, it did not use bandwidth efficiently, since some superframe
slots are wasted if the nodes to which they are attributed have no data to transmit for
that superframe. It also used considerably more overhead in order to achieve reliability,
which is not positive to resource-constrained WBANs [13], [14].
Using the IEEE 802.15.6 standard, UWB communications and the slotted ALOHA (S-
ALOHA) channel access method, Karvonen et al. in [15] proposed a duty cycled MAC
and PHY layer energy optimization mechanism wherein coordinators of a WBAN
defined the transmission slots of their associated nodes using beacons based on network
requirements. The contention period and probability of nodes to transmit in a slot were
updated iteratively based on their channel access history and priority. The MAC and
PHY layer success probabilities per error coding rate per payload determined the
number of retransmissions and energy consumption. Experimental results portrayed
uncoded transmissions as being most energy efficient for smaller node-coordinator
10
separation distances, a trend which was reversed by coded transmissions at greater
node-coordinator separation distances due to the parity mechanism, which curbed
increasing received signal power. The use of received signal strength (RSS) as opposed
to the node-coordinator separation distance parameter used in this study would have
taken environmental conditions into consideration, giving more relevance to the results
obtained, and getting closer to what obtains in real life.
Capitalizing on the assertion that energy spent by nodes in a WBAN depended on the
coding method, modulation type, filter type, signal spreading method, acceptable BER,
and the sensor-coordinator distance, Kalra et al. [16] used MATLAB to experiment the
effect of varying standard Leech protocol parameters on WBAN performance iteratively
until depletion of the battery. The scenario with modified settings reported lengthier
node lifetime per round, better throughput, and more residual energy for the modified
parameter scenario relative to the standard parameter scenario. However, their
simulation environment was overly simplistic, lacking the complexity that comes with
humans wearing the nodes on their bodies. Consequently, their assertions are yet to be
tested in a realistic environment characterized by the dynamics expected in real world
settings.
In [17], Miskowicz presented a quantitative analysis of the send-on-delta signal-
dependent temporal sampling scheme wherein sampling is triggered when the signal’s
value varies by delta (∆), defined as a significant change based on the physiological
entity being sensed. The scheme capitalized on reducing the mean traffic of reports for a
well-chosen sampling resolution without compromising the quality of sensed data,
improving throughput and energy consumption relative to periodic sampling scenarios.
This was motivated by the assertion that every bit transmitted could reduce the lifetime
of WSNs, and the relatively higher energy spent by the communication module
compared to the processing module [18]. Through an analytical model, Miskowicz
prove that the send-on-delta mechanism improved effectiveness defined as a reduction
in the mean rate of messages compared to periodic sampling for a given sampling
resolution from between 1.57 to 5.85 times, and was independent of the signal’s
sampling resolution. Miskowicz only performed quantitative analysis to prove
performance gains brought about by the send-on-delta concept, and did not consider
11
simulating it in a real-world environment characterized by signal attenuation and
external interference from other devices.
In line with the send-on-delta mechanism above, Lunze and Lehmann [19] proposed an
event-based sampling method wherein they used an input signal with bounded
disturbance and compared the states of the event-based feedback control with that of
continuous feedback between two back-to-back events. By analyzing the upper bounds
of the event-based state feedback and the continuous state-feedback loop, they found
out that the behavior of the former could very closely approximate to that of the latter,
with an advantage of reducing communication between sensors, coordinators and
actuators. It also adapted the sensor-coordinator communication frequency to the
current system performance, only triggering communication when the resultant
disturbance exceeded the set threshold, hence conserving node resources. The gain
brought about by reduction of activity in the communication channel was at the expense
of increased node computation. Moreover, the authors did not have any limits to the
computational power of the event generator. Consequently, their results fell short of the
computational constraints experienced by real world sensors.
12
CHAPTER 3: THEORETICAL
FRAMEWORK
3.1 HYPOTHESIS
Though a lot of theories and knowledge has culminated in shaping up this research, it is
centrally based on one simple hypothesis:
If congestion causes collisions and data loss, delays and waste of energy, then
decongesting the wireless channel should increase packet reception rate, reduce
latency, and curb energy consumption.
3.2 OVERVIEW OF WIRELESS BODY AREA NETWORKS
Consisting of a number of small devices called nodes which are worn in, on, or around
the body, wireless body area networks help capture physiological signals such as blood
pressure, blood glucose level, electrical impulses of the heart, and electrical impulses of
the brain. The nodes then transmit these signals to medical personnel or other
stakeholders, who in turn reply with preventive or curative feedback using WIFI or
other transmission mechanisms, in a bit to promoting a healthier people [20]. In addition
to healthcare, sensors are also used in other fields including gaming, entertainment, and
ambient intelligence (detecting the presence of humans and taking a particular action,
e.g. turning lights on). Advances in microelectronics and wireless communications are
major contributing factors which have projected WBAN technology in recent times
[21], [22]. This technology demands high reliability and energy efficiency; low data
rate, device complexity, latency, energy consumption, and cost, characteristics which
make it more exigent than the wireless sensor network predecessor [23].
3.2.1 WIRELESS BODY AREA NETWORK ARCHITECTURE
WBANs consist of a 3-tiered architecture. The first tier referred to as the base tier
comprises sensor nodes, which capture physiological signals from various parts of the
body and transmit to a central sink [24] as portrayed by Figure 1 below. It is termed a
13
flat tier if it gathers information from a single human, or multi-tier if it does so from
many individuals. The second tier is a personal server such as a mobile phone, which
may double as a data aggregator. It is the bridge between the first and third tier,
transmitting sensed data to a base station via a wireless communication link such as Wi-
Fi. The third tier constitutes the medical server level, which is reached via different
types of cellular communication mechanisms – Wi-Fi and GPS for example. The server
is the main point of access for medical personnel and other stakeholders [25]. They can
then provide feedback to the patients in order to prevent or reverse a critical ill health
condition.
Figure 1: Wireless Body Area Network architecture [24].
3.2.2 NODE ARCHITECTURE
WBANs revolve around the node. It senses physiological signals, processes, and
transmits them. To this effect, it comprises a sensing unit, processing unit, transmission
unit, and power unit [26] as depicted in Figure 2 below. The sensor unit converts
14
physical entities such as blood pressure into analog electrical signals, which the analog
to digital converter (ADC) transforms into digital format understood by the processing
unit. Made up of microcontrollers, the processing unit then uses communication
protocols and signal processing algorithms to process the data for onward transmission.
Lastly, the transmitter dispatches the packets it receives from the processing unit to
medical personnel and other stakeholders. All these components function thanks to the
power unit which usually comprises of batteries, supplying energy to all the other
components of the node [27].
3.2.3 WBAN DESIGN REQUIREMENTS AND CHALLENGES
The effect of the wireless medium, battery lifetime, and collocated networks are the
most important factors for any BAN design. The human body characterized by tissue
absorbance, diffraction, and refraction of signals, influences the nature of the wireless
channel in WBANs. Interference ensues with collocated devices operating concurrently
on the same 2.4 GHz ISM transmission channel [28]. WBAN requirements include:
Topology and range: Typical WBANs usually do not exceed 2 m in diameter, using a
1-hop star topology. However, due to the conductance of the human body, coupled with
its curves, a relay node may be required to link a sensor node to the coordinator [29].
Reliability: The packet error rate in WBAN communications should be less than 10 %,
for 95 % of the best performing links when using a payload size of 256 bytes [30].
Factors which influence reliability in WBANs include packet transmission procedure
TRANSCEIVER
EXTERNAL MEMORY
MICROCONTROLLER
SENSOR 1
SENSOR 2
ADC
PO
WE
R S
OU
RC
E
Figure 2: Sensor node architecture.
15
and delay, packet loss probability, BER, channel access technique, retransmission
schemes, size of packet being transmitted, and MAC layer scheduling schemes [31].
Scalability: WBAN data rates range from a few kilobits per second to tens of megabits
per second, supporting a few nodes to tens of nodes [32]. Plug-and-play node addition
with minimum setup overhead should be supported as the need arises [31].
Quality of service (QoS): Scheduled access protocols such as TDMA and polling offer
deterministic packet loss rate and delay at the expense of additional overhead,
increasing susceptibility to collisions. Contention access on the other hand, transmits in
a survival-of-the-fittest manner, so no guarantee of delivery. WBAN transmissions
require reliability, prioritizing more time-sensitive and critical packets [33].
Security: Privacy, authentication, authorization, integrity, and confidentiality must be
observed in WBAN applications, especially those used in medical settings [34].
Radio channel and antenna: Antenna design in WBAN technology is very
challenging, especially since a trade-off needs to be made between size and efficiency.
Human body contours have an effect on antenna polarization and radiation, increasing
the complexity. Consequently, the radio channel characteristics and operational
environment considerations are invaluable in driving antenna design [35].
Power consumption: Ultra-low power design for WBAN nodes is important, especially
considering their lifespan requirement - at least 5 years for pacemakers as an example.
Devices are constrained to transmit at a power of 0.1 mW (-10 dBm), with a maximum
radiated power less than 1 mW (0 dBm), in order not to exceed the FCC SAR of 1.6
W/Kg in 1 g of body tissue regulation [30]. A power-conscious MAC is required as well
[21]. Duty cycling (shutting down the transceiver and CPU when not transmitting or
receiving) is a practical power saving technique, though at the expense of end-to-end
delay. Energy scavenging from body heat, body movement, or other sources is another
practical means of saving battery power and extending lifetime.
Coexistence: The fact that most WBAN implementations use the free unlicensed 2.4
GHz ISM frequency band is one reason they are plagued with interference. This
frequency band is overly congested, hosting Wi-Fi, Bluetooth, IEEE 802.15.4, and
ZigBee devices amongst others, necessitating a good interference reduction mechanism
for the operation of BANs [21]. The effectiveness of WBAN technology in medical
scenarios requiring high levels of reliability is dependent on such mechanisms.
16
Form factor: The biggest challenge when it comes to the size of a WBAN node
involves incorporating a battery and antenna, which will provide good lifetime and
radiation of signals respectively. The resulting node should be wearable [35].
Signal processing: Radio communication is the most power-hungry of sensor node
functions. Compressed sensing techniques are employed, allowing sensing of sparse
analog signals at sub-Nyquist rates, saving energy, and maintaining integrity of the
sensed signal [21].
Safety: WBAN operation can result in the heating up of tissue. SAR restrictions were
mated on frequencies ranging from 100 KHz to 10 GHz for healthy operation. In
Europe, the International Commission on Non-Ionizing Radiation Protection (ICNIRP)
specified SAR limits of 2.0 W/Kg in 10 g of tissue, in Australia and the US, Australian
Standards AS/NZS 2772.1 and American Standard ANSI C95.1 in tandem with FCC,
specified limits of 1.6 W/Kg in 1 g of tissue respectively [36]. Table 1 below portrays
acceptable parameters for major WBAN medical applications.
Table 1: Major WBAN application parameters [37].
Application Bit rate Delay BER
Deep brain stimulation < 320 kbps < 250 ms < 10-10
Drug delivery < 16 kbps < 250 ms < 10-10
Capsule endoscope 1 Mbps < 250 ms < 10-10
ECG 192 Kbps < 250 ms < 10-10
EEG 86.4 kbps < 250 ms < 10-10
EMG 1.536 Mbps < 250 ms < 10-10
Glucose level monitor < 1 kbps < 250 ms < 10-10
Audio streaming 1 Mbps < 20 ms < 10-5
Video streaming < 10 Mbps < 100 ms < 10-3
Voice 50 – 100 kbps < 100 ms < 10-3
Body temperature 120 bps < 125 ms < 10-3
Heart rate < 10 Kbps < 125 ms < 10-3
3.2.5 WBAN APPLICATIONS
Healthcare is amongst the most impactful of WBAN applications. Symptoms leading to
heart attacks and epileptic seizures for example can be obtained by continuously sensing
and reporting heart and brain activity respectively to medical personnel for remedial
action, before the situation becomes lethal. Some common health related WBAN
17
applications include electrocardiography (ECG), electroencephalography (EEG),
electromyography (EMG), pulse oximetry (SpO2), post-operative and temperature
monitoring, drugs delivery, toxins, and glucose level sensing [38]. The visual and
auditory systems can also be improved using artificial retinas and cochlear implants
respectively. The measure of physiological signals such as body weight and number of
calories burnt during sporting activities can greatly help stakeholders in planning for,
and avoiding injuries during subsequent sports sessions. WBAN sensors acting as
gyroscopes and accelerometers are positioned on different parts of the body, making the
user a game remote controller, and help in the animation of non-human characters in
movies. In the military, environmental sensors help individual soldiers to have a better
mastery of their environment, and assist commanders to effectively manage their
subordinates [39].
3.3 STANDARD USED:
The major standards controlling the functioning of WBANs are IEEE 802.15.6, IEEE
802.15.4, and Bluetooth Low Energy. The IEEE 802.15.4-2006 standard which was
upgraded from the initial version in 2003, specifies a MAC and a PHY layer for low
power, low data rate, and short-range wireless communications, with relatively low
overhead [38]. I adopted the IEEE 802.15.4 standard for this research because of its
sufficiently high bandwidth, support of many channels, and protection of adjacent
channel interference [40], [41]. Its features are analyzed below beginning with its
architecture, comprising of the Physical and MAC layers.
Figure 3: IEEE 802.15.4 architecture [42].
18
As portrayed in Figure 3 above, the IEEE 802.15.4 standard is based on the MAC and
PHY layers, while another add-on protocol called ZigBee specifies the upper layers –
Network and Security, Application Framework, and Application/Profiles layers. ZigBee
adds up a logical network layer, security, and application software on the IEEE 802.15.4
protocol to create a complete usable solution. It supports low power and low latency
devices in a star or peer-to-peer topology. The nodes use dynamic addressing and access
the channel through the contention-based CSMA/CA algorithm, or scheduled protocol -
TDMA. CSMA/CA is either unslotted or slotted, in which case it uses handshaking for
reliability. The standard supports duty cycling below 0.1 % [43], [44]. IEEE 802.15.4
and ZigBee drive the sensors for automation control in the industrial and commercial
sectors; perform diagnosis in the personal health sector; steer game consoles in the toys
and games sector; control lighting in the home automation sector; control keyboards and
other peripherals in the PC and peripherals sector; and are incorporated in
TV/VCR/CD/DVD remote controls in the consumer electronics sector [45]. An
overview of the two IEEE 802.15.4 layers follows.
3.3.1 PHYSICAL LAYER
The IEEE 802.15.4 PHY layer activates and deactivates the radio transceiver, performs
energy detection of the current channel, selects an appropriate channel for
communication, captures the link quality indicator (RSSI) of packets received, performs
channel access using the CSMA/CA protocol; transmits and receives data. It uses three
frequency bands for data transmission. The first is the universal 2.4 GHz band which
comprises 16 channels and has a data rate of 250 Kbps, adopted in this study. The others
are the 915 MHz ISM band which operates at a data rate of 40 Kbps, and the 868 MHz
band which uses a data rate of 20 Kbps on a single channel [41], [46], as depicted in
Figure 4 below. All three bands use DSSS.
19
Figure 4: IEEE 802.15.4 PHY layer frequency bands [41].
Communication in the PHY layer is effected via the PHY packet structure as shown in
Figure 5 below. It consists of a 32-bit preamble for synchronization, an 8-bit start of
frame delimiter, an 8-bit header, and a maximum of 127 bytes physical service data unit
(PSDU) which represents the MAC frame. The PHY frame has a maximum convenient
size of 133 bytes, appending a 6-byte header to the 127-byte maximum PSDU [47].
3.3.2 MAC LAYER
The MAC layer manages channel access for periodic data, such as physiological signals
captured by sensors. The Frame is the basic unit of information exchange as portrayed
by Figure 5 below, and made up of four main types – Data Frame for the transmission
of sensed data, Beacon Frame to delineate the boundaries of the superframe,
Acknowledgement Frame to confirm receipt of transmitted data, and a MAC Command
Frame to interrupt data transmission and deliver commands to perform various
functions. It consists of a header which ranges from 7 to 23 bytes, a variable size
payload depending on the packet size from the network layer, and a 2 byte FCS footer
for error correction [48].
Figure 5: IEEE 802.15.4 MAC frame and PHY packet structure [41].
20
Two main types of devices are defined in the IEEE 802.15.4 standard. The full function
device (FFD) serves as the coordinator, having any topology and able to talk to any
other device; the reduced function device (RFD) is limited to a star topology, sensing
and forwarding physiological signals to the coordinator. It might occasionally act as a
router, relaying data to the coordinator in a two-hop scenario. Each device in a BAN has
a 64-bit address which includes a PAN ID allocated by the BAN coordinator and shared
by all the devices on the BAN. Once associated with the coordinator, sensors’ addresses
could be reduced to a 16-bit short address assigned by the coordinator [48]. Devices are
arranged in a star topology, which is the main topology in the IEEE 802.15.4 standard.
Other topologies such as the mesh and tree topologies which could themselves combine
to form clustered star topologies are available and depicted in Figure 6 below.
The main unit of communication in the IEEE 802.15.4 contention access method is the
superframe shown in Figure 7 below. It contains an active period, and an inactive period
if duty cycling is implemented. Two main variants exist – a beacon-enabled and a non-
beacon-enabled mode. In the former, the superframe is delineated by beacons, which
announce the existence of a BAN, and synchronize associated devices. The active
portion of the beacon-enabled superframe consists of 16 equally sized slots, of which a
maximum of seven can be reserved as guaranteed time slots (GTS) for low latency
packet transmissions [49]. For a given frame, SO and BO are related by the expression 0
≤ SO ≤ BO ≤ 14. The ratio of SO and BO gives the duty cycle of the BAN, with the
active portion expressed as 2−(𝐵𝑂−𝑆𝑂) and the inactive portion as 1 − 2−(𝐵𝑂−𝑆𝑂).
(a) (a) (b) (a) (c)
Figure 6: (a) Star, (b) Mesh, and (c) Tree topologies in IEEE 802.15.4 standard [41].
21
Figure 7: IEEE 802.15.4 superframe [50].
3.3.3 CHANNEL ACCESS MECHANISM
The contention-based channel access mechanism is dwelt on in this study. All the nodes
vie for the transmission medium, backing off and transmitting their data later if the
medium is not idle. Asynchronous messages and data packets are transmitted using a
best effort service with no bandwidth and latency assurance, though well adapted to the
bursty traffic characteristic of WBANs. The superframe is used for data transmission,
with its CAP beginning after the nodes receive a beacon carrying the superframe
structure and other network management information from the coordinator. Nodes
transmit packets to the coordinator within the beacon period in a survival-of-the-fittest
manner. High priority packets are transmitted using a maximum of seven GTS slots,
which can exist per superframe. A node with data to transmit first listens to the
transmission channel in a process called carrier sensing. If the channel is idle, it
transmits its data. If the channel is however busy, the node waits until it is idle, then
starts vying for the channel in a contention process. If the channel becomes idle before
contention time elapses, the node with shortest contention time transmits its data, while
others wait until the channel is idle again. Otherwise, the packet is discarded because of
channel access failure [51], [52]. Figure 8 below gives a rundown of the steps involved
in slotted CSMA/CA.
22
Figure 8: IEEE 802.15.4 slotted CSMA/CA mechanism [50].
STEP 1 Initialization: The NB and CW variables are initialized to 0 and 2 respectively.
The BE variable is also initialized to 2 or aMinBE (with a value of 3), if the
Battery Life Extension MAC attribute is enabled or disabled respectively.
STEP 2 Backoff: The algorithm backs off, counting down from a number of random
back-off periods between 0 and 2𝐵𝐸 − 1, beginning at the back-off boundary.
STEP 3 Clear Channel Assessment: When count down expires, CSMA/CA performs
CCA to verify if the channel is idle, at the beginning of a back-off boundary.
23
STEP 4 Beginning Transmission: In case the channel is busy, CW is reinitialized to 2,
BE is incremented by 1 up to aMaxBE (8), and NB is incremented up to
macMaxCSMABackoffs (5). The algorithm goes back to STEP 2 and iterates
until NB gets to the value of macMaxCSMABackoffs, at which point
CSMA/CA reports a failure to the upper layer and aborts the packet.
STEP 5 Transmission and acknowledgement: If the channel is idle, CW is decremented
until it gets to 0 (carrying out 2 CCAs to prevent subsequent collision of ACK
frames), at which point the frame is transmitted and acknowledged, if the
remaining back-off periods of the current superframe can conveniently
accommodate the frame and accompanying ACK packet, one IFS before the
end of its CAP. Otherwise, CCA and frame transmission are deferred to the
active portion of the next superframe, increasing latency in the process [53].
3.3.4 THE HIDDEN TERMINAL (NODE)
Illustrated in Figure 9 below, the hidden node problem occurs when two or more
transmitting nodes which are out of the radio range of each other, transmit to the same
receiver which is in the radio range of both transmitters. Node A can see node B, and
node C can see node B. However, nodes A and C cannot see each other. Consequently,
simultaneous transmissions by node A and node C to node B will result in a collision
and warrant a retransmission. This is because nodes A and C are out of the transmission
range of each other, and cannot sense the presence of the other, so the CSMA/CA
algorithm will report a clear channel to both transmitters despite the fact that they are
both transmitting, until their signals arrive the receiver, node B. The hidden node
problem can result in up to 41 % of collisions [54].
Figure 9: Hidden node problem.
A B C
Collision
Node A’s
radio range Node C’s
radio range
24
3.3.5 CSMA PERSISTANCE
The CSMA/CA channel access mechanism comprises 1-persistent CSMA, p-persistent
CSMA, and non-persistent CSMA, by virtue of its channel back-off process. A node
with a packet to transmit senses the channel continuously, and transmits with a
probability of 1 immediately the channel is idle, or waits until it is idle if busy, for 1-
persistent CSMA. The microcontroller actually polls the radio of the node within
stipulated time intervals (0.128 ms for Tunable MAC) to find out if the channel is free.
There is however a probable that two nodes sense the channel to be idle, and transmit
their packets simultaneously, leading to this variant recording the highest number of
collisions. Though 1-persistent CSMA reduces idle listening, its effect is annulled by an
increase in propagation time. Next in line, p-persistent CSMA/CA works in a slotted
CSMA/CA scenario characterized by a slot length equal to, or greater than the
propagation time. It senses the channel continuously as 1-persistent CSMA does.
However, when the channel is idle, instead of the node transmitting with a probability of
1, it transmits with a probability p, where 0 < p < 1. It then delays for a probability 1 –
p, senses the next slot, and continues transmitting until it senses the channel as busy. It
waits until the channel is idle again, and then transmits if the probability of transmission
is less than p, or continues waiting if it is not. It reduces collisions relative to 1-
persistent CSMA [55]. Lastly, unlike the previous two variants which continuously
sensed the channel when busy, non-persistent CSMA senses the channel once. If busy, it
waits for a fixed interval, after which it senses the channel again. However, if idle, the
node transmits its packet, and repeats the cycle. Adopted for experiments in this
research, this variant prevents collisions, at the expense of reduced efficiency in dense
traffic scenarios, due to its long and rigid back-off durations. The efficiency of non-
persistent CSMA can be increased though, by putting nodes to sleep during the known
fixed back-off period [56].
25
3.4 METRICS RATIONALE
Amongst other metrics, packet reception rate also termed throughput (reliability), End-
to-end delay (latency), and power (energy consumption), stand out in the field of
wireless body area networks. A good WBAN is one, which consumes ultra-low energy
while transmitting data at maximum throughput and minimum latency (delay).
However, there exists a trade-off between these metrics, sometimes making it difficult
to optimize all three at once. Often, some are optimized at the expense of the other(s)
[57]. Reliability, delay, and energy consumption in the slotted beacon-enabled IEEE
802.15.4 standard is estimated with the help of a comprehensive analytical model
proposed by Park et al. [58], which models the PHY layer using combined radio and
channel models motivated by Zamalloa and Krishnamachari [59], which was in turn
inspired by the Markov chain proposed by Bianchi [60]. A personal area network (PAN)
with a coordinator and N contending one-hop nodes arranged in a star topology was
assumed. An unsaturated traffic reminiscent of wireless body area networks was
considered as well. Metrics estimation were guided by computations arising from
probabilities to attain a 3-variable Markov chain tuple (s(t), c(t), r(t)), where s(t)
represents the back-off stage at time t; c(t) represents the state of the back-off counter at
time t; and r(t) represents the retransmission counter at time t. In addition to the metrics
mentioned above which motivated coining of the proposed Selective Transmission
Algorithm, other metrics such as Bit Error Rate, Fade Depth Distribution, and Signal-to-
Noise Ratio were used to investigate performance of the algorithm with respect to
standards laid down by the domain, making use of constants and attributes in Table 2
below. An overview of the metrics follows. Figure 10 below shows stages in a packet
transmission process.
Figure 10: Packet transmission sequence of IEEE 802.15.4 [61].
26
Table 2: IEEE 802.15.4 constants and attributes courtesy of the IEEE 802.15.6-
2006 standard [41].
Constant/Attribute Description Value
aMaxPHYPacketSize The maximum PSDU size
(in octets) the PHY shall
be able to receive
127
aTurnaroundTime RX-to-TX or TX-to-RX
maximum turnaround
time (in symbol periods)
12
phySymbolsPerOctet Number of symbols per
octet for the current PHY
Range: 0.4, 1.6, 2, 8
Default: 2
dataRate Data rate of the IEEE
802.15.4 (2450 MHz)
PHY
250 kb/s
aBaseSlotDuration The number of symbols
forming a superframe slot
when the superframe
order is equal to 0
60
aMaxMPDUUnsecuredOverhead
The maximum number of
octets added by the MAC
sublayer to the PSDU
without security
25
aMaxSIFSFrameSize Maximum size of an
MPDU, in octets, that can
be followed by a SIFS
period
18
aNumSuperframeSlots Number of slots in any
superframe
16
aUnitBackoffPeriod The number of symbols
forming the basic time
period used by the
CSMA-CA algorithm
20
macMaxBE Maximum value of the
back-off exponent, BE, in
the CSMA-CA algorithm
Range: 3-8
Default: 5
macMaxCSMABackoffs The maximum number of
Back-offs the CSMA-CA
algorithm will attempt
before declaring a channel
access failure
Range: 0-5
Default: 4
macMaxFrameRetries The maximum number of
retries allowed after a
transmission failure
Range: 0-7
Default: 3
macMinBE Minimum value of the
back-off exponent (BE) in
the CSMA-CA algorithm
Range: 0-macMaxBE
Default: 3
27
Figure 11: IEEE 802.15.4 Markov chain model for the CSMA/CA algorithm [58].
The Park et al. model culminated in three linear equations generated from finding steady
state probabilities. The first was the probability 𝜏 that a node attempts a first carrier
sense to transmit a frame. The second was the probability 𝛼 that a node finds the
channel busy during CCA1, and the third was the probability 𝛽 that a node finds the
channel busy during CCA2. In order to improve this model, Kone et al. [62], Zayani et
al. [63], and Bijalwan et al. [64] appended an M/M/1/K queuing model to it that added a
28
finite buffer to each node. They also attempted to solve the non-linear system of
probabilities, estimated the probability of going back to the idle state 𝑝0 by considering
the payload per node 𝜆, and produced output such as throughput per payload. Figure 20
below shows the steps involved therein.
Figure 12: IEEE 802.15.4 PHY and MAC model flowchart, courtesy of [64].
PHY inputs MAC inputs
𝑝0 = 1, 𝜆
Computing 𝑃𝑒 Solving Non-
Linear System:
Computing 𝜏, 𝛼
and 𝛽
Computing 𝑃𝑐
using 𝜏 and 𝑝0
Computing 𝑃𝑓𝑎𝑖𝑙
using 𝑃𝑐 and 𝑃𝑒
Computing 𝐸𝑇
using 𝑃𝑓𝑎𝑖𝑙, 𝛼
and 𝛽
Computing new
𝑝0 using 𝐸𝑇 and
𝜆
Computing
Outputs
New 𝑝0 − old 𝑝0 > 𝜀
(i.e. no convergence)
29
3.4.1 PACKET RECEPTION RATE AND PACKET FAILURE
As mentioned in the steps comprising the CSMA/CA algorithm above, the transmission
of a packet is preceded by the node gaining access to the transmission channel. To
estimate reliability or the probability that a packet is successfully received, the
stationary probability 𝜏 that a node attempts a first carrier sense (CCA1) in a time slot
chosen at random was derived, as in Equation (1) below.
𝜏 = ∑ ∑ 𝑏𝑖,0,𝑗 = (
1 − 𝑥𝑚+1
1 − 𝑥) (
1 − 𝑦𝑛+1
1 − 𝑦)
𝑛
𝑗=0
𝑚
𝑖=0
𝑏0,0,0 , (1)
where 𝑚 is the maximum number of back-offs the CSMA/CA algorithm will attempt
prior to declaring a channel access failure; 𝑛 is the maximum number of allowable
retries after a transmission failure; 𝑖 is the back-off number; 𝑗 is the retry number; 𝑥 is
the busy channel probability denoted by ∝ +(1 − 𝛼)𝛽; 𝑦 is the probability of
unsuccessful packet transmission after channel access denoted by 𝑃𝑓𝑎𝑖𝑙(1 − 𝑥𝑚+1); and
𝑏0,0,0 is the State where the variables of back-off stage counter, back-off counter, and
retransmission counter are all equal to 0. However, the channel might be busy during
both the first (CCA1) and second (CCA2) channel assessment attempts represented by
the probability 𝛼 and 𝛽 respectively. In addition, the transmitted packet might be met
with a collision, represented by the probability 𝑃𝑐. Consequently, 𝜏 is influenced by 𝛼,
𝛽, and 𝑃𝑐 as detailed below. The probability 𝑃𝑐 that at least one of the other N-1 sensor
nodes in the WBAN transmits with probability 𝜏 in the same time slot as the current
sensor node thus causing a collision was expressed by Equation (2) below.
𝑃𝑐 = 1 − (1 − (1 − 𝑝0)𝜏)𝑁−1 , (2)
where 𝑝0 is the probability that the node is idle; and 𝑁 is the total number of sensing
nodes in the BAN. When the channel is busy, it is assumed that it is either transmitting a
data packet with probability 𝛼1 or an ACK packet with probability 𝛼2, which is
reflected in the busy channel probability in Equation (3) below.
𝛼 = 𝛼1 + 𝛼2 (3)
The probability of having a busy channel during CCA1 as a result of a data packet
transmission was:
𝛼1 = 𝐿(1 − (1 − (1 − 𝑝0)𝜏)𝑁−1)(1 − 𝛼)(1 − 𝛽) , (4)
30
where 𝐿 is the length of the data frame in slots (1 slot = 80 bits). The probability of
having a busy channel during CCA1 as a result of an ACK packet transmission was
given as:
𝛼2 = 𝐿𝑎𝑐𝑘
𝑁(1 − 𝑝0)𝜏(1 − (1 − 𝑝0)𝜏)𝑁−1
1 − (1 − (1 − 𝑝0)𝜏)𝑁∗
(1 − (1 − (1 − 𝑝0)𝜏)𝑁−1)(1 − 𝛼)(1 − 𝛽)
(5)
where 𝐿𝑎𝑐𝑘 is the length of the acknowledgement packet in slots. The probability that
the channel is busy during the second channel assessment attempt CCA2 is denoted by
Equation (6) below.
𝛽 =
1−(1 − (1 − 𝑝0)𝜏)𝑁−1 + 𝑁(1 − 𝑝0)𝜏(1 − (1 − 𝑝0)𝜏)𝑁−1
2 − (1 − (1 − 𝑝0)𝜏)𝑁 + 𝑁(1 − 𝑝0)𝜏(1 − (1 − 𝑝0)𝜏)𝑁−1 (6)
The carrier sensing probability 𝜏 and the busy channel probabilities 𝛼 and 𝛽 above
respectively constitute a mechanism of non-linear equations solvable via a numerical
method.
The main reasons why packets are discarded in slotted CSMA/CA is either because of
the transmitter failing to gain access to the transmission medium, or the number of
allowable retransmission retries has been exceeded as a result of prior packet
transmission failure. Channel access failure ensues after a transmitter is unsuccessful in
obtaining an idle channel within two back-to-back CCAs in a period equivalent to m+1
back-offs. In addition, a packet is lost if consecutive attempts to transmit it end up in a
collision more than n+1 times of trying. With this in mind, and referring to the Markov
chain model for the CSMA/CA algorithm for IEEE 802.15.4 in Figure 11 above, the
probability that the packet was lost due to a channel access failure is given as:
𝑃𝑐𝑓 = ∑ 𝑥𝑏𝑚,0,𝑗
𝑛
𝑗=0
=𝑥𝑚+1(1 − 𝑦𝑛+1)
1 − 𝑦 (7)
The probability of a packet loss resulting from the number of available retries being
exceeded after prior transmission failure attempts was expressed as:
𝑃𝑐𝑟 = ∑ 𝑃𝑐(1 − 𝛽)𝑏𝑖,−1,𝑛
𝑚
𝑖=0
= 𝑦𝑛+1 (8)
Taking reliability to be the result of a packet not having failed, and combining Equation
(7) and Equation (8) above, reliability was computed using Equation (9) below.
𝑅 = 1 − 𝑃𝑐𝑓 − 𝑃𝑐𝑟 (9)
31
From the above probabilities, the probability of failed packets 𝑃𝑓𝑎𝑖𝑙 was:
𝑃𝑓𝑎𝑖𝑙 = 1 − (1 − 𝑃𝑐)(1 − 𝑃𝑒) , (10)
where 𝑃𝑒 is the probability of loss due to channel and radio constraints computed by the
PHY model. In a related study by Kone et al. [62], the authors observed that Park et al.
used 𝜏 in what was the original version of Equation (4), Equation (5) and Equation (6)
above to indicate that a node was transmitting. However, 𝜏 signified that a node was
idle, but a transmitting node cannot be idle. Consequently they replaced 𝜏 in Equation
(2), Equation (4), Equation (5), and Equation (6) above by (1 − 𝑝0)𝜏, where 𝜏 is the
probability that a node tries to transmit, and 1 − 𝑝0 is the probability that a node has a
frame to send. The authors also appended an M/M/1/K queue to each node, and every
queue received frames by a Poisson arrival process, 𝜆 frames per second. Courtesy of
Bijalwan et al. [64], the queue utilization 𝜌 was thus given as:
𝜌 = 𝜆 ×
1
𝐸𝑇 , (11)
where 𝜆 is the packet arrival rate, and 𝐸𝑇 is the expected time (time to process a frame).
Next, the steady state probability that there were 𝑖 frames in the queue was given as:
𝑝𝑖 = 𝜌𝑖 ∑ 𝜌𝑗 ,
𝐾
𝑗=0
⁄ (12)
where 𝐾 is the back-off counter number. Hence, the probability that the node is idle was
expressed as:
𝑝0 = [∑ 𝜌𝑗
𝐾
𝑗=0
]
−1
(13)
The processes iterated till 𝑝0 converged to a stable value, at which point the outputs
from queuing analysis could be computed per node payload size 𝜆. The probability of a
good frame reception (reliability) was expressed as in Equation (14) below.
𝑅 = (1 − 𝑝𝑘)(1 − 𝑃𝑐𝑓)(1 − 𝑃𝑐𝑟) , (14)
where 𝑝𝑘 is the probability of having a full buffer. The average throughput per node
𝑆𝑎𝑣𝑔 was given as:
𝑆𝑎𝑣𝑔 = 𝜆𝑅𝐿𝑝 , (15)
where 𝐿𝑝 is the Application data size. The probability of packet failure due to link error
or collision was thus given as in Equation (10) above.
32
Observable from Figure 13 below, simulations conducted by Zayani et al. in Matlab
portrayed a declining reliability as payload increased in the Wireless Personal Area
Network (WPAN) [63].
Figure 13: Reliability per payload for a WPAN, courtesy of [63].
Similar experiments by the same authors indicated an increase in packet failure
probability as payload size increased, depicted by Figure 14 below, and confirming
Equation (10) above. Interestingly, the graph of throughput obtained by Kone et al.
above had a trend which looked antagonistic to that of the probability of failed packets
obtained by Zayani et al. below.
Figure 14: Failure probability per payload for a WPAN, courtesy of [63].
33
3.4.2 END-TO-END DELAY PRESUMPTION
With reference to Park et al., the average delay for successful reception of a packet is
considered to be the time lapse from when the packet is at the head of the MAC queue
ready for transmission, until an ACK is received, signaling its successful transmission
[65]. To help in delay estimation, analysis is subdivided into two main parts. The first
involves deriving an expression for successful packet transmission at a time equivalent
to j+1, after unsuccessful transmission attempts at a time j because of collisions. The
second part entails deriving an expected value for the approximate back-off delay
resulting from a busy channel. For the first part, let Dj be the time taken for successfully
transmitting a packet at the jth back-off stage, expressed as:
𝐷𝑗 = 𝐿𝑠 + 𝑗𝐿𝑐 + ∑ 𝑇ℎ
𝑗
ℎ=0
, (16)
where 𝐿𝑠 is the time taken to successfully transmit a packet; 𝐿𝑐 is the time taken for a
collided packet transmission; 𝑇ℎ is the back-off stage delay. By Knowing the length of
the data packet including the header, the ACK frame’s duration, ACK timeout, and IFS,
the successful packet transmission time 𝐿𝑠 and collided packet transmission time 𝐿𝑐 can
be computed using Equation (17) and Equation (18) below respectively.
𝐿𝑠 = 𝐿 + 𝑡𝑎𝑐𝑘 + 𝐿𝑎𝑐𝑘 + 𝐼𝐹𝑆 , (17)
𝐿𝑐 = 𝐿 + 𝑡𝑚,𝑎𝑐𝑘 + ∑ 𝑇ℎ
𝑗
ℎ=0
, (18)
Where 𝐿 is the length of the packet including overhead; 𝑡𝑎𝑐𝑘 is the ACK waiting time;
𝐿𝑎𝑐𝑘 is the length of the ACK frame; 𝐼𝐹𝑆 is the inter-frame spacing; and 𝑡𝑚,𝑎𝑐𝑘 is the
ACK timeout. The probability of successfully transmitting a packet after j+1
unsuccessful attempts because of collisions is then given as:
Pr(𝐴𝑗|𝐴𝑡) =
𝑃𝑐𝑗(1 − 𝑥𝑚+1)𝑗
∑ (𝑃𝑐(1 − 𝑥𝑚+1))𝑘𝑛
𝑘=0
,
=(1 − 𝑃𝑐(1 − 𝑥𝑚+1))𝑃𝑐
𝑗(1 − 𝑥𝑚+1)𝑗
1 − (𝑃𝑐(1 − 𝑥𝑚+1))𝑛+1 ,
(19)
where Aj is the event of a successful packet transmission at a time j+1 after j events of
unsuccessful transmissions, normalized by taking into consideration all possible events
of successful attempts 𝐴𝑡 within n total allotted attempts; 𝑃𝑐 is the probability of
collisions per sending attempt; and (1 − 𝑥𝑚+1) is the probability of successful channel
34
access within m maximum number of back-off stages. Equation (20) below expresses
the delay 𝐷 incurred for a successful packet transmission after j unsuccessful attempts.
𝐷 = ∑ 𝟙(𝐴𝑗|𝐴𝑡)𝐷𝑗
𝑛
𝑗=0
(20)
Consequently, the expected value of 𝐷 is:
𝔼[𝐷] = ∑ Pr(𝐴𝑗|𝐴𝑡)𝔼[𝐷𝑗]
𝑛
𝑗=0
, (21)
where 𝔼[𝐷𝑗] = 𝑇𝑠 + 𝑗𝑇𝑐 + ∑ 𝔼[𝑇ℎ]𝑗ℎ=0 ; is the time for successful transmissions; and 𝑇𝑐
is the time for collisions.
Similarly, considering the second part which entails derivation of an expected value for
the approximate back-off delay 𝑇ℎ as a result of a busy channel, let 𝑇ℎ,𝑖 be the random
time required to complete CCA1 and CCA2 from the selected back-off counter value at
back-off level 𝑖. It is worthy of remembrance that as per the CSMA/CA algorithm
analyzed above, a node gains access to the channel for transmission when the back-off
timer reaches 0, and after completion of CCA1 and CCA2 wherein the transmission
may be successful with a probability 1 − 𝑃𝑐 or lead to a collision with probability 𝑃𝑐 as
expressed in Equation (2) above. Let 𝐵𝑖 represent the event that a node tries accessing
the channel when it is busy for 𝑖 times, and then idle at time 𝑖 + 1. Furthermore, let 𝐵𝑡
represent the event of successfully sensing the channel within the total number of 𝑚
allotted sensing attempts. If the node successfully accesses the channel after 𝑖 busy
CCA attempts, the total back-off delay is given as: ̴
𝑇ℎ = ∑ 𝟙(𝐵𝑖|𝐵𝑡)𝑇ℎ,𝑖
𝑚
𝑖=0
(22)
The completion time of CCA1 and CCA2 at back-off level 𝑖 is:
𝑇ℎ,𝑖 = 2𝑇𝑠𝑐 + ∑ 𝑇ℎ,𝑘𝑠𝑐
𝑖
𝑘=1
+ ∑ 𝑇ℎ,𝑘 ,𝑏
𝑖
𝑘=0
(23)
where 2𝑇𝑠𝑐 is the successful sensing time; ∑ 𝑇ℎ,𝑘𝑠𝑐𝑖
𝑘=1 is the unsuccessful sensing time as
a result of a busy channel during CCA; and ∑ 𝑇ℎ,𝑘𝑏𝑖
𝑘=0 is the back-off time. The
probability of successfully accessing the channel is thus given as:
35
Pr(𝐵𝑖|𝐵𝑡) =
∑ 𝐶∝𝛽𝑘 (𝑖)2𝑖
𝑘=1
∑ 𝐶𝛼𝛽(𝑘)𝑚𝑘=0
, (24)
where 𝐶∝𝛽(𝑖) gives all possibilities of choosing elements from a set of busy channel
probabilities {∝, (1−∝)𝛽}, and 𝐶∝𝛽𝑘 (𝑖) is one of the elements in the set 𝐶∝𝛽(𝑖).
Consequently, the total number of combinations for 𝑖 elements is 2𝑖, and 𝐶∝𝛽𝑘 (𝑖) returns
one combination out of 2𝑖. The expected back-off delay is thus:
𝔼[𝑇ℎ] = ∑ Pr(𝐴𝑗|𝐴𝑡) 𝔼[𝑇ℎ,𝑖]
𝑚
𝑖=0
(25)
The unsuccessful sensing time ∑ 𝑇ℎ,𝑘𝑠𝑐𝑖
𝑘=1 in Equation (23) above is related to the picking
of 𝑖 elements from the 𝐶∝𝛽(𝑖) set, returning the unsuccessful sensing delay 𝑇𝑠𝑐 + 𝑇𝑠𝑐 for
the combination (𝛼, 𝛼), and the unsuccessful sensing delay 𝑇𝑠𝑐 + 2𝑇𝑠𝑐 for the
combination (𝛼, (1 − 𝛼)𝛽). In addition, the back-off time 𝑇ℎ,𝑘𝑏 of 𝑘 unsuccessful
sensing tries is uniformly distributed in [0, 𝑊𝑘 − 1]. Consequently, the expected back-
off delay could take the form:
𝔼[𝑇ℎ] = 2𝑇𝑠𝑐 + ∑ Pr(𝐵𝑖|𝐵𝑡) ∑𝑊𝑘 − 1
2
𝑖
𝑘=0
𝑆𝑏
𝑚
𝑘=0
+𝑇𝑠𝑐
∑ 𝐶𝛼𝛽(𝑘)𝑚𝑘=0
∑ ∑ 𝐶𝛼𝛽𝑘 (𝑖) (𝑁𝛼
𝑘(𝑖) + 2𝑁�͂�𝑘(𝑖)) ,
2𝑖
𝑘=1
𝑚
𝑖=0
(26)
Where 𝑆𝑏 is the aUnitBackoffPeriod; and, 𝑁𝛼𝑘(𝑖) and 𝑁�͂�
𝑘(𝑖) return the number of 𝛼 and
(1 − 𝛼)𝛽 of the 𝐶𝛼𝛽𝑘 (𝑖) combination, respectively.
In a related study carried out by Kone et al. [62], they summarized the relationship
between delay 𝐷 and other MAC parameters in Equation (27) below. This was similar
to yet another study carried out by Zayani et al. [63] represented by Figure 15 below.
𝐷 =
𝐿
𝜆(1 − 𝑃𝑘) (27)
36
Figure 15: Delay per payload for a WPAN, courtesy of [63].
3.4.3 ENERGY CONSUMPTION PRESUMPTION
Power is amongst the most important of WBAN performance parameters, owing to the
requirement of nodes to function real-time, all the time. The small size of, and the
impracticality of frequently recharging nodes, warrants them to be powered up by small
batteries, which can only provide power to nodes for durations as short as their sizes can
permit [66]. Park et al. used the Markov chain in Figure 11 above in tandem with IEEE
802.15.4 MAC and PHY parameters to characterize energy consumption in WPANs. In
doing this, they assumed that the radio was in the idle-listen state while in the back-off
stage, and the ACK timeout 𝑡𝑚,𝑎𝑐𝑘 = 𝐿𝑎𝑐𝑘 + 1 in 𝑆𝑏 (aUnitBackoffPeriod) time units
(20 symbols). Energy consumption for the various stages of a packet transmission
process were considered, as expressed in Equation (28) below.
𝐸𝑡𝑜𝑡 = 𝑃𝑖 ∑ ∑ ∑ 𝑏𝑖,𝑘,𝑗
𝑛
𝑗=0
𝑊𝑖−1
𝑘=1
𝑚
𝑖=0
+ 𝑃𝑠𝑐 ∑ ∑(𝑏𝑖,0,𝑗 + 𝑏𝑖,−1,𝑗) + 𝑃𝑡 ∑ ∑(𝑏−1,𝑘,𝑗 + 𝑏−2,𝑘,𝑗)
𝐿−1
𝑘=0
𝑛
𝑗=0
𝑛
𝑗=0
𝑚
𝑖=0
+ 𝑃𝑟 ∑(𝑏−1,𝐿,𝑗 + 𝑏−2,𝐿,𝑗)
𝑛
𝑗=0
+ ∑ ∑ (𝑃𝑟𝑏−1,𝑘,𝑗 + 𝑃𝑖𝑏−2,𝑘,𝑗) + 𝑃𝑠𝑝 ∑ 𝑄𝑙 ,
𝐿0−1
𝑙=0
𝐿+𝐿𝑎𝑐𝑘+1
𝑘=𝐿+1
𝑛
𝑗=0
(28)
37
where 𝑃𝑖, 𝑃𝑠𝑐, 𝑃𝑡, 𝑃𝑟 and 𝑃𝑠𝑝 constitute average energy consumption in the idle-listening,
channel sensing, transmitting, receiving, and sleep states respectively. Substituting
Equations (12) - (15) from [58] in Equation (28) above will enable obtaining energy
consumption in a closed form. Furthermore, an approximation of the total energy
consumed can be made. By assuming that the carrier sensing probability 𝜏 in Equation
(12) from [58] is measured and not computed analytically, it is rewritten as:
𝑃𝑖 ∑ ∑ ∑ 𝑏𝑖,𝑘,𝑗
𝑛
𝑗=0
𝑊𝑖−1
𝑘=1
𝑚
𝑖=0
=𝑃𝑖𝜏
2 (
(1 − 𝑥)(1 − (2𝑥)𝑚+1)
(1 − 2𝑥)(1 − 𝑥𝑚+1)𝑊0 − 1) (29)
By combining Equations (12), (13), and (16) in [58], the average energy consumed by
the sensing state is given as:
𝑃𝑠𝑐 ∑ ∑(𝑏𝑖,0,𝑗 + 𝑏𝑖,−1,𝑗)
𝑛
𝑗=0
𝑚
𝑖=0
= 𝑃𝑠𝑐(2 − 𝛼)𝜏 (30)
Likewise, by substituting Equations (14) and (16) in [58], the average energy consumed
for packet transmission and collision combined is:
𝑃𝑡 ∑ ∑(𝑏−1,𝑘,𝑗 + 𝑏−2,𝑘,𝑗) +
𝐿−1
𝑘=0
𝑛
𝑗=0
𝑃𝑖 ∑(𝑏−1,𝐿,𝑗 + 𝑏−2,𝐿,𝑗)
𝑛
𝑗=0
+ ∑ ∑ (𝑃𝑟𝑏−1,𝑘,𝑗 + 𝑃𝑖𝑏−2,𝑘,𝑗)
𝐿+𝐿𝑎𝑐𝑘+1
𝑘+𝐿+1
𝑛
𝑗=0
= (1 − 𝛼)(1 − 𝛽)𝜏(𝑃𝑡𝐿 + 𝑃𝑖 + 𝐿𝑎𝑐𝑘(𝑃𝑟(1 − 𝑃𝑐) + 𝑃𝑖𝑃𝑐))
(31)
Assuming a negligible energy consumption during the sleep state and summing up
Equations (29) - (31) above, the approximated energy consumption is expressed in
Equation (47) below.
�̃�𝑡𝑜𝑡 =
𝑃𝑖𝜏
2(
(1 − 𝑥)(1 − (2𝑥)𝑚+1)
(1 − 2𝑥)(1 − 𝑥𝑚+1)𝑊0 − 1) + 𝑃𝑠𝑐(2−∝)𝜏
+ (1−∝)(1 − 𝛽)𝜏(𝑃𝑡𝐿 + 𝑃𝑖 + 𝐿𝑎𝑐𝑘(𝑃𝑟(1 − 𝑃𝑐) + 𝑃𝑖𝑃𝑐))
(32)
Figure 16 below shows Monte Carlo simulations by Park et al. to validate their IEEE
802.15.4 MAC layer parameter adaptive tuning algorithm under stationary and transient
conditions to abstract its real world behavior, both in the I-mode (Idle-listening) and S-
mode (sleep). Observations show a reduction in energy consumed as the load increased.
38
Figure 16: Energy consumption per payload in a WPAN, courtesy of [58].
3.4.4 SIGNAL-TO-NOISE RATIO, BIT ERROR RATE, AND PACKET
ERROR RATE
Though pundits consider link quality indicator (LQI) as a more accurate measure of the
quality of received signals relative to raw SNR, the latter represents a simple and
resource-efficient method of link quality estimation, and hence quality of the received
signal without additional overhead characteristic of other methods. This makes it
favorable for resource-constrained WBANs, preventing overhearing in the process [67].
Besides, LQI is considered as SNR in Castalia Simulator, used for experiments in this
study [68]. In order to obtain SNR from the received signal strength indicator (RSSI),
Qin et al. expressed the received signal strength 𝑃𝑟𝑠𝑠 as:
𝑃𝑟𝑠𝑠 = 𝑃𝑡𝑥 − 𝑃𝐿(𝑑) + 𝑋𝜎 = 𝑃𝑡𝑥 − 𝑃𝐿(𝑑0) − 10𝑛𝑙𝑜𝑔10 (
𝑑
𝑑0) + 𝑋𝜎 (33)
where 𝑃𝐿(𝑑) is the path loss at distance 𝑑; 𝑑0 is the reference distance; 𝑛 is the path
loss exponent; and 𝑋𝜎 is a zero-mean Gaussian random process with standard deviation
𝜎 representing variation of channel fading with time. Consequently, SNR (in dB) can be
computed by Equation (34) below.
𝑆𝑁𝑅 = 𝑃𝑟𝑠𝑠 − 𝑁𝑖 − 𝑁𝑒 (34)
where 𝑁𝑖 is the internal noise (from within the node) and 𝑁𝑒 is the environmental noise
[67].
39
Alternatively, the authors in [69] correlated LQI with chip error rate, which is the
method adopted by the CC2420 radio adopted for this study. The 2450 MHz PHY layer
was adopted with a maximum data rate of 250 Kbps, using O-QPSK modulation based
on DSSS technology on 16 channels each with a 5 MHz bandwidth. Transmission took
place in 4-bit symbols at a time, which were in turn translated into 1 of 16 quasi-
orthogonal 32-chip pseudo-random noise (PN) sequences. A chip stream resulting from
the concatenation of successive PN sequences was then modulated on the carrier using
O-QPSK and transmitted. At the receiver, the demodulated PN sequence was compared
with 16 valid PN sequences, and the one with which it had the least hamming distance
(number of chip positions for which the 2 PN sequences differ) was adopted as the
received sequence, and translated back into a symbol. The checksum in the packet
header helped with erroneous symbol identification. In order to estimate packet failure,
the probability that the receiver does not identify the transmitted symbol correctly was
considered. This meant that the hamming distance between the transmitted and received
sequence was equal to or greater than that between the received signal and another valid
sequence (each valid sequence differs from others in at least 12, and at most 20 chip
positions) for an O-QPSK modulated signal under additive white Gaussian noise
(AWGN), and was expressed as:
𝐵𝐸𝑅 =
1
2𝑒𝑟𝑓𝑐(√𝛾) (35)
where 𝑒𝑟𝑓𝑐 is the complementary error function; and is the ratio of the signal energy to
the noise energy. The symbol error rate 𝑆𝐸𝑅 was expressed further as:
𝑆𝐸𝑅 = ∑ (32
𝑛) 𝐵𝐸𝑅𝑛(1 − 𝐵𝐸𝑅)32−𝑛 × 𝑃𝑠𝑦𝑚𝑒𝑟𝑟(𝑛)
32
𝑛=1
(36)
where 𝑃𝑠𝑦𝑚𝑒𝑟𝑟(𝑛) is the probability of a symbol error when 𝑛 chips are received
erroneously. It follows that for a packet of 𝑚 bytes long (or 2𝑚 symbols long), the
probability of receiving a packet in error, or the packet error rate 𝑃𝐸𝑅 is given by
Equation (37) below.
𝑃𝐸𝑅 = 1 − (1 − 𝑆𝐸𝑅)2𝑚 (37)
Figure 17 below portrays the relationship between the BER, SER, and PER with the
SNR, implying that bigger packets containing many more bits were susceptible to more
bit errors per packet.
40
Figure 17: Relationship between BER, SER, and PER with deteriorating SNR,
courtesy of [69].
3.4.5 PATH LOSS (FADE DEPTH) The human body is made up of different types of tissues with varied shapes, dielectric
constants, permittivity, conductivity, and impedance. Consequently, the transmitted
signal which is electromagnetic in nature experiences fading originating from energy
absorption, reflection, diffraction, and shadowing from body tissues. Multipath fading
which is caused by characteristics of the environment surrounding the body, also has an
impact on the signal quality, and thus adds a level of complexity to the channel model
for WBANs not experienced by other applications. The fading power profile is
generated in terms of received power as a function of transmit power [70]. It is
manifested in terms of path loss, being the path loss variation around the mean path
loss. Path loss has a bearing on the distance between the transmitter and receiver, as
expressed by the Friis formula [71] in equation (38) below.
𝑃𝐿(𝑑) = 𝑃𝐿0 + 10𝜂𝑙𝑜𝑔 (
𝑑
𝑑0)
(38)
where 𝑃𝐿(𝑑) is the path loss at a distance (d); 𝑃𝐿0 is the known path loss at a reference
distance 𝑑0; 𝜂 is the path loss exponent; 𝑑 is the distance between transmitter and
receiver; and 𝑑0 is the reference distance. Considering the immobile setting used in this
study, and proximity of the equipment to the human body, fading would be more as a
result of shadowing arising from signal blocking by different body parts, causing
variation in path loss from the mean path loss at a given distance. Shadowing is
41
represented as a zero-mean Gaussian variable with standard deviation 𝜎. Addition of the
shadowing component modifies equation (38) above as follows:
𝑃𝐿(𝑑) = 𝑃𝐿0 + 10𝜂𝑙𝑜𝑔 (
𝑑
𝑑0) + 𝑆𝜎
(39)
where 𝑆𝜎 is the shadowing component. Related studies by Sharif et al. [72] portrayed an
increase in path loss as the payload increased, with packets having higher payload sizes
experiencing even more losses with increased path loss as depicted by Figure 18 below.
Figure 18: Packet loss vs. path loss per payload, courtesy of [72].
3.4.6 DUTY CYCLE SUPPOSITION
Defined as the ratio of Superframe Order to Beacon Interval (SO:BI), or the ratio of
active time to active and inactive time of sensors in a network, duty cycle is amongst the
most resource-friendly mechanisms used to curb energy wastage and extend the lifetime
of nodes in WBANs [73], [74]. It must not use complex algorithms like TDMA, nor
specialized hardware e.g. wake-up radios, to discover network topologies and keep
nodes synchronized, like other mechanisms do. Duty cycling is also an ideal energy
reduction mechanism for dynamic networks such as WBANs, since it does not need
information about the network topology in order to function. More power is used during
packet reception than during packet transmission in WBANs, in order to enable the
42
pick-up of signals which are relatively weak. However, most often, actual signal
reception only happens for a fraction of the time, wasting energy in idle listening, which
consumes as much energy as packet reception. Duty cycling capitalizes on the reduction
of idle listening, while maintaining the reliability and latency constraints of WBANs
[75]. Duty cycle is expressed as:
𝐷𝑢𝑡𝑦 𝐶𝑦𝑐𝑙𝑒 =
𝑆𝐷
𝐵𝐼=
2𝑆𝑂
2𝐵𝑂= 2𝑆𝑂−𝐵𝑂 (40)
where 𝑆𝐷 is the superframe duration; 𝐵𝐼 is the beacon interval; 𝑆𝑂 is the superframe
order; and 𝐵𝑂 is the beacon order. In a study carried out by Sharma et al. [76] to
analyze the impact of varying BO and SO on beacon-enabled IEEE 802.15.4, they
reported a decrease in throughput; an increase in end-to-end delay; a higher packet loss
rate; and a decrease of energy consumed in the transmit and receive modes with a
corresponding increase of energy consumed in the idle mode when the duty cycle
decreases as portrayed by Figure 19 below. PGI is the packet generation interval. A
similar relationship between end-to-end delay and duty cycle in related studies carried
out by Petrova et al. [77] is represented by Figure 19 below.
Figure 19: Relationship between throughput; delay; packet loss; power
consumption; and duty cycle for a constant BO of 5 and varying SO, courtesy of
[76].
43
3.4.7 CONFIDENCE INTERVAL
Considering the part evidence plays in the validation of any research study, the
statistical significance of every scholarly piece of work in this domain cannot be
overemphasized. This began with determination of the size of samples to be used for the
research, which was guided by the chosen confidence level and margin of error [78]. In
addition to being the convention, a standard confidence level of 95 % is a safe haven
between the less accurate but more precise confidence level of 90 % and the more
accurate but less precise standard confidence level of 99 %. As opposed to the
traditional standard deviation of ±2.58 for a confidence level of 99 %, the 95 %
confidence level has ±1.96 as the traditional z-value and standard deviation [79]. This
means that there is a 95 % certainty that the mean body temperature and heart rate of a
population of humans would fall within the range of the confidence interval, demarcated
by the lower and upper confidence limits. Though different scenario-specific formulae
exist for the calculation of the requisite sample size for research studies, Equation (41)
below expresses the formula to calculate the sample size 𝑛 for quantitative based
research adopted in this study. The traditional error margin for this confidence level is
5 %.
𝑛 =
(𝑍1−∝ 2⁄ )2
. 𝑆𝐷2
𝑑2 (41)
where 𝑍1−∝ 2⁄ is the standard normal variate (1.96 at a 5 % error margin); 𝑆𝐷 is the
standard deviation obtained from previous or pilot studies; and 𝑑 is the absolute error or
precision (5 % in this scenario). Using a standard deviation of 50 which I computed
from 100 repetitions of transmitting signals from sensors to the sink in a trial
experiment, a sample size of 385 was obtained, which I rounded up to 400 for
experiments in this study.
44
3.5 THE SELECTIVE TRANSMISSION ALGORITHM
UNDERPINNING
The Selective Transmission Algorithm is a mechanism to optimize percentage
throughput, latency, and power consumption in WBANs by curbing congestion of the
wireless transmission medium. Its conception was motivated by the fact that
physiological signals in WBANs are usually repeated, especially when the subject under
observation is not manifesting symptoms which require intervention by medical
personnel or other stakeholders. Since the motive of WBANs is to sense and report
those signals which surpass a threshold value or which conform to some stipulated
condition, a mere periodic sensing and transmission of redundant signals results in a less
efficient system, especially bearing in mind the energy spent in transmitting and
receiving data packets, and the limited resources of Body Area Networks. The Selective
Transmission Algorithm capitalizes on the curbing of this redundancy in sensed data to
optimize reliability, latency, and energy consumption in BANs. Its flowchart and
pseudocode are found in Figure 20 and Figure 21 below, respectively.
45
Figure 20: Selective Transmission algorithm flowchart.
Start
Extract senseValue from data
currSentSampleSN
= 0?
Store senseValue
in tempData
Transmission
condition satisfied?
Perform CCA
senseValue =
tempData?
Replace tempData
with senseValue
Create out_of_energy
packet
No
Yes
Yes Yes
No
No
Yes
remainingEnergy
> 0
Sense data
No
No
Channel
clear?
maxNumRetries
?
No
Yes Yes
Transmit
packet
Discard
packet
46
1. IF (! isSink)
2.
IF remainingEnergy > 0
3.
SENSE data
4.
EXTRACT senseValue from data
5.
IF first packet
6.
CREATE the tempData variable
7.
STORE senseValue in tempData
8.
IF (! maxNumRetries)
9.
IF senseValue (< 35 and > 38)
10.
IF senseValue (>= 35 and =< 38) and (senseValue - tempData >= 0.9)
11.
DISPLAY senseValue
12.
ENCAPSULATE senseValue into a packet
13.
TRANSMIT packetsensor
14.
ELSE, DISCARD packetsensor
15.
ELSE DISCARD packetsensor
16.
ELSE, DISCARD packetsensor
17.
ELSE, IF senseValuesensor = tempDatasensor, DISCARD packetsensor
18.
ELSE, REPLACE tempDatasensor with senseValuesensor
19.
ELSE, CREATE out_of_energy_packet
20. ELSE, DISCARD packetsink
21. END
Figure 21: Pseudocode of Selective Transmission algorithm.
Using Castalia Simulator’s Resource Manager module, the Selective Transmission
Algorithm begins with a verification of the node’s current energy levels. This is to
ascertain that the node is viable to sense, process, and transmit a packet to the sink. To
sense and transmit a packet in Castalia Simulator using the CC2420 node at -25 dBm,
the node requires baseline node power of 40 mW, state transition power from the
default listen to the transmit state of 62 mW, and transmission power of 29.04 mW at -
25 dBm [80]. This adds up to 131.04 mW which equates to 0.1310 J of energy and
constitutes the basic amount of energy needed by a node to launch the algorithm.
Castalia Simulator’s remainingEngergy variable stores this value and assumes a value
of 0 when the node’s energy goes below it. After the node’s viability is confirmed, the
algorithm proceeds to sense physiological signals – body temperature and heart rate,
extracting the sensed value. For the very first signal, a tempData variable is created in
the node’s buffer into which a copy of the sensed signal’s value is stored. The value of
each subsequent signal’s sensed value is then compared with the value in tempData
using the condition per signal type for the body temperature and heart rate signals
47
analyzed below respectively, to determine if the signal is suitable for further processing.
Signals which pass the suitability condition are transmitted once the channel is
determined to be clear after a successful CCA mechanism, or discarded if the CCA is
unsuccessful, or if the maximum number of transmission retries has been reached.
Body Temperature: G. Kelly [81] defined 36.2oC – 37.5oC as normal human body
temperature range. A host of other researchers including Tousseau had 36.1oC – 37.2oC
as the normal temperature range [82]. With a resolution of 0.1, I adopted plausible
human body temperatures in the range of 32 ºC to 42 ºC in this study. Within this range,
values from 32 ºC to below 35 ºC constitute a medical condition called Hypothermia;
those from 35 ºC to 38 ºC represent normal body temperature; and those beyond 38 ºC
up to 42 ºC symbolize a medical condition called Hyperpyrexia, characterized by fever
[83]. A temperature of 37 ºC is considered as the body mean, having the possibility to
change by up to 0.7 ºC within a day, and for different parts of the body [84]. For this
reason, it takes about 5 minutes to accurately measure body temperature. For
experiments, I assumed oral temperature measurements because they very well reflect
the core body temperature [85]. I chose 0.0033 Hz as the frequency of change, which is
within the recommended 0 to 0.1 frequency range [86]. In Castalia Simulator, I used the
scenario based physical process and modified it to generate temperature values between
32 ºC and 42 ºC, each value lasting for 36.3636 s before changing to the next. Since the
sampling rate was set to 1 sample per second for both the Value Reporting and Selective
Transmission Algorithm scenarios, the former would sense and transmit 400 packets
(good enough to generate statistically relevant results at a confidence level of 95 %)
during the 400 s simulation period, while the latter is expected to generate and transmit
fewer packets based on the restricted threshold condition, which constrains packet
transmission to those packets which are medically relevant only. It is worth noting that
these values were a little skewed by parameters such as a device bias of 0.1 and device
noise of 0.1, changing the final value of the sensed signal by an amount of up to 0.9 ºC.
For all sensor nodes, the tempData variable is created for temporal storage of sensed
data (senseValue). The first sensed value is stored as tempData, which changes
iteratively each time a new sensed value is different from it. Prior to processing recently
sensed data (senseValue), its value is compared with that of the previously sensed data
which was stored as tempData. If senseValue falls within a safe range, or if the
48
difference between senseValue and tempData does not exceed a set value, then the
sensed data does not get transmitted. If the sensed data does not comply with the
condition set for transmission, it is discarded. Based on the explanation above, the only
packets which get transmitted would be those with their senseValue parameter having a
value below or beyond the normal body temperature range of 35 ºC to 38 ºC, as well as
those within the normal range with their senseValue parameter’s value less than or
greater than the stored tempData parameter’s value by an amount of up to, or greater
than 0.9 ºC. The value of 0.9 ºC was chosen because as per trial experiments, the sensed
value that was transmitted differed from the generated body temperature by an amount
up to 0.9 ºC. This was also confirmed upon comparison of the generated body
temperature values and transmitted temperature values during the experiment proper. I
designed it so because data packets which are sensed back-to-back have a higher
probability of adopting the same data value for a majority of physiological signal types.
I set the body temperature change frequency to 0.0033, in line with [86]. This meant
that for the 11 body temperature values from 32 ºC to 42 ºC, each sensed value was
retained for 36.36 s prior to sensing the next value. Considering the sensing frequency
of 1 Hz adopted for experiments, this meant that each value was sensed about 36 times
during the 400 s of simulation. Transmission prevention of redundant packets meant
that at any one time, there would be less packets being transmitted on the transmission
channel, decongesting the channel. This in turn reduces the number and probability of
packets which are prone to collision. Many more packets are thus guaranteed successful
arrival at the receiver’s radio. In addition, retransmissions are reduced, curbing packet
arrival latency at the receiver’s radio, and saving energy which should have been used
to retransmit erroneous or compromised packets. senseValuesensor and tempDatasensor are
the sensed and stored values by the sensor nodes respectively, while senseValuesink is
the sensed value by the sink. In the experiments, any data sensed by the coordinator was
discarded, since the coordinator’s duty was to manage the sensor nodes and forward
their sensed data to a base station for onward processing. The sensor nodes only
transmitted data which was of relevance to medical personnel, safeguarding the BAN’s
scarce resources.
Heart Rate: The heart rate signal had a similar measurement procedure to the body
temperature signal, differing in the sensed and threshold values. Unlike body
49
temperature, what is considered a normal heart rate can vary markedly depending on the
subject’s age. While the average maximum heart rate of a man who is 20 years old
could be as high as 200 beats per minute with a target heart rate zone between 100 to
170 beats per minute, a man who is 70 years of age could record an average maximum
heart rate of 150 beats per minute with a target heart rate zone from 75 beats per minute
to 128 beats per minute [87]. At rest, the normal heart rate is considered to be between
60 beats per minute to 100 beats per minute, having a frequency of between 1 to 1.67
Hz and an average heart rate of 70 beats per minute. A heart rate below 60 bpm results
in a medical condition called Bradycardia, while a heart rate above 100 bpm results in a
medical condition called Tachycardia. For experiments, I assumed a range of 111 heart
rate values ranging from 40 beats per minute to 150 beats per minute. The upper limit of
150 beats per minute would correspond to a 70-year-old man, if going by the “220 beats
per minute minus age” maximum heart rate formula. Though the resolution of heart rate
is 1 beat per minute, I did measurements after every 2 values (i.e. 40, 42, 44…),
meaning that each measured value changed after every 7.207 s, giving a total of 2000
readings within the 400 s of simulation and a heart rate to body temperature frequency
ratio of 5:1. This means that for the sensing frequency of 1 Hz adopted for experiments
(same as for body temperature), each value would be sensed about 7 times within the
400 s of simulation as opposed to about 36 times for the body temperature scenario.
Based on the explanation above, a sensed signal only gets transmitted if it is either
below the 60 bpm or above the 100 bpm normal range, as well as being within the
normal range but differing from the stored tempData value by an amount of up to or
more than 2 bpm.
Frequency band: I used the 2.4 GHz 802.15.4 PHY layer band because it
accommodates the highest number of channels – 16 channels relative to 10 channels and
1 channel for the alternative 915 MHz and 868 MHz bands respectively. In addition to
its support of the highest number of channels to accommodate many more devices, it
also boasts of the highest data rates. Furthermore, the 2.4 GHz carrier frequency has the
lowest BER compared to the other carrier frequencies [88].
Collision model: A collision model enables the radio to determine the impact of various
incoming signals (nature of interference) on the reception of each other. I used the
50
simplistic collision model having a value of 1 for the Selective Transmission Algorithm
because it maximized the effect of interference. If signals from the radios of two
different nodes are received by the coordinator’s radio even minimally, it assumes that a
collision has occurred. This gave an idea of what obtains in real life, though some
experiments by other researchers reported that the interference obtained is sometimes a
little higher than what obtains in real life [80].
Payload size: I chose payload sizes of 25 bytes, 50 bytes, 75 bytes, and 100 bytes
respectively, which when combined with the overhead of 33 bytes (Application header
of 8 bytes, Network header of 6 bytes, MAC header of 13 bytes, and PHY header of 6
bytes), culminate in packet sizes ranging from 58 bytes to 133 bytes, conforming to the
packet size constrains of the IEEE 802.15.4 standard. A MAC buffer size of 32 packets
and a network buffer size of 32 messages was chosen to amply accommodate sensed
packets and prevent buffer overflow in case of prolonged back-offs.
Radio: The CC2420 radio was chosen because of its proven suitability for use in
simulating body area networks [89]. The radio transmitted at a low power of -25 dBm in
conformance to the IEEE 802.15.4 specification of using a transmission power level in
the neighborhood of -10dBm so as to prevent overheating and damage of tissue. This is
also in conformance to the FCC stipulation of having an SAR of 1.6 W/Kg for devices
transmitting within 20 cm of the human body [90]. The coordinator had to stay awake,
receiving packets from all the sensor nodes, and then transmit them to a base station for
onward transmission to medical personnel and/or other stakeholders. This function
alone was resource intensive, the coordinator did not need to sense and transmit data
like the other sensors did. It only sent beacon packets which had a length of 125 bytes,
in addition to other control messages. The big size of beacons was in order to reduce
overhead, enabling a lot more information to be contained in a single beacon as opposed
to transmitting many beacons which increased the probability of collision.
Sampling frequency: Though the body temperature and heart rate signals may change
at a higher frequency in real life, the signals I chose for experiments changed every
36.3636 s and 7.2072 s respectively, in order to cater for the required repetitions.
According to the Nyquist theorem, signal sampling should be done at a frequency of at
51
least twice that of the highest frequency expected to be sampled, in order not to distort
the sampled waveform. I adopted a maxSampleInterval of 1,000 ms for both the Value
Reporting and the Selective Transmission Algorithm experiments using the body
temperature and heart rate signals respectively, to amply cover the worst case scenario
of the signals.
Back-off type: Non-persistent back-off (default back-off type) was used such that the
channel is not clear for transmission during back-off, preventing any possibility of
collision, and also making it possible to reduce power consumption during back-off by
putting the radio to the lower energy consuming sleep mode during this period. The
back-off duration was constant, corresponding to the backoffBaseValue parameter
which has a default value of 16 ms [91].
Routing protocol: All the experiments in this study used the BypassRouting protocol
for routing. This routing protocol only served as an interface between the Application
and MAC layers, and did not actually perform a routing function per se [92]. The nodes
in the experiment were configured in a star topology, each having a direct and one-to-
one communication with the coordinator, so no routing was required. A functional
routing protocol was intentionally left out so as not to influence the outcome of the
experiment. The topology was designed such that the only impedance between nodes
and the coordinator should be their function as sensing and transceiver devices, as well
as the nature of the communication medium between them. If implemented, a routing
protocol should have acted as a point of failure.
Aggregation: No aggregation was considered in the experiments because, for the
Selective Transmission Algorithm experiments most especially, the algorithm needed to
filter each sensed packet based on its value in order to implement its decongestion
mechanism. Nodes were configured to transmit all their data once the channel was free.
Though research has it that this is kind of greedy, it is more effective and efficient than
having to transmit data intermittently, interspersed with time-consuming back-offs. The
node buffers were set to 32 packets, reasonably sufficient for packet queueing during
back-offs [93].
52
3.6 CHOICE OF SIMULATOR Castalia Simulator version 3.3 referred to simply as Castalia was chosen as the
simulator of choice for this research amongst alternatives such as Matlab, NS2, NS3,
OPNET, NetSim, and REAL because it is very well suited for simulating event-driven
networks consisting of low-power devices functioning in a distributed manner, which is
characteristic of WBANs. It is implemented on the OMNET++ platform which
possesses features such as NED and INI file model creation and configuration; batch
execution capability; simulation results analysis; C++ editing; UML modeling; and bug
tracker integration. These features allow for the design and first-hand implementation of
algorithms and protocols using realistic node, radio, and wireless models which abstract
what obtains in real world settings. These are the most heralded of Castalia’s attributes.
Its highly parametric nature enables it to adequately capture and emulate the intrinsic
properties of a wide range of scenarios. Other characteristics which contributed in the
choice of Castalia as simulator for this study are enlisted below, courtesy of the
developers [80].
The channel model used in Castalia is based on thousands of measurements on
subjects in real world environments, comprising a path loss map and complex
variation of path loss in time and space. It considers the mobility of subjects and the
nodes they wear, analyzing interference as received signal strength. As such, it
directly captures the effect of noise on the transmitted signal. While path loss
constituted a map of how much the transmitted signal would attenuate in space, the
variability model gives a dynamic component to this attenuation with time. This
ensures that the results obtained are as close as possible to real world scenarios.
Castalia’s radio model is a software replica of real commercial low-power radios,
which model the reception of signals as a function of the signal to interference and
noise ratio (SINR), size of packet being transmitted, and the type of modulation
used. Moreover, it is versatile, allowing custom modulation types to be modeled by
defining the SNR-BER curve. Multiple transmission power levels are supported,
with the ability of defining different transmission power values for different nodes
in a BAN. In addition to modeling RSSI and carrier sensing in a realistic manner,
Castalia also captures the power consumption while switching between transmit,
receive, and sleep states, reminiscing real world scenarios.
53
The physiological parameter sensing process is extensively modelled, with the
abstraction of a very flexible physical process. Not only is the signal sensed, but the
device noise, bias, and power consumption are captured as well. Since different
nodes might have different timing, Castalia also takes note of clock drift.
Castalia includes a number of application, routing, and MAC protocols, as well as
blueprints to help kick-start the development and implementation of new protocols.
It adopts a modular nature in the development of algorithms and protocols. It is also
highly reliable, speedy, versatile, and scalable, attributes it owes partly to its event-
driven OMNET++ framework.
As depicted by Figure 22 below, Castalia is very modular. This modularity accounts
for its flexibility, enabling a protocol or algorithm formulated in one layer of the
WBAN protocol stack to be investigated with different protocols or algorithms of
different layers. The node module which itself is a composite module communicates
with other modules - mainly the Physical Process module, Wireless Channel
module, Resource Manager module, and Mobility Manager module through
message passing as indicated by the solid directional arrows, and function calling
portrayed by the dashed directional arrows. Each physical process is associated with
a particular module. Nodes sample the physical process by sending a message to its
corresponding module. Depending on the value of the message, they take a
particular action, and communicate with other nodes by sending messages to the
Wireless Channel module. Modules (and submodules if present), their parameters,
and input/output gates are easily formulated with the help of OMNET++’s NED
language. This helps Castalia to represent real world scenarios in a succinct manner.
Castalia supports the design and implementation of many and different types of
protocols using its NED blueprint, without altering it. For each experiment, selected
parameters from the .ned file are copied to a configuration file (.ini file) and the
values changed as desired to suit the goal of the experiment. Each experiment thus
has a unique configuration file. In addition, multiple values can be attributed to a
single parameter. Castalia runs as many simulations as there are values for one or
many parameters. This simplifies the comparison of the effect of one or more
parameters in one experiment.
An incorporated plotting feature is very instrumental in representing experimental
trends. This feature is versatile, enabling the plotting of different types of graphs at
54
different sizes and different levels of detail. One other good thing with the plot
feature is that it enables graphs to be exported in a number of graphic formats,
making it easy for them to be included in a number of different document types.
The inbuilt –r (repetition) and –c (confidence interval) features in Castalia help
smoothen random results, and enable pre-determination of the confidence level of
the results obtained. Castalia has 11 different random number streams, enabling the
repeat of experiments with a different number of random seeds. This increases the
quality of results, enabling the distinction of outliers from valid trends.
Figure 22: Communication within the node composite module of Castalia [80].
Mobility Manager
To/from Wireless Channel
Radio
MAC
Application
Sensors Manager Resource Manager
(Battery, CPU state,
Memory)
To/from Physical Process
Com
munic
ati
ons
com
posi
te m
odule
Routing
location
Any module
(read only)
To wireless channel
55
CHAPTER 4: RESEARCH
METHODOLOGY
More quantitative than qualitative in nature, the methodology I adopted in this research
was adapted from both the Engineering-based and Scientific-based approaches in [94],
and tips from domain literature as depicted by Figure 23 below. I chose this course
because of its suitability for the study, which is scientific in nature. Experiments were
conducted using tailor-made settings, drawing from concepts advanced by prior
researchers. To this end, I employed conceptual, experimental, and analytical methods
in tandem with a revisit of the research questions, as detailed below. Qualitatively,
trends from results of experiments I conducted were used to make guided conclusive
statements and generalizations.
Figure 23: Engineering research methodology (adapted from [58]).
Ask Research Questions
Review Literature
Formulate Hypothesis
Design Experiments
Collect & Analyze Results
Publish Findings
Hypothesis is True Hypothesis is False /
Partially True
Think! Try Again
56
For a recap of the research questions:
How effective is the channel decongesting Selective Transmission Algorithm in
optimizing packet reception, latency, and energy consumption in wireless body
area networks for medical applications?
What effect does payload size have on performance of the Selective
Transmission Algorithm?
How does duty cycle affect performance of the Selective Transmission
Algorithm?
How valid is the Selective Transmission Algorithm with regards to bit error rate,
fade depth distribution and signal-to-noise ratio?
4.1 CONCEPTUALIZATION AND MODELING
4.1.1 HYPOTHESIS Based on assertions made by [95], [96] pointing out throughput degradation, excessive
energy loss, severe packet loss, and packet delivery latency as undesirable effects of
congestion in Wireless Body Area Networks; pronouncements made by [97] pertaining
to the adverse effect large packet sizes have on packet reception rate and bit error rate;
coupled with the notion of a reduction in packet reception rate, increase in end-to-end
delay and reduction of energy consumed by nodes with increased duty cycling
expressed in [98]; I developed the hypothesis below, which sets the pace for this
research, and motivated my research questions, assuming a preventive measure to
mitigate the issues plaguing wireless body area networks.
If collisions resulting in data loss, delays, and a waste of energy were caused by a
congested channel, then decongesting the channel would certainly lead to gains in
throughput while curbing latency and energy consumption.
Results of prior studies purporting an increase in bit error rate; a reduction in
performance as fade depth increases; a reduction in reliability with a corresponding
decrease in signal-to-noise ratio as payload size increased [99], motivated the
investigation of performance gains brought about by the proposed Selective
Transmission Algorithm.
57
4.1.2 STRENGTH OF CHOSEN RESEARCH METHOD In order to mitigate the claim that quantitative methods inadequately represent the true
population and require a lot of resources [100], I used a sample size of 400 which is a
little more than the recommended statistically significant sample size (𝑛) of 385 for the
chosen confidence level of 95 % and error margin of 5 % computed using equation (41)
above. The slightly higher sample size improves the precision, and the 5 % error margin
enables results in an acceptable range without stretching on required resources [78]. A
regressive analysis of experimental results justifies the 5 % error margin used.
Assuming a simple random sample from a large population, the margin of error reduces
as the number of samples increase. To be more precise, the margin of error is inversely
proportional to the square root of the sample size [101], as expressed in equation (42)
below. Substituting the chosen sample size 𝑛 of 400 gives an error margin of 0.049
which is equivalent to 5 %.
𝑀𝑎𝑟𝑔𝑖𝑛 𝑜𝑓 𝑒𝑟𝑟𝑜𝑟 =
0.98
√𝑛 (42)
Compared to the conduct of experiments with humans and physical equipment which
entails risks of radiation and little control of the environment, I chose to use a computer
simulator (Castalia Simulator) for experiments because it ensured tailored and stable
environmental settings by defining requisite parameters. It is also less costly, and easier
to achieve multiple replications of the experiments [102]. The ability of Castalia
Simulator to store experimental results in text files makes conversion into basic data
analysis software such as Microsoft Excel, and the use of descriptive statistical tools
which I employed for analysis, easy.
4.1.3 NODE TOPOLOGY AND EXPERIMENTAL PARAMETERS I used a total of six nodes – one coordinator and five sensing nodes modelled as
CC2420 radios in a space of 4 x 4 m for experiments. They were placed on the body at
positions recommended for the sensing of major physiological signals –
Electromyography, Pulse Oximeter, Blood Oxygen Saturation, Blood Pressure, and
Electrocardiography [103] as portrayed by Figure 24 below. Six nodes also happen to be
the perfect number of nodes to accurately implement the path loss model in Castalia
[80]. The 5 sensing nodes – node 0, node 1, node 2, node 4, and node 5 were placed
0.64 m, 0.30 m, 0.67 m, 0.60 m, and 0.32 m from the sink (node 3) respectively. In
addition to the sensing nodes being placed as they would be placed on the human body,
58
their proximity to the sink ensured that they could successfully send and receive signals
to and from each other at a low transmission power of -25 dBm which I used for
experiments. I chose the transmission power to be sufficiently low to get signals across,
while respecting requirements of the Federal Communications Commission to keep the
specific absorption rate below 1.6 W/kg in 1 g of body tissue, which equates to a
maximum transmission radiation power of 1.6 mW [104]. The low transmission power
also reduced the footprint of interference from neighboring nodes [105]. Though
alternative research purports that the actual interference is lesser than what is obtained
from the addition of signals of neighboring nodes [80], this choice of a collision model
critically scrutinized and abstracted transmission medium disturbance in WBAN
settings. Some experimental parameters are depicted by Table 3, and performance
metrics analyzed below.
Figure 24: Body sensor placement for experiments.
0
1 2
3 4
5
LEGEND
Sensor node
Sink
1
2
3
4
5
EMG
Pulse Oximeter
SpO2
SINK
Blood pressure
0
ECG
59
Table 3: Experimental parameters
Parameter Value
Value
Reporting STA Body
Temperature STA Heart
Rate
Testbed dimension 2m x 2m 2m x 2m 2m x 2m
Simulation time 400 ms 400 ms 400 ms
Carrier frequency 2.4 GHz 2.4 GHz 2.4 GHz
Sensor type Temperature Temperature Heart rate
Collision model 1 1 1
Transmission power -25 dBm -25 dBm -25 dBm
Number of transmissions and interval 2 / 40 ms 2 / 40 ms 2 / 40 ms
Back-off type Non-persistent Non-persistent Non-persistent
Sample interval 1000 ms 1000 ms 1000 ms
Signal change frequency 0.0033 Hz 0.0033 Hz 0.0167 Hz
Device bias 0.1 0.1 0.1
Device noise 0.1 0.1 0.1
Device sensitivity 32 32 40
Device resolution 0.1 0.1 2
Device saturation 42 42 150
Remaining energy 0.1310 J 0.1310 J 0.1310 J
4.2 PERFORMANCE MEASURES
I used two sets of performance measures in this study. The first set – channel
decongestion, packet reception rate, packet loss rate, end-to-end delay, and power
consumption constituted core criteria for implementation of the proposed Selective
Transmission Algorithm on the one hand. On the other hand, the second set of
performance measures – bit error rate and signal-to-noise ratio served to validate the
results obtained from experiments against stipulated requirements in the domain. As a
recap, Value Reporting and both Selective Transmission Algorithm scenarios (body
temperature and heart rate) sensed and transmitted body temperature and heart rate
physiological signals to a sink, which interacted with 5 sensing nodes in a Wireless
Body Area Network on a simulated human body in Castalia. While Value Reporting
transmitted packets periodically (400 packets per node, being one packet per second),
both variants of the Selective Transmission Algorithm transmitted fewer packets,
depending on whether they passed the transmission condition analyzed in the flowchart
and pseudo code below, and were of medical significance. A brief resume of how the
performance measures were implemented follows.
60
4.2.1 CHANNEL DECONGESTION It is a measure of how much the proposed Selective Transmission Algorithm curbed
congestion in the transmission channel. It is worth noting that Value Reporting served
as the building block of, and provided the foundation code on which the Selective
Transmission Algorithm was built, because of its ability to sense and transmit data
constituting different values. Using Value Reporting for control experiments, channel
decongestion was investigated by launching the transmission of 400 packets per node
within 400 s of simulation, employing default settings for Value Reporting, and
recording the proportion of packets which were transmitted per payload size of 25 bytes,
50 bytes, 75 bytes, and 100 bytes respectively. Next, using similar settings but with the
Selective Transmission Algorithm implemented, the proportion of packets transmitted
per payload was again recorded, and compared with the Value Reporting scenario. In
order to investigate the effect of duty cycling on the Selective Transmission Algorithm,
the experiments were repeated, but this time at different duty cycle values of 0.2, 0.4,
0.6, 0.8, and 1.0 respectively, recording the proportion of packets transmitted per duty
cycle. Decongestion constituted the measuring rod of the Selective Transmission
Algorithm. How well it curbed congestion of the transmission channel relative to Value
Reporting was an indication of its effectiveness.
4.2.2 PACKET RECEPTION RATE A measure of reliability, I investigated the packet reception rate for Value Reporting
and both scenarios of the Selective Transmission Algorithm by comparing the
proportion of packets received to those transmitted per payload per scenario, repeating
each scenario 400 times to comply with the minimum number of samples required to
obtain statistically relevant results at the confidence level of 95 % which I chose for this
study. Since Value Reporting transmitted packets periodically at a regular rate of one
packet per second for 400 seconds of simulation by 5 sensing nodes, it is expected that
the more packets it transmits and the congestion which ensues thereof relative to the
Selective Transmission Algorithm scenarios which only transmitted packets that were
medically relevant, would result in a smaller proportion of packets received and hence
less reliability of the former relative to the latter. As it was the case with channel
decongestion, the proportion of packets received to that of packets transmitted per duty
cycle was recorded and compared for both Value Reporting and the Selective
61
Transmission Algorithm per duty cycle, to gain insight on how duty cycling affected
reliability while implementing the Selective Transmission Algorithm. The primary
function of every BAN is to sense and transmit physiological signals for appropriate
preemptive and/or curative measures to be taken by medical personnel or other
stakeholders. The proportion of packets received is thus paramount to evaluating the
effectiveness of any algorithm meant to optimize the functioning of BANs, as is the
goal of the Selective Transmission Algorithm.
4.2.3 PACKET LOSS RATE In addition to acting as a parameter for evaluating the Selective Transmission
Algorithm, the packet loss rate also doubles as a validator for the packet reception rate
performance measure, since they are supposed to be antagonistic, all things equal. For
the Selective Transmission Algorithm to effectively optimize BANs, it is expected that
its implementation would curb the packet loss rate. As was the case with packet
reception rate, I evaluated the packet loss rate by launching the transmission of 400
packets per node per payload size of 25 bytes, 50 bytes, 75 bytes and 100 bytes
respectively within 400 s of simulation, culminating in 2000 packets for all 5 sensing
nodes of the BAN for the Value Reporting scenario. Using similar settings (though with
fewer packets transmitted for the Selective Transmission Algorithm), this was repeated
400 times, first using the body temperature Selective Transmission Algorithm, and then
the heart rate Selective Transmission Algorithm scenarios. For each scenario, the
average proportion of failed packets per payload per scenario was recorded and
compared. In order to evaluate the effect of duty cycling on the Selective Transmission
Algorithm, the packet failure rate per duty cycle of 0.2, 0.4, 0.6, 0.8, and 1.0 were
recorded and compared as well. The failed packets were categorized as per their reason
for failure – failed packets with no interference, failed packets as a result of
interference, failed packets because their transmission power was below the sensitivity
threshold, and failed packets because the receiver was in a non RX (receive) state when
they were transmitted. An antagonistic correlation between packet reception rate and
packet loss rate would mean that all the transmitted packets were accounted for. In
addition, the proportion of, and reason for the failed packets would be known. This
could act as a starting point in improving the Selective Transmission Algorithm further.
62
4.2.4 END-TO-END DELAY Also referred to as one-way delay (OWD), this is the time lapse between when a packet
is transmitted by a sensing node, and when it is received by the sink [106]. As it was
with the other performance measures above, the average end-to-end delay was gotten
from each of the 400 packets transmitted per node during the Value Reporting scenario,
compared with that of the fewer packets transmitted during both Selective Transmission
Algorithm scenarios. I obtained the end-to-end delay by feeding the difference between
the timestamp of each packet prior to transmission and that of the timestamp during
reception with respect to the simulation time, as parameter into Castalia’s
SIMTIME_DBL() function [107]. I also repeated the experiment per scenario using
duty cycle values of 0.2, 0.4, 0.6, 0.8, and 1.0, in order to investigate the effect duty
cycling has on the Selective Transmission Algorithm with respect to latency.
Considering the need for accurate results in the medical industry, and mindful of the
fact that a signal arriving later than the 125 ms threshold set for medical applications
[108] might be as good as a signal not received, the importance of timely arrival of
sensed signals at the sink cannot be overemphasized. An improvement of the end-to-end
latency of signals generated and propagated in BANs which is amongst the desired
merits of the Selective Transmission Algorithm, would lead to a corresponding
reduction in the time medical personnel and other stakeholders react to, and curb the
undesirable conditions of which the sensed signals are symptoms.
4.2.5 ENERGY CONSUMPTION Most Wireless Body Area Network implementations have a depletable source of energy,
highlighting energy consumption as the scarcest of BAN resources [109]. This explains
why any feasible optimization scheme pertaining to BANs needs to consider energy
conservation. I used the CC2420 radio at a transmission power of -25 dBm. With this
specification, the radio should consume 29.04 mW or 0.02904 J of energy to transmit,
0.062 J of energy to receive, and 0.006 J of energy to run the radio circuitry for a
second, drawing 0.02 mJ of energy per sample [110]. State transitions from transmit to
receive, receive to transmit, and sleep to either receive or transmit consumed 0.062 J,
while transitions from any state to the sleep state consumed 0.0014 J of energy. I
obtained the energy consumed (which is automatically summed up by Castalia) by
taking the average of reported energy to transmit 400 samples each per node for the
63
Value Reporting scenario, and doing same for the fewer transmitted packets during both
Selective Transmission Algorithm scenarios, then comparing them. The remaining
battery lifetime could also be computed by subtracting energy consumed from the
18,720 J of initial node energy, then converting it to days. The Selective Transmission
Algorithm is expected to curb energy consumption, owing to its channel decongesting
feature.
4.2.6 BIT ERROR RATE Calculated for the unchanged portion of the signal after every signal change in Castalia,
and as a function of other signals received concurrently, BER is instrumental in
determining if a packet gets received, or discarded by the sink. Packets with a BER
above 0 are discarded [80]. Prior to delivering packets to the upper layers of the
networking stack, radios calculate their Packet Reception Rate (PRR) computed from
the Packet Error Rate (PER) which was in turn computed from the product of the Bit
Error Rate and the packet size in bits, using the inbuilt SNR2BER() Castalia function. I
used this function to compute the BER of each packet received by the sink and
appended results to the statistics collected per packet for the Value Reporting and both
Selective Transmission Algorithm scenarios. I then calculated the average BER for 400
repetitions per scenario per payload size for the Value Reporting and Selective
Transmission Algorithm scenarios. A BER in the order of 10-3 is the maximum allowed
for BANs [37]. A comparison of the average BER between Value Reporting, the
Selective Transmission Algorithm and the domain standard is a good measure of how
the Selective Transmission Algorithm fares, and portrays the quality of packets it
transmits.
4.2.7 SIGNAL-TO-NOISE RATIO Signal-to-noise ratio (SNR) is the primary determinant of whether a packet gets
received or discarded by the sink. It is used to derive the signal quality in a tumultuous
wireless transmission channel and determines the packet reception rate [111]. Reported
per packet received in Castalia, I computed the average SNR for all packets received
over 400 simulation repetitions for both Value Reporting and the Selective
Transmission Algorithm scenarios. A comparison of the values obtained per scenario
helped give more insight on the signal quality of the Selective Transmission Algorithm
64
compared with that of Value Reporting per payload. In comparing the SNR per duty
cycle, trends were gotten on the effect duty cycling has on the Selective Transmission
Algorithm.
4.2.8 FADE DEPTH DISTRIBUTION A measure of how much the immediate environment caused attenuation of the received
signal, fade depth distribution influences, and helps explain the interference impacting
transmitted signals. Constituting 13 buckets ranging from -50 dBm to 15 dBm in this
study, it represents the path loss variation around the mean path loss [112]. To capture
this difference in path loss, I took the mean of fade depths reported by 400 repetitions of
simulation results, from which I plotted a graph and compared with standards in the
likes of Lognormal, Weibull, and Rayleigh distributions. The fade depth distribution
gave more insight on the interference packets experienced during transmission.
4.3 EXPERIMENTATION
Simulator-hosted experiments were carried out in Castalia Simulator 3.3, which was
chosen because it provided a real life environment with a realistic path loss model and
wireless transmission channel variability amongst other features. This method gave me
a means of generating and analyzing data to confirm or refute my hypothesis. Another
motivation for choosing experimentation as a method of investigation and a means of
realizing my concept stemmed from the fact that it provided a user-controlled near-real-
world environment void of costs involved in the acquisition of physical equipment. In
addition, the object of my findings dwelt on the manipulation of electromagnetic signals
around the human body, which are purported to cause undesirable ill-health conditions
[113], and thus sensitive. Furthermore, I needed 400 samples in order to produce results
which were statistically relevant at the 95 % confidence level which I used. Simulator-
led experiments availed me from the inconvenience involved in rallying 400 subjects to
perform a study on them. The ability to setup steady experimental conditions with just
the variables I wanted as opposed to an uncontrolled real-world environment is yet
another reason why experimentation was a method worthy of implementation for the
study I carried out. Bias in the results obtained was checked by maintaining the same
common parameters for the Value Reporting scenario, as well as for both Selective
Transmission scenarios, and having 400 repetitions, taking their averages for further
65
analysis. However, I acknowledge the fact that no matter how accurate, the environment
captured by Castalia Simulator which was used for experiments is only second best
compared to using physical equipment in a real world, thus there might exist some
inaccuracies in the results I obtained due to the discrepancy between a simulated
environment and what obtains in the real world. Additionally, in my modification of the
physical process leading to sensed body temperature and heart rate physiological
signals, I used a linear stepping of values (though they were modified by deviceBias and
deviceNoise values), which might not exactly reflect what is obtained in a real world
setting.
All experiments adopted the topology in Figure 24 above. For the Value Reporting
scenario, each of the 5 sensing nodes transmitted 400 packets with payloads of 25 bytes,
50 bytes, 75 bytes and 100 bytes to the sink at the rate of 1 packet per second within 400
s of simulation, and with duty cycling of 0.2, 0.4, 0.6, 0.8, and 1.0 enforced. The
average number and proportion of packets transmitted, packets received, packets lost,
end-to-end delay, energy consumed, BER, SNR, and fade depth distribution were then
collected for 400 experimental repetitions conducted in Castalia Simulator, as computed
in Equation 41 above. A similar procedure was carried out for the Selective
Transmission Algorithm scenarios with the exception that fewer packets were
transmitted by the sensing nodes due to the channel decongesting function of the
algorithm. During the experiments, two types of physiological signals were sensed –
body temperature with a frequency of 0.0033 Hz (once every 5 minutes), and heart rate
with a frequency of 0.0166 Hz (once every minute) for all Value Reporting and
Selective Transmission Algorithm scenarios. All computations were done at a
confidence level of 95 % and a 5 % error margin.
4.4 ANALYSIS
The data I gathered from experiments were predominantly analyzed using descriptive
statistical methods – mean, standard error, standard deviation, variance, range, sum,
count, minimum, maximum, and confidence intervals incorporated in Castalia Simulator,
and Microsoft Excel. Graphs were plotted for better visual clarity and elucidation of
trends. In the sample size calculation, I assumed a 100 % response rate, because rather
66
than dealing with human subjects who might not turn up for sampling, this study dealt
with computer simulations having well stated and precise parameters. All conclusions
made were guided by experimental results and trends derived thereof, as well as results
obtained from related research in the domain.
Conceptualization aided by domain literature laid the groundwork for coining the
hypothesis, node topology and experimental parameters, as well as the performance
measures overviewed above for all four research questions. Experiments which were
qualitatively analyzed using graphs to abstract trends in the quantitative results obtained,
provided an empirical means of answering all four research questions. An analysis of
results obtained and a discussion thereof follows.
67
CHAPTER 5: RESULTS AND
DISCUSSION
5.1 RESULTS
5.1.1 CHANNEL DECONGESTION While implementing the heart rate signal for the Selective Transmission Algorithm, the
sensor nodes transmitted 1280 packets as opposed to 1112 during implementation of the
body temperature variant, and 2000 packets during implementation of Value Reporting.
This led to the Selective Transmission Algorithm heart rate variant decongesting the
wireless transmission channel by 36 % relative to Value Reporting, and the STA body
temperature variant decongesting the channel further by 8.4 % relative to the heart rate
variant, in line with expectations. The number of packets transmitted and hence channel
decongestion was equal across payload sizes. As portrayed by Figure 25 below.
Figure 25: Channel decongestion comparison per payload between VR and STA.
5.1.2 PACKET RECEPTION RATE (PRR) Excluding beacons, management and control packets, the 5 sensing nodes transmitted
2000 packets per payload for VR, 1112 packets per payload for STA using the body
temperature signal type, and between 1215 at a payload of 100 bytes to 1223 packets at
0
5
10
15
20
25
30
35
40
45
50
0
500
1000
1500
2000
2500
25 50 75 100
Pe
rce
nta
ge o
f p
acke
ts (
%)
Nu
mb
er
of
pac
kets
Payload (bytes)
Comparison of channel decongestion
Percentagedecongestion by STA(Body Temperature)Percentagedecongestion by STA(Heart Rate)No. of Packets sentby Value Reporting
No. of packets sentby STA (BodyTemperature)No. of packets sentby STA (Heart Rate)
68
a payload of 25 bytes while implementing STA using heart rate as signal type. The
percentage of packets received decreased as the payload size increased for all three
scenarios, with a similar pattern in the proportion of packets received per payload per
node. While the percentage of packets received (from between 94.76 % and 95.04 %
with a payload of 100 bytes to between 95.39 % and 95.63 % when the payload was 25
bytes) for STA using heart rate as signal type clearly exceeded that received for VR by
15.24 % to 17.41 % across payload sizes (the PRR of the body temperature STA variant
exceeded that of VR by 15.5 % and 17.11 % across payload sizes), the two STA
variants (body temperature and heart rate scenarios respectively) had a similar
percentage of packets received per payload, with the most conspicuous difference of
0.10 % observed when the payload was 50 bytes as depicted by Figure 26 below.
Figure 26: Comparison of percentage packet reception between VR and STA using
body temperature and heart rate signal types.
5.1.3 PACKET LOSS RATE As expected, the number and proportion of failed packets for the STA scenario using
heart rate as the signal type was inversely proportional to that of received packets,
ranging from 4.45 % when a payload of 25 bytes was used, up to 5.08 % when a 100-
byte payload was used as portrayed by Figure 27 below. Though the STA heart rate
scenario experienced about 5 times less failed packets relative to the VR scenarios, it
had a similar proportion of failed packets compared to the STA Body Temperature
scenario (4.50 % and 5.04 % at 25 bytes and 100 bytes payload respectively), though
94.00
94.20
94.40
94.60
94.80
95.00
95.20
95.40
95.60
77.00
77.50
78.00
78.50
79.00
79.50
80.00
80.50
81.00
25 50 75 100
Pe
rce
nta
ge o
f p
acke
ts f
or
STA
(%
)
Pe
rce
nta
ge o
f p
acke
ts f
or
VR
(%
)
Payload (bytes)
Comparison of percentage packet reception
Percentage ofpacketsreceived by VR
Percentage ofpacketsreceived by STA(BodyTemperature)Percentage ofpacketsreceived by STA(Heart Rate)
69
with slightly lower values for small payload sizes, and slightly higher values for bigger
payload sizes. For all three scenarios, the bulk of failed packets were either as a result of
their signal power being inferior to the receiver sensitivity, or their bit errors exceeding
the allowed threshold, though their interference did not exceed the sink radio’s noise
floor. Only a few packets failed because the sink was not in the receive state (sink either
in transmit state, or changing states). However, though these failed packets increased as
the payload size increased for the VR scenario, they leveled up and even reduced for the
STA Body Temperature and STA Heart Rate scenarios respectively as portrayed by
Figure 27 below.
Figure 27: Comparison of failed packets distribution per payload between VR,
STA (Body Temperature), and STA (Heart Rate).
5.1.4 END-TO-END DELAY All three scenarios (Value Reporting, STA using the body temperature signal, and STA
using the heart rate signal) experienced minimum and maximum delay values which
though increased as payload increased, had the same values when the payload was 50
bytes and 75 bytes respectively. The difference between average end-to-end delay was
also least obvious with these payload sizes as observable from Table 4 and Figure 28
below, with increasing confidence intervals as payload increased. Though both the body
temperature and heart rate variants of the Selective Transmission Algorithm reduced the
average end-to-end delay with respect to Value Reporting, the STA body temperature
0
5
10
15
20
25
0
50
100
150
200
250
300
350
400
450
25 50 75 100
Per
cen
tag
e o
f p
ack
ets
(%)
Nu
mb
er o
f p
ack
ets
Payload (bytes)
Comparison of Lost packets between VR and STA variants
Percentage of VR
packets lost
Percentage of STA
packets lost (Body
Temperature)
Percentage of STA
packets lost (Heart
Rate)
Total number of VR
packets lost
Total number of STA
packets lost (Body
Temperature)
Total number of STA
packets lost (Heart
Rate)
70
variant recorded the lowest delay amongst all three scenarios with lower payload sizes.
The heart rate STA variant however changed this pattern and reported the lowest delay
values as payload increased to 75 bytes and 100 bytes respectively.
Table 4: Minimum, maximum, average delay comparison; percentage reduction of
end-to-end delay by STA body temperature and heart rate variants relative to VR.
End-to-end delay per payload for VR and STR variants
Description Payload
25
bytes 50
bytes 75
bytes 100
bytes
Minimum delay for VR 1 3 3 5
Average delay for VR 1.0753 3.1206 3.1249 5.2254
Maximum delay for VR 17 19 19 21
Minimum delay for STA Temperature 1 3 3 5
Average delay for STA Temperature 1.0713 3.1098 3.1191 5.2171
Maximum delay for STA Temperature 17 19 19 21
Percentage reduction of delay by STA Temperature 0.3794 0.3481 0.1865 0.1587
Minimum delay for STA Heart Rate 1 3 3 5
Average delay for STA Heart Rate 1.0638 3.1049 3.1223 5.2222
Maximum delay for STA Heart Rate 17 19 19 21
Percentage reduction of delay by STA Heart Rate 1.0743 0.5038 0.0828 0.0604
Figure 28: End-to-end delay comparison between VR & STA, and percentage
delay reduction of STA body temperature and heart rate variants relative to VR.
0
0.2
0.4
0.6
0.8
1
1.2
0
1
2
3
4
5
6
25 50 75 100
Per
cen
tag
e d
ela
y (
%)
Del
ay
(m
s)
Payload (bytes)
Average delay for
VR
Average delay for
STA Temperature
Average delay for
STA Heart Rate
Percentage reduction
of delay by STA
Temperature
Percentage reduction
of delay by STA
Heart Rate
Comparison of End-to-end delay between VR and STA Comparison of End-to-end delay between VR and STA variants
71
5.1.5 ENERGY CONSUMPTION The average energy consumption per sensor node is displayed in Table 5 below. For all
the Value Reporting and Selective Transmission Algorithm scenarios, the energy
consumed per payload decreased as payload increased, though the Value Reporting
scenario consumed an average of 16.6 % more energy than both Selective Transmission
Algorithm variants as portrayed by Table 5 below. Energy saving was even more
pronounced with an increase in payload size from 25 bytes to 100 bytes as observed
from Figure 29 below. This was matched by a decrease in confidence intervals,
indicating higher precision in that order. Though one might expect more energy to be
consumed during the transmission of larger packets as a result of more work done and
hence energy required to complete the task, the reverse was true, a trend which was
similar to that observed from the Value Reporting scenario. Considering the initial
18,720 J of energy the nodes were equipped with, each node was left with a remaining
battery lifetime of between 3.8255 days to 3.8280 days across payloads for the STA
heart rate scenario compared to a lifetime of between 3.1869 days and 3.1873 days
across payloads for the VR scenario (saving about 16.6 % of energy), the former
keeping the nodes on for more than 15 hours relative to the latter, and for a duration
between 17 seconds and 45 seconds relative to the STA body temperature scenario.
Table 5: Comparison of energy consumed between VR and STA variants (body
temperature and heart rate).
Energy consumption comparison between VR and STA variants
Description Payload
25 bytes 50 bytes 75 bytes 100 bytes
Consumed energy for VR 163.08 163.02 162.97 162.92
Lifetime for VR 3.1869 3.1869 3.1872 3.1873
Initial energy 18720 18720 18720 18720
Consumed energy for STA Temperature 135.9372 135.9106 135.8836 135.8581
Lifetime for STA Temperature 3.8253 3.8260 3.8268 3.8275
Percentage energy saved by STA Temperature 16.6439 16.6295 16.6205 16.6105
Consumed energy by STA heart rate 135.9302 135.8997 135.8687 135.8395
Lifetime of STA heart rate 3.8255 3.8264 3.8272 3.8280
Percentage energy saved by STA heart rate 16.6481 16.6362 16.6296 16.6220
Lifetime (hours) extension by STA heart rate
relative to VR 15.2837 15.2793 15.2751 15.2704
Lifetime (seconds) extension by STA heart rate
relative to STA body temperature 17.0978 26.6378 36.2737 45.3713
72
Figure 29: Comparison of energy consumption per payload between VR and STA
variants (body temperature and heart rate).
5.1.6 EFFECT OF DUTY CYCLING ON THE SELECTIVE TRANSMISSION ALGORITHM
5.1.6.1 CHANNEL DECONGESTION
Fewer packets were transmitted per payload as the duty cycle reduced from 1.0 to 0.2,
the decrease being more conspicuous with lower duty cycling enforced as can be
observed from Table 6 and Figure 30 below. On average, the STA scenario using heart
rate as signal type transmitted more packets relative to the STA variant using body
temperature, per payload. The latter in turn decongested the channel more than the
former by magnitudes ranging from 6.5 % using a payload of 25 bytes at a duty cycle of
0.2 to 8.4 % while using a payload of 100 bytes when no duty cycling was enforced
(duty cycle of 1.0). The pattern obtained while simulating with heart rate as signal type
matched that obtained when the body temperature signal type was used. For both STA
scenarios, disparity between the percentages of channel decongestion reduced as duty
cycle reduced from 1.0 to 0.2 across all payload sizes.
135.78
135.80
135.82
135.84
135.86
135.88
135.90
135.92
135.94
135.96
162.50
162.60
162.70
162.80
162.90
163.00
163.10
25 50 75 100
Ene
rgy
(J)
Payload (bytes)
Energy consumption comparison between VR and STA
Consumedenergy for VR
Consumedenergy for STATemperature
Consumedenergy by STAheart rate
73
Table 6: Comparison of channel decongestion per duty cycle per payload between
STA variants (body temperature and heart rate).
Channel decongestion comparison between STA variants per payload per duty cycle
Description Duty cycle
0.2 0.4 0.6 0.8 1.0
Transmitted packets, 25 bytes, body temperature 893 1058 1087 1103 1112
Percentage decongestion, 25 bytes, temperature 55.35 47.10 45.65 44.85 44.40
Transmitted packets, 25 bytes, heart rate 1023 1217 1251 1270 1280
Percentage decongestion, 25 bytes, heart rate 48.85 39.15 37.45 36.50 36
Transmitted packets, 50 bytes, body temperature 919 1041 1095 1105 1112
Percentage decongestion, 50 bytes, temperature 54.05 47.95 45.25 44.75 44.40
Transmitted packets, 50 bytes, heart rate 1058 1197 1260 1271 1280
Percentage decongestion, 50 bytes, heart rate 47.10 40.15 37 36.45 36
Transmitted packets, 75 bytes, body temperature 911 1047 1089 1106 1112
Percentage decongestion, 75 bytes, temperature 54.45 47.65 45.55 44.70 44.40
Transmitted packets, 75 bytes, heart rate 1048 1204 1254 1273 1280
Percentage decongestion, 75 bytes, heart rate 47.60 39.80 37.30 36.35 36
Transmitted packets, 100 bytes, body temperature 937 1043 1089 1108 1112
Percentage decongestion, 100 bytes, temperature 53.15 47.85 45.55 44.60 44.40
Transmitted packets, 100 bytes, heart rate 1078 1200 1254 1274 1280
Percentage decongestion, 100 bytes, heart rate 46.10 40 37.30 36.30 36
Figure 30: Percentage channel decongestion of STA variants (body temperature
and heart rate) per payload per duty cycle.
30
35
40
45
50
55
60
0.2 0.4 0.6 0.8 1.0
Pe
rce
nta
ge d
eco
nge
stio
n (
%)
Duty cycle
Channel decongestion per duty cycle
Percentage decongestion,25 bytes, temperaturePercentage decongestion,25 bytes, heart ratePercentage decongestion,50 bytes, temperaturePercentage decongestion,50 bytes, heart ratePercentage decongestion,75 bytes, temperaturePercentage decongestion,75 bytes, heart ratePercentage decongestion,100 bytes, temperaturePercentage decongestion,100 bytes, heart rate
74
5.1.6.2 PACKET RECEPTION RATE
Although the Selective Transmission Algorithm using the heart rate signal transmitted
more packets (1280 packets) than its variant using the body temperature signal (1112
packets) per payload size, the percentage packet reception had a mixed pattern with
respect to both variants as depicted by Figure 31 below. The relative number of packets
received to those transmitted and hence the percentage of packets received reduced as
duty cycling reduced from 1.0 (no duty cycling) to 0.8. This pattern changed, recording
a higher percentage of packets received as the duty cycle reduced further to 0.6 and 0.4,
then reduced once more as the duty cycle troughed at 0.2. This trend was similar for
both STA variants across all payload sizes from 25 bytes to 100 bytes. There was a
sharp drop in the percentage of packets received by the sink at a duty cycle of 0.8 for
both STA variants across payloads ranging from 50 bytes to 100 bytes as portrayed by
Table 7 below. Furthermore, the pattern of percentage packets received per duty cycle
across payload sizes from 25 bytes to 100 bytes was similar for both STA variants.
75
Table 7: Comparison of the number and percentage of packets received per
payload per duty cycle between STA variants (body temperature and heart rate).
Packet reception comparison between STA variants per payload per duty cycle
Description Duty cycle
0.2 0.4 0.6 0.8 1.0
Transmitted, 25 bytes, temperature 893 1058 1087 1103 1112
Received 25 bytes, body temperature 771 895 873 771 1062
Percentage reception, 25 bytes, temperature 69.33 80.49 78.51 69.33 95.50
Transmitted, 25 bytes, heart rate 1023 1217 1251 1270 1280
Received 25 bytes, heart rate 889 1029 1004 887 1223
Percentage reception, 25 bytes, heart rate 69.45 80.39 78.44 69.30 95.55
Transmitted, 50 bytes, temperature 919 1041 1095 1105 1112
Received 50 bytes, body temperature 800 880 863 308 1061
Percentage reception, 50 bytes, temperature 71.94 79.14 77.61 27.70 95.41
Transmitted, 50 bytes, heart rate 1058 1197 1260 1271 1280
Received 50 bytes, heart rate 923 1013 995 345 1220
Percentage reception, 50 bytes, heart rate 72.11 79.14 77.73 26.95 95.31
Transmitted, 75 bytes, temperature 911 1047 1089 1106 1112
Received 75 bytes, body temperature 795 882 846 305 1058
Percentage reception, 75 bytes, temperature 71.49 79.32 76.08 27.43 95.14
Transmitted, 75 bytes, heart rate 1048 1204 1254 1273 1280
Received 75 bytes, heart rate 914 1017 978 349 1218
Percentage reception, 75 bytes, heart rate 71.41 79.45 76.41 27.27 95.16
Transmitted, 100 bytes, temperature 937 1043 1089 1108 1112
Received 100 bytes, body temperature 801 894 837 306 1056
Percentage reception, 100 bytes, temperature 72.03 80.40 75.27 27.52 94.96
Transmitted, 100 bytes, heart rate 1078 1200 1254 1274 1280
Received 100 bytes, heart rate 921 1024 964 353 1215
Percentage reception, 100 bytes, heart rate 71.95 80.00 75.31 27.58 94.92
Number of packets generated, temperature 1112 1112 1112 1112 1112
Number of packets generated, heart rate 1280 1280 1280 1280 1280
76
Figure 31: Percentage packet reception per payload per duty cycle of STA variants
(body temperature and heart rate).
5.1.6.3 PACKET FAILURE RATE
The percentage of failed packets had a similar pattern while using heart rate as signal
type for the Selective Transmission Algorithm, as it was when the body temperature
signal type was used as portrayed by Figure 32 below. However, the former registered
fewer failed packets when duty cycling was at its lowest and highest respectively, and
more failed packets in between relative to the latter while using small payload sizes. As
the payload size increased, the Selective Transmission Algorithm variant using the heart
rate signal reported higher packet failure rates with low duty cycling, and lower
percentage failed packets when duty cycling was at its peak respectively, relative to the
variant using body temperature as signal type. When the payload size peaked at 100
bytes, this pattern was antagonistic to that produced with small payload sizes - higher
packet failure rates were experienced by the heart rate STA variant when duty cycling
was lowest and highest respectively, while fewer packets failed in between, relative to
the scenario using body temperature as signal type.
5.1.6.3.1 Failed packets with no interference:
Being the third highest category of failed packets, the packets which failed with no
interference tended to reduce with reduced duty cycling as portrayed by Figures 49 (a),
(b), (c), and (d) when small payload sizes were used, though there was an increase
towards the trough of duty cycling, before decreasing again when duty cycling was at its
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0.2 0.4 0.6 0.8 1.0
Pe
rce
nta
ge p
acke
ts (
%)
Duty cycle
Percentage packet reception per duty cycle
Percentage reception, 25bytes, temperature
Percentage reception, 25bytes, heart rate
Percentage reception, 50bytes, temperature
Percentage reception, 50bytes, heart rate
Percentage reception, 75bytes, temperature
Percentage reception, 75bytes, heart rate
Percentage reception, 100bytes, temperature
Percentage reception, 100bytes, heart rate
77
lowest. A similar trend was observed as the payload size increased. However, the slight
increase in number of failed packets with no interference was observed earlier, and
lasted longer as payload increased, prior to decreasing anew when duty cycling was at
its peak. Implementing the Selective Transmission Algorithm with heart rate as signal
type revealed that the percentage of failed packets with no interference were initially
lower, and then higher relative to those of the scenario using body temperature as signal
type when the payload size was 25 bytes as duty cycling increased. An increase in
payload size led to a reduction of percentage failed packets with no interference as duty
cycling reduced from 1.0 to 0.4 while using a payload of 75 bytes, and from 1.0 to 0.6
when the payload size was 100 bytes.
5.1.6.3.2 Failed packets with interference:
Constituting the lowest category of failed packets as can be observed from Table 8 and
Figure 32 below, the packets which were not received by the sink due to the effect of
interference mostly surfaced when duty cycling was at its lowest, and when it was at its
highest respectively. However, the weightage of these packets was more with higher
duty cycles, and with increased payload sizes. A comparison of the percentage of failed
packets with interference between implementations of the Selective Transmission
Algorithm using heart rate signals and body temperature signals reported a higher value
for the former relative to the latter when duty cycling was high and a small payload size
was used. As the duty cycle reduced, the percentage of failed packets with interference
tended to reduce. An increase in payload to 100 bytes which was the highest for this
simulation, caused the percentage of this category of failed packets to be higher for the
body temperature STA variant with higher duty cycling enforced, and lower with less
duty cycling enforced, relative to the heart rate STA variant.
5.1.6.3.3 Failed packets below sensitivity:
This category of packets represented the second highest group of failed packets as
portrayed by Figure 32 below. They tended to reduce with reduced duty cycling, though
a slight increase distorted this trend when duty cycling reduced to about half its trough
value, before decreasing once more at the lowest duty cycle of 0.2. This minimal
increase in the number of failed packets below sensitivity was observed even earlier as
duty cycling reduced, with bigger payload sizes. When no duty cycling was enforced,
78
the weightage of packets which failed because their transmission power was inferior to
the sensitivity threshold reduced as payload size increased. Implementing the Selective
Transmission Algorithm with the heart rate signal gave rise to a lower percentage of
failed packets below the receiver's sensitivity threshold than the body temperature
variant did while reducing duty cycling, when the payload was 25 bytes. The number of
this category of failed packets increased progressively relative to that of the body
temperature STA variant, as payload size increased, while reducing the duty cycle.
5.1.6.3.4 Failed packets, non RX state:
The highest proportion of packets failed because the receiver was not in the "receive"
state (the receiver was either in the "transmit" state, or changing states from transmit to
receive and vice versa). These packets tended to increase with lengthier duty cycles as
depicted by Table 8 and Figure 32 below. However, as payload size increased, there
was a slight drop in the proportion of this packet category, progressively observed
earlier as the duty cycle reduced. For a small packet size of 25 bytes, the percentage of
failed packets due to the receiver not being in the receive state was higher for the heart
rate signal than it was for the body temperature signal while reducing the duty cycle
during implementation of the Selective Transmission Algorithm. As payload size
increased, the percentage of failed packets for the heart rate signal type progressively
got lower than that of the body temperature signal with moderate duty cycling (0.6 and
0.8), before increasing slightly once more at duty cycles of 0.2 and 0.4 respectively.
79
Table 8 (a), (b), (c), (d): Number and percentage of failed packets per payload per
duty cycle of STA variants.
(a) Payload = 25 bytes
Failed packets comparison between STA variants per payload per duty cycle
Description
Duty cycle =
0.2
Duty cycle =
0.4
Duty cycle =
0.6
Duty cycle =
0.8
Duty cycle =
1.0
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
Failed, no
interference, body
temperature
4 1.1730 4 1.8433 4 1.6736 7 2.0528 23 46.0000
Failed, no
interference, heart
rate
5 1.2788 4 1.5936 5 1.8116 8 2.0356 26 45.6140
Failed with
interference, body
temperature
1 0.2933 0 0.0000 0 0.0000 0 0.0000 0 0.0000
Failed with
interference, heart
rate
1 0.2558 1 0.3984 0 0.0000 1 0.2545 1 1.7544
Failed below
sensitivity, body
temperature
4 1.1730 3 1.3825 5 2.0921 10 2.9326 24 48.0000
Failed below
sensitivity, heart
rate
4 1.0230 2 0.7968 6 2.1739 11 2.7990 27 47.3684
Failed, non RX
state, body
temperature
332 97.3607 210 96.7742 230 96.2343 324 95.0147 3 6.0000
Failed, non RX
state, heart rate
381 97.4425 244 97.2112 265 96.0145 373 94.9109 3 5.2632
Total No. & % of
failed packets,
body temperature
341 30.6655 217 19.5144 239 21.4928 341 30.6655 50 4.4964
Total No. & % of
failed packets,
heart rate
391 30.5469 251 19.6094 276 21.5625 393 30.7031 57 4.4531
80
(b) Payload = 50 bytes
Failed packets comparison between STA variants per payload per duty cycle
Description
Duty cycle =
0.2
Duty cycle =
0.4
Duty cycle =
0.6
Duty cycle =
0.8
Duty cycle =
1.0
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
Failed, no
interference, body
temperature
4 1.2821 4 1.7241 5 2.0080 11 1.3682 24 47.0588
Failed, no
interference, heart
rate
5 1.4006 4 1.5417 6 2.1053 13 1.3904 29 48.3333
Failed with
interference, body
temperature
1 0.3205 0 0.0000 0 0.0000 0 0.0000 1 1.9608
Failed with
interference, heart
rate
1 0.2801 0 0.1395 0 0.0000 0 0.0000 1 1.6667
Failed below
sensitivity, body
temperature 4 1.2821 4 1.7241 5 2.0080 16 1.9900 22 43.1373
Failed below
sensitivity, heart
rate
4 1.1204 4 1.4903 6 2.1053 18 1.9251 26 43.3333
Failed, non RX
state, body
temperature
303 97.1154 224 96.5517 239 95.9839 777 96.6418 4 7.8431
Failed, non RX
state, heart rate
347 97.1989 259 96.8284 273 95.7895 904 96.6845 4 6.6667
Total No. & % of
failed packets,
body temperature
312 28.0576 232 20.8633 249 22.3921 804 72.3022 51 4.5863
Total No. & % of
failed packets,
heart rate
357 27.8906 267 20.8594 285 22.2656 935 73.0469 60 4.6875
81
(c) Payload = 75 bytes
Failed packets comparison between STA variants per payload per duty cycle
Description
Duty cycle =
0.2
Duty cycle =
0.4
Duty cycle =
0.6
Duty cycle =
0.8
Duty cycle =
1.0
No.
of
pkts.
% of pkts. No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of pkts. No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
Failed, no
interference,
body
temperature
4 1.2618 4 1.7391 5 1.8797 11 1.3631 27 50.0000
Failed, no
interference,
heart rate
5 1.3661 4 1.5209 5 1.7903 12 1.2889 30 48.3871
Failed with
interference,
body
temperature
1 0.3155 0 0.0000 0 0.0000 0 0.0000 1 1.8519
Failed with
interference,
heart rate
2 0.5464 0 0.0000 0 0.1235 0 0.0000 1 1.6129
Failed below
sensitivity, body
temperature
4 1.2618 3 1.3043 6 2.2556 16 1.9827 22 40.7407
Failed below
sensitivity, heart
rate 4 1.0929 4 1.5209 7 2.1484 18 1.9334 26 41.9355
Failed, non RX
state, body
temperature
308 97.1609 223 96.9565 255 95.8647 780 96.6543 4 7.4074
Failed, non RX
state, heart rate
355 96.9945 255 96.9582 290 95.9378 901 96.7777 5 8.0645
Total No. & %
of failed packets,
body
temperature
317 28.5072 230 20.6835 266 23.9209 807 72.5719 54 4.8561
Total No. & %
of failed packets,
heart rate 366 28.5938 263 20.5469 302 23.5938 931 72.7344 62 4.8438
82
(d) Payload = 100 bytes
Failed packets comparison between STA variants per payload per duty cycle
Description
Duty cycle =
0.2
Duty cycle =
0.4
Duty cycle =
0.6
Duty cycle =
0.8
Duty cycle = 1.0
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
No.
of
pkts.
% of
pkts.
Failed, no
interference,
body
temperature
4 1.2862 4 1.8349 5 1.8182 10 1.2346 26 46.4286
Failed, no
interference,
heart rate
5 1.3928 4 1.5625 6 1.8987 12 1.2945 30 45.9770
Failed with
interference,
body
temperature
1 0.3215 0 0.0000 0 0.0000 0 0.0000 2 3.5714
Failed with
interference,
heart rate
1 0.2786 0 0.0000 1 0.3165 0 0.0000 2 2.6820
Failed below
sensitivity, body
temperature
4 1.2862 3 1.3761 6 2.1818 16 1.9753 20 35.7143
Failed below
sensitivity, heart
rate 4 1.1142 4 1.5625 6 1.8987 18 1.9s417 23 36.3985
Failed, non RX
state, body
temperature
302 97.1061 211 96.7890 264 96.0000 784 96.7901 8 14.2857
Failed, non RX
state, heart rate
349 97.2145 248 96.8750 303 95.8861 897 96.7638 10 14.9425
Total No. & %
of failed packets,
body
temperature
311 27.9676 218 19.6043 275 24.7302 810 72.8417 56 5.0360
Total No. & %
of failed packets,
heart rate 359 28.0469 256 20.0000 316 24.6875 927 72.4219 65 5.0781
83
Figure 32: Comparison of percentage failed packets per payload per duty cycle of STA variants.
5.1.6.4 END-TO-END DELAY
The average end-to-end delay of the Selective Transmission Algorithm implementation
using heart rate as signal type was higher than that of its body temperature variant with
low duty cycling implemented while employing a payload size of 25 bytes. As duty
cycling increased, the delay experienced by the heart rate variant dropped slightly below
that of its body temperature alternative, then increased slightly before experiencing a
relative decrease anew as duty cycling peaked (duty cycle of 1.0). As payload size
increased, the heart rate variant tended to experience a relative decrease in end-to-end
delay as duty cycling increased, before having a slight relative increase as both duty
cycling and payload size peaked at 1.0 and 100 bytes respectively. Both STA variants
experienced a general decrease in end-to-end delay as duty cycling increased, and an
overall increase as payload size increased per duty cycle. The confidence interval was
lower for the Selective Transmission Algorithm variant using heart rate as signal type
relative to that using body temperature for all duty cycle and payload values, but for the
duty cycle value of 0.4 while using a payload size of 50 bytes, as depicted in Figure 34
below.
0
10
20
30
40
50
60
70
80
0.2 0.4 0.6 0.8 1
Per
cen
tage
pac
kets
(%
)
Duty cycle
Comparison of percentage failed packets of STA variants
25 bytes, total percentage of failedpackets, temperature
25 bytes, total percentage of failedpackets, heart rate
50 bytes, total percentage of failedpackets, temperature
50 bytes, total percentage of failedpackets, heart rate
75 bytes, total percentage of failedpackets, temperature
75 bytes, total percentage of failedpackets, heart rate
100 bytes, total percentage offailed packets, temperature
100 bytes, total percentage offailed packets, heart rate
84
Figure 33: Comparison of end-to-end delay per duty cycle between STA variants.
5.1.6.5 ENERGY CONSUMPTION
The energy consumption trend was fairly regular for both the heart rate and body
temperature implementations of the Selective Transmission Algorithm. However, less
energy was consumed by the former relative to the latter across all payload sizes for
duty cycles of 0.8 and 1.0 respectively. However, as duty cycling reduced further, the
STA heart rate variant consumed more energy relative to the body temperature variant
across all payload sizes as portrayed by Figure 35 below.
Figure 34: Comparison of energy consumption per duty cycle per payload between
STA variants.
0
13
26
39
52
0.2 0.4 0.6 0.8 1
End
-to
-en
d d
elay
(m
s)
Duty cycle
Comparison of delay per duty cycle between STA variants
Delay STAtemperature, 25 bytesDelay STA heart rate,25 bytesDelay STAtemperature, 50 bytesDelay STA heart rate,50 bytesDelay STAtemperature, 75 bytesDelay STA heart rate,75 bytesDelay STAtemperature, 100 bytesDelay STA heart rate,100 bytes
0
20
40
60
80
100
120
140
160
0.2 0.4 0.6 0.8 1
Ener
gy (
J)
Duty cycle
Comparison of energy consumption per duty cycle between STA variants
Energy, STA temperature, 25bytesEnergy, STA heart rate, 25bytesEnergy, STA temperature, 50bytesEnergy, STA heart rate, 50bytesEnergy, STA temperature, 75bytesEnergy, STA heart rate, 75bytesEnergy, STA temperature,100 bytesEnergy, STA heart rate, 100bytes
85
5.1.7 STA PERFORMANCE MEASURES I used the bit error rate and signal-to-noise ratio parameters to evaluate performance of
the proposed Selective Transmission Algorithm against Value Reporting which acted as
a control implementation, as well as against the domain standard. Fade depth
distribution gave insight on the simulation environment’s path loss characteristics, and
how they affect experimental results.
5.1.7.1 BIT ERROR RATE (BER)
Amongst the most important criteria to measure performance in digital communications,
bit error rate is the number of bit errors per unit time, computed by dividing the number
of erroneous bits by the total number of transmitted bits of a time-bound digital
communication [114]. A bit error rate in the order of 10-3 is the maximum for medical
applications [115]. A comparison of BER per duty cycle per payload obtained from the
implementation of Value Reporting and both STA variants is observable from Table 9
and Figure 36 below.
Table 9: Comparison of average BER per duty cycle per payload between VR and
STA variants.
Average BER per duty cycle per payload of VR and STA variants
Description Duty cycle
0.2 0.4 0.6 0.8 1.0
BER VR, 25 bytes 4.63941E-05 4.96022E-05 4.60604E-05 5.87016E-05 7.68496E-05
BER STA temperature, 25 bytes 7.64668E-06 8.65865E-06 5.01483E-06 5.81294E-06 1.08365E-05
BER STA heart rate, 25 bytes 8.90179E-06 2.39955E-06 1.26497E-05 1.61174E-05 1.76557E-05
BER VR, 50 bytes 5.66745E-05 4.43803E-05 7.51241E-05 4.64902E-05 5.3264E-05
BER STA temperature, 50 bytes 1.78261E-05 5.50792E-06 1.12471E-05 1.46854E-05 1.67884E-05
BER STA heart rate, 50 bytes 7.95717E-06 1.33967E-05 1.98229E-05 4.06151E-06 2.28936E-05
BER VR, 75 bytes 3.29913E-05 4.62391E-05 3.67063E-05 4.49913E-05 5.49625E-05
BER STA temperature, 75 bytes 9.45148E-06 7.27881E-06 1.12407E-05 4.7518E-06 5.61896E-06
BER STA heart rate, 75 bytes 6.71082E-06 5.3674E-06 1.16441E-05 4.05E-06 7.10701E-06
BER VR, 100 bytes 4.33672E-05 4.59038E-05 6.37085E-05 4.22548E-05 4.3104E-05
BER STA temperature, 100 bytes 9.4675E-06 1.05223E-05 1.45166E-05 9.16627E-06 2.37992E-06
BER STA heart rate, 100 bytes 3.85487E-06 6.21122E-06 2.49179E-06 1.84165E-05 1.56265E-05
86
Figure 35: Comparison of average BER per duty cycle per payload between VR
and STA variants.
When the payload was 25 bytes, the bit error rate tended to increase as duty cycling
increased for the Value Reporting and both Selective Transmission Algorithm
scenarios, though with varying degrees of change per duty cycle as portrayed by Table 9
and Figure 36 above. In addition, the Selective Transmission Algorithm scenario using
body temperature as signal type recorded fewer bit errors compared to its heart rate
variant across duty cycle values, apart from the change in pattern experienced at a duty
cycle of 0.4. An increase in payload size resulted in alternating high and low bit error
rates as duty cycling increased for the VR scenario, though the pattern of reducing BER
could still be noticed. Though both STA scenarios still experienced lower BER as
payload size and duty cycling increased, a new trend was conspicuous when payload
peaked at 100 bytes – the number of bit errors observed with maximum duty cycling
and payload size reduced relative to what was obtained with smaller payload sizes, and
with the Value Reporting scenarios. As observed from Table 9 and Figure 36 above, the
Selective Transmission Algorithm using body temperature and that using heart rate as
signal types swapped positions when both payload size and duty cycling increased. The
former reported fewer bit errors with smaller payload sizes, while the latter reported
fewer packets with erroneous bits when the payload peaked at 100 bytes, as the duty
cycle increased. Overall, both Selective Transmission Algorithm scenarios experienced
0
0.00001
0.00002
0.00003
0.00004
0.00005
0.00006
0.00007
0.00008
0.00009
0.2 0.4 0.6 0.8 1
BER
(n
o. o
f b
its)
Duty cycle
Comparison of BER between VR and STA variants
BER VR, 25 bytes
BER STA temperature,25 bytesBER STA heart rate, 25bytesBER VR, 50 bytes
BER STA temperature,50 bytesBER STA heart rate, 50bytesBER VR, 75 bytes
BER STA temperature,75 bytesBER STA heart rate, 75bytesBER VR, 100 bytes
BER STA temperature,100 bytesBER STA heart rate,100 bytes
87
fewer packets with erroneous bits relative to the Value Reporting scenario, confirming
the edge they have on signal quality. The reduction in BER as duty cycling increased
could be attributed to better channel encoding techniques. The fewer bit errors observed
by both Selective Transmission Algorithm scenarios with the use of big payload sizes
explains the better packet reception rate they reported relative to the Value Reporting
scenario. The fewer erroneous bits reported at the peak of duty cycling and maximum
payload size by the Selective Transmission Algorithm using heart rate relative to the
body temperature variant suggests the state of the transmission medium during both
scenarios, and explains why the former registered a higher percentage of packets
received compared to the latter.
5.1.7.2 SIGNAL-TO-NOISE RATIO (SNR)
Radio-specific and scenario-specific in nature, the Signal-to-noise ratio (SNR)
determines which signal is good enough to be received by the sink. The Signal-to-noise
ratio of the Value Reporting and Selective Transmission Algorithm scenarios were
extracted as portrayed by Table 10 below with confidence intervals ranging from
0.296992 to 0.381947 for the Value Reporting scenario; from 0.456782 to 0.585019 for
the body temperature Selective Transmission Algorithm scenario, and from 0.418275 to
0.560185 for the heart rate Selective Transmission Algorithm scenario. A quick glance
at the SNR graph in Figure 37 below clearly shows the Selective Transmission
Algorithm having an edge on Value Reporting. With a small payload size of 25 bytes,
the heart rate Selective Transmission Algorithm scenario recorded the highest Signal-to-
noise ratio, followed by the Selective Transmission Algorithm variant using body
temperature as signal type, and lastly by the Value Reporting scenario. This trend was
maintained as payload increased. However, when payload peaked at 100 bytes, the body
temperature Selective Transmission Algorithm scenario had a slightly higher SNR
relative to its heart rate variant, probably owing to the increased channel activity
experience by the latter since its frequency was higher than that of the former. For both
the Value Reporting and Selective Transmission Algorithm scenarios, the highest SNR
was recorded at a duty cycle of 0.4, followed by a duty cycle of 0.8. Though this had a
bearing on the trends, it did not directly translate to an equitable packet reception rate as
reported by the respective scenarios when different levels of duty cycling were
implemented. This is because the decision on whether a packet was received or dropped
depended on other factors as well, such as the bit error rate (BER), in addition to the
88
signal-to-noise ratio [116]. The results obtained above align with, and explain the better
packet reception rate, end-to-end delay, and power consumption experienced during the
heart rate Selective Transmission Algorithm scenario, the body temperature Selective
Transmission Algorithm scenario, and the Value Reporting scenario in that order.
Table 10: Comparison of average SNR per payload per duty cycle between VR and
STA variants.
Average SNR per duty cycle per payload of VR and STA variants
Description Duty cycle
0.2 0.4 0.6 0.8 1.0
SNR VR, 25 bytes -81.9166 -81.0116 -81.8144 -82.8289 -83.3801
SNR STA temperature, 25 bytes -75.0790 -72.8660 -73.7870 -75.3533 -76.1897
SNR STA heart rate, 25 bytes -75.3624 -72.2714 -73.9292 -75.5962 -76.1853
SNR VR, 50 bytes -82.1157 -81.3913 -83.4879 -82.1021 -82.3116
SNR STA temperature, 50 bytes -75.1086 -73.8977 -77.3581 -74.2691 -75.0565
SNR STA heart rate, 50 bytes -75.2515 -73.6259 -77.5006 -74.3872 -74.9190
SNR VR, 75 bytes -81.7081 -80.8614 -82.1479 -81.9611 -81.7278
SNR STA temperature, 75 bytes -75.2809 -72.7778 -74.6566 -74.7863 -74.3771
SNR STA heart rate, 75 bytes -74.9239 -72.8239 -75.2038 -74.7960 -74.2565
SNR VR, 100 bytes -81.8848 -81.5835 -82.3782 -80.9479 -81.8832
SNR STA temperature, 100 bytes -74.1256 -74.3916 -74.4925 -73.5396 -74.5353
SNR STA heart rate, 100 bytes -73.8466 -74.3602 -74.5327 -73.4778 -74.5700
89
Figure 36: Comparison of SNR per payload per duty cycle between VR and STA
variants.
5.1.7.3 FADE DEPTH DISTRIBUTION
The density of fade depth distribution was recorded and segregated into buckets of 5 dB
each ranging from -50 dB to 15 dB. The mean values were plotted per payload per duty
cycle as depicted by Figure 38 below, and trends analyzed. It is worth noting that the
fade depth distribution followed the lognormal distribution recommended for Wireless
Body Area Network implementations, and was similar to results obtained from other
channel modeling experiments such as those conducted by Smith and Hanlen [117].
5.1.7.3.1 Payload of 25 bytes
At a payload of 25 bytes, the highest number of packets experienced fading between -5
dBm and 0 dBm for the Value Reporting scenario, and both Selective Transmission
Algorithm scenarios as portrayed by Figure 38 below. About twice as many packets
were transmitted by Value Reporting relative to the Selective Transmission Algorithm
using body temperature as signal type, which in turn transmitted fewer packets than the
Selective Transmission Algorithm using the heart rate signal type. This trend resonated
-86.0000
-84.0000
-82.0000
-80.0000
-78.0000
-76.0000
-74.0000
-72.0000
-70.0000
-68.0000
-66.0000
0.2 0.4 0.6 0.8 1
SNR
(d
B)
Duty cycle
SNR per payload per duty cycle for VR and STA variants
SNR VR, 25 bytes
SNR STA temperature,25 bytes
SNR STA heart rate, 25bytes
SNR VR, 50 bytes
SNR STA temperature,50 bytes
SNR STA heart rate, 50bytes
SNR VR, 75 bytes
SNR STA temperature,75 bytes
SNR STA heart rate, 75bytes
SNR VR, 100 bytes
SNR STA temperature,100 bytes
SNR STA heart rate,100 bytes
90
during deep fades within the 10 dBm and 15dBm fade depth histogram bucket, though
the number of packets transmitted by all three scenarios was fewer.
5.1.7.3.2 Payload of 50 bytes
As the payload size increased to 50 bytes, the pattern of packets transmitted by all three
scenarios was similar to what was obtained at a payload of 25 bytes. However, slightly
more packets were transmitted by the Value Reporting scenario, fewer packets by the
body temperature Selective Transmission Algorithm scenario, and slightly more packets
by the heart rate Selective Transmission Algorithm scenario within the -5 dBm and 0
dBm fade depth bucket. Compared to the 25 bytes payload scenarios, the Value
Reporting scenario transmitted fewer packets, while both Selective Transmission
Algorithm scenarios transmitted slightly many more packets during deep fades within
the 10 dBm and 15 dBm buckets when the payload size was 50 bytes.
5.1.7.3.3 Payload of 75 bytes
A further increase in payload to 75 bytes resulted in the Value Reporting scenario again
transmitting the highest number of packets, followed by the heart rate Selective
Transmission Algorithm variant, and then the body temperature Selective Transmission
Algorithms variant in that order within the -5 dBm to 0 dBm fade depth bucket.
However, all three scenarios transmitted slightly more packets within this bucket, and
slightly fewer packets within the 10 dBm and 15 dBm deep fade bucket.
5.1.7.3.4 Payload of 100 bytes
As payload peaked at 100 bytes, the Value Reporting scenario transmitted yet again the
highest number of packets, slightly higher than was the case when the payload size was
smaller. Though the trend of the proportion of packets transmitted per scenario was
maintained throughout the experiments, both Selective Transmission Algorithm
scenarios transmitted slightly fewer packets at a payload of 100 bytes compared to when
the payload size was 75 bytes. However, all three scenarios transmitted many more
packets than when the payload was lower, within the 10 dBm and 15 dBm deep fade
bucket. It is worth noting that the confidence intervals followed a similar pattern to the
fade depth distribution per fade depth bucket per payload size. Values ranged from
between 0.0198 to 10.2223, with highest values registered within the [-10,-5] dBm, [-
91
5,0] dBm, and [5,10] dBm fade depth buckets, as was the case with fade depth values.
This throws more light on the adverse effect deep fades have on the performance of
WBANs, and acts as a precursor to remedial actions to ensure a SNR of at least 30 dB
in order to keep the BER within the acceptable 10-3 upper limit for medical applications
[115].
Figure 37: Comparison of Fade depth distribution histograms between VR and
STA variants per payload.
5.2 DISCUSSION
5.2.1 CHANNEL DECONGESTION
In line with the STA implementation using body temperature as signal type, channel
decongestion was achieved while using the heart rate signal type by restricting packet
transmission to those of medical relevance only. Using heart rate as signal type resulted
in the generation of, and transmission of many more packets relative to using the body
temperature signal type, hence yielding lower channel decongestion compared to when
the latter was used as signal type. This confirmed stipulations in [17]. The higher
frequency of the heart rate signal led to the generation and transmission of many more
packets with different sensed values within a given timeframe. The STA body
0
5000
10000
15000
20000
25000
30000
35000
Den
sity
Channel gain (dBm)
Fade depth distribution comparison between VR and STA variants
STA HR, Payload=100,dutyCycle=1.0STA Temp, Payload=100,dutyCycle=1.0VR Temp, Payload=100,dutyCycle=1.0STA HR, Payload=75,dutyCycle=1.0STA Temp, Payload=75,dutyCycle=1.0VR Temp, Payload=75,dutyCycle=1.0STA HR, Payload=50,dutyCycle=1.0STA Temp, Payload=50,dutyCycle=1.0VR Temp, Payload=50,dutyCycle=1.0STA HR, Payload=25,dutyCycle=1.0STA Temp, Payload=25,dutyCycle=1.0VR Temp, Payload=25,dutyCycle=1.0
92
temperature and heart rate variants decongested the channel 1.80 and 1.56 times
respectively relative to periodic transmissions; while the Send-on-delta scheme (an
event sensing and transmission concept based on the condition that the signal changes
significantly by an amount, delta) decongested the channel between 1.57 and 5.85 times.
However, though Send-on-delta is just an analytical model, as opposed to the Selective
Transmission Algorithm which has been implemented in Castalia Simulator.
5.2.2 PACKET RECEPTION RATE
The reduction in proportion of packets received as the payload increased could be
attributed to the additional time required to transmit a bigger packet, as well as the
increased demand for, and consequent contention of the wireless transmission medium
owed to an increase in packet size [118], indicated by the increase in confidence
intervals as payload size increased. This is further buttressed by the fact that a
decongestion of the transmission medium by STA resulted in a higher packet reception
rate with bigger payload sizes. For the constant simulation duration of 400 s, the bigger
packet size meant that there were fewer interframe spaces. Once a node gained access to
the channel, it spent more time transmitting than contending since the
txAllPacketsInFreeChannel option was enabled to that effect. The similarity in
proportion of packets received per node for both VR and STA suggest that the
proportion of packets received from each node was determined by its position with
respect to the other nodes. Despite the difference in signal generation frequency
between the two signals – body temperature and heart rate used for the two STA
variants, and despite their transmission of different number of packets to the sink during
simulation, they had similar percentages of packets received which were beyond the 90
% requirement for 95 % of best performing links requirement in WBANs, thanks to
STA’s consistency and optimization of packet reception rate for signals of varying
frequencies. Contrary to expectations of nodes 1 and 3 having the highest number of
packets successfully received due to their proximity to the sink as per the network
topology, node 1 only came second after node 4, and node 5 had the least number of
packets received by the sink. This could be as a result of a busy channel when nodes 1
and 3 intended transmitting their packets, causing the CSMA/CA mechanism to delay
the transmission of their packets in order to prevent collisions on the contended wireless
transmission medium.
93
5.2.3 PACKET LOSS RATE The increase in proportion of failed packets as payload increased suggests the adverse
effect a large packet size has on transmission reliability [119]. At a given transmit
power level, data rate, bandwidth, and level of interference, the observed increase in
number of failed packets as the payload increased could be attributed to lengthier packet
transmission times. This increased the probability of two or more nodes transmitting
simultaneously even with the CSMA/CA mechanism in place, because their random
back-off periods ended at the same time, causing them to transmit concurrently.
Consequently, a collision and packet loss ensues. Alternatively, the lengthy waiting time
new packets experience prior to accessing the transmission medium because a big
packet is currently being transmitted could lead to buffer overflow and packet loss as
well [120]. A large number of failed packets was as a result of their receive power being
below the sensitivity threshold of the sink. This could be as a result of the low
transmission power (-25 dBm) which was chosen so as to minimize radiation and keep
the SAR way below the 1.6 W/Kg limit for wireless transmissions around the human
body, above which tissue damage ensues after exposure for just 15 minutes [36]. The
same explanation could be given for the packets which failed because they registered
more errors than the allowable error threshold (which was set at 0 dBm), though their
interference level did not exceed the noise floor (which was – 100 dBm for the CC240
radio used). The leveling up of, and the reduction in proportion of failed packets as
payload was increased for the STA body temperature and STA heart rate scenarios
respectively relative to the VR scenarios was as a result of the channel decongestion and
hence collision prevention capability of the STA algorithm. The slightly lower
proportion of failed packets for STA heart rate relative to STA body temperature
scenarios could be as a result of better radio coding and packet delivery efficiency as
payload increased [121]. For both STA variants, the percentage of failed packets was
well below the 10 % for 95 % of best performing links requirement for wireless body
area networks [30].
5.2.4 END-TO-END DELAY The increase in average end-to-end delay as payload increased from 25 bytes to 100
bytes was expected, due to the additional time needed to transmit bigger packets which
were created as a result of the bigger payload. This is substantiated by the increase in
94
confidence intervals as payload increased, suggesting more dispersion of end-to-end
delay values to that effect. In addition, the slight reduction of delay across payload sizes
by the Selective Transmission Algorithm relative to Value Reporting is owed to its
channel decongestion and hence collision curbing ability. Moreover, the conspicuous
edge in percentage delay reduction with lower payload sizes by the STA heart rate
scenario is explained by the fact that when the payload is small, it takes a very short
time to transmit a packet, thereby liberating the transmission medium for other nodes to
transmit, reducing the probability of collisions in the process. As the payload increases,
packets occupy the transmission medium for longer durations, increasing the probability
of collisions most especially of packets which were transmitted simultaneously by
different nodes. The lower packet transmission latency recorded by the STA heart rate
variant relative to its body temperature counterpart even though the latter transmitted
fewer packets could be attributed to better coding and adaptation packet transmission
techniques as the frequency of transmission increases [122]. It is worth noting that
simulators do not usually take account of the operating system and layer code execution
delays which might have increased the end-to-end delay obtained from this study [123],
as would be in the physical world if experiments were conducted on human subjects
using real sensors.
5.2.5 ENERGY CONSUMPTION
Both variants (body temperature and heart rate) of the Selective Transmission
Algorithm consumed less energy relative to the Value Reporting Scenario because the
former transmitted fewer packets owing to its ability to reduce the number of packets
transmitted, though maintaining their medical relevance. The CC2420 radios used as
nodes consumed less power when transmitting (29.04 mW) at -25 dBm than when
receiving or listening (62 mW) [80, pp. 60-61]. This meant that less energy should be
consumed with more transmissions when no duty cycling is implemented. However, the
Value Reporting Scenario was characterized by many packet retransmissions which
resulted in more energy consumed. In addition, the superframe which is the basic
information transmission entity in WBANs receives data for transmission as long as it
has enough space to accommodate it, thanks to better packet coding techniques [80, p.
70]. A big packet which fits into a superframe would use about half of the energy two
smaller packets half its size would use if transmitted in two superframes separated by a
95
time equivalent to the interframe space. Energy and network lifetime is thus preserved.
The Selective Transmission Algorithm scenario using heart rate as signal type
transmitted more packets relative to its covariant using the body temperature signal
type. However, the former consumed less energy in the process because in addition to
using up less energy for transmission, the channel decongestion mechanism of the
Selective Transmission Algorithm curbed retransmissions. This also explains the small
reduction in energy consumed by the heart rate STA scenario which is commensurate to
its transmission of more packets (1280 packets) relative to the body temperature
scenario which transmitted a total of 1112 packets.
5.2.6 EFFECT OF DUTY CYCLING ON THE SELECTIVE TRANSMISSION ALGORITHM
CHANNEL DECONGESTION
As expected, reducing the duty cycle curtailed the duration for which the nodes were
active (listening, transmitting, and receiving) per cycle. Though the nodes would wake
up when they have data to transmit, there’s the tendency that they listen to the channel
less due to a reduction in the superframe duration [124], coupled with the time transition
required to change state from sleep to active, leaving them prone to collisions. This
explains why fewer packets were transmitted, and the transmission channel was more
decongested with decreased duty cycling. Increase in the level of activity witnessed on
the wireless medium as duty cycle reduced is buttressed by the increase in confidence
interval in that order. It is worth noting that channel decongestion is a measure of the
number of packets transmitted on the transmission medium within a given timeframe
(400 s in case of these experiments). Higher channel decongestion by itself does not
necessarily imply an improvement in performance, because other factors such as the
packet reception rate, end-to-end delay, power consumption, and level of interference
come into play. The STA body temperature and heart rate variants decongested the
channel between 1.57 and 2.24 times with duty cycling respectively, relative to periodic
transmissions; while the Send-on-delta scheme did same between 1.57 and 5.85 times.
5.2.6.1 PACKET RECEPTION RATE
The drop in percentage packet reception as the duty cycle reduced from 1.0 to 0.8 could
be explained as being caused by an increase in the number of packets on the
96
transmission medium due to retransmissions caused by collisions. Retransmissions were
in turn initiated by a reduction in the timeframe available for nodes to be active and
packets to be transmitted. As duty cycle reduced further to 0.6 and then 0.4, better
channel encoding techniques ensured that more packets were delivered to the sink,
resulting in an increase in the number of packets transmitted [125]. Following an
additional reduction of duty cycle to 0.2, the time for which nodes were active became a
constraint once more, causing the percentage packet reception to drop due to increased
congestion of the transmission medium. This is substantiated by considering just the
number of packets transmitted (void of the number of packets which failed because their
signal strength was below the sensitivity threshold), rather than the number of packets
generated by the application layer of the nodes in calculating the packet reception rate.
In this case, there were less packets on the transmission medium as observed from Table
7 above. Consequently, instead of the percentage packet reception dropping at a duty
cycle of 0.2, it increased all the way from a duty cycle of 0.8 to a duty cycle of 0.2 for
all payload sizes, but for the payload of 100 bytes using the body temperature signal.
The reason for this could be due to the increased packet size, which favored channel
congestion and collisions.
5.2.6.2 PACKET FAILURE RATE
As expected, the pattern of failed packets demonstrates how the number of packets and
their sizes affect packet transmission reliability. For both STA variants, a few packets
failed when both payload size and duty cycling were at their lowest. As duty cycling
increased, more packets were transmitted due to a lengthier superframe duration,
leading to a reduction in packet failure rate. This was soon reversed at a duty cycle of
0.8 since most nodes were active most of the time, increasing the number of packets in
circulation, and hence collisions. However, when both payload size and duty cycling
peaked at 100 bytes and 1.0 respectively, all the nodes were active all the time. Though
there were more packets in circulation, better packet and channel coding techniques
stepped in to ensure the successful transmission of more packets [61], hence the drop in
packet failure rate. Despite better packet and channel encoding techniques, the STA
heart rate variant had more packets in circulation owing to its higher frequency. This
increased the number of collisions, causing it to experience more packet failures relative
to the STA body temperature variant.
97
5.2.6.3 END-TO-END DELAY
In line with expectations, packets of the STA body temperature variant experienced less
end-to-end delay relative to those of the heart rate STA variant when small payload
sizes were used with minimal duty cycling, and when large packets were used with
maximum duty cycling. This was as a result of their lower frequency, which caused
fewer packets to be transmitted during the 400 s of simulation, and hence a lower
probability of packet collisions. There was a decrease in end-to-end delay experienced
by packets of the heart rate STA variant relative to the body temperature variant as duty
cycle increased. This happened despite their higher frequency and hence packets
transmitted. This occurrence could be attributed to better media-independent FEC
encoding techniques, which prompt the channel to introduce redundancy into the
bitstream to enable the receiver detect and correct possible channel errors [126],
preventing packet loss during high traffic conditions. Since the packet sizes were small,
many more packets could conveniently fit into the active portion of the superframe,
increasing the number of packets transmitted per unit time, and hence the average end-
to-end delay. However, as the payload size increased, more time was needed to transmit
the bigger packet size. In addition, the likelihood of deferring the transmission of a
packet to the next superframe because it could not fit into the active portion of the
current superframe due to its larger size, increased [127], and so did end-to-end delay.
5.2.6.4 ENERGY CONSUMPTION
As expected, the STA heart rate variant consumed less energy than the heart rate variant
when high duty cycling of 0.8 and 1.0 was enforced. This is because, owing to the
higher frequency of the heart rate signal relative to the body temperature signal, the
former transmitted many more packets during simulation relative to the latter.
Considering the CC2420 radio used for simulations of this study, less power (29.04 mW
at -25 dBm) and hence energy is used up during transmission than during listening or
packet reception (62 mW) [80, pp. 60-61]. This justifies the lower energy consumed by
the heart rate scenario relative to the body temperature scenario. As duty cycling
reduced, the transmission channel became more and more congested, leading to more
collisions and consequently many more retransmissions, causing the increase in energy
consumed by the heart rate scenario. This increase in congestion was evident from the
increasing number of failed packets because the receiver was not in the receive state,
which constituted a majority of the packets lost as portrayed by Table 8 (a) to (d) above.
98
CHAPTER 6: CONCLUSION AND
FUTURE WORK
6.1 CONCLUSION
My research set out to investigate the merits of an algorithm aimed at optimizing packet
reception, curbing packet failure, lowering end-to-end delay, and reducing energy
consumption in wireless body area networks for medical applications. Furthermore, it
sought the effect payload size and duty cycling has on such an algorithm, in addition to
evaluating its quality using bit-error-rate and signal-to-noise ratio. To this end, I
developed and implemented the channel-decongesting Selective Transmission
Algorithm in Castalia Simulator using the body temperature signal, and the higher
frequency heart rate signal. Employing a mixture of conception, experimental, and
analytical methods, I compared performance of the proposed Selective Transmission
Algorithm with Value Reporting from which it was coined, and doubled as a medium
for benchmarking. Results show that the body temperature STA variant decongested the
transmission channel nearly twice as much, and the heart rate variant did so by more
than a third compared to Value Reporting, respectively. This confirmed assertions made
in [17]. Both STA variants also outperformed Value Reporting in terms of packet
reception rate, packet failure rate, end-to-end delay, and energy consumption, with the
heart rate variant having a slightly better performance. Though payload size had no
effect on channel decongestion, it reduced the packet reception rate by a negligible
average of 0.21 % per 25-byte increase in payload from a payload of 25 bytes, the heart
rate variant having slightly better performance with smaller payload sizes, and the body
temperature variant having slightly better results with bigger payload sizes. This
relationship was antagonistic for packet failure rate. Furthermore, there was a decrease
in end-to-end delay and energy consumption as payload size increased. In addition, an
increase in duty cycling produced a decrease in channel decongestion and end-to-end
delay, a jigsaw-like pattern of packet reception rate, and an increase in power
consumption. Moreover, both STA variants experienced lower BER as both payload
size and duty cycling increased, the body temperature variant performing better with
smaller payload sizes and the heart rate variant performing better with larger payload
99
sizes. Similarly, STA recorded better SNR relative to VR, the heart rate variant having
better results with smaller payloads and the body temperature variant having better
results with bigger payload sizes. As duty cycling increased, all scenarios experienced a
jigsaw-like pattern of SNR, with highest values at a duty cycle of 0.4, followed by a
duty cycle of 0.8. Likewise, fade depth distribution was investigated to abstract its
possible effect on the results obtained. It followed the lognormal trend recommended
for WBANs, represented by Smith and Hanlen in [117]. My study both contributes to,
and challenges extant research: Packet reception rate trends from my experiments
confirmed those in [63] and [121], matched those in [77] with loads in excess of 70
Kbps, but were antagonistic to results from [21]. Packet failure rate results were similar
to those in [21] and [69]; end-to-end delay matched results in [65] and [86], but opposed
those in [54]; energy consumption trends matched those in [26] and [65] but differed
from those in [21] and [108]. A variation of duty cycling in the experiments I conducted
produced similar packet reception rate trends as in [76] despite an interruption at a duty
cycle of 0.8; packet failure rate had similar results with [76] and [7]; end-to-end delay
confirmed results in [7], [76], and [77]. My results produced a similar power
consumption trend as obtained in [8], [75], and [76], though antagonistic to results from
[31]. These results could be of interest to fellow researchers and WBAN equipment
manufacturers. For researchers, the disparity in results highlight how case-specific a
study could be in this domain. Trends in the results obtained can help equipment
manufacturers in the production of tailor-made equipment for specific physiological
signals and functional settings characterized by varied path loss and temporal channel
environments. A possible point of error in these experiments could arise from the
additive collision model used. This model adds up signals from neighboring nodes and
considers them as interference, though alternative collision models would allow the
stronger of the signals to be received. Moreover, recent research has proven that actual
interference is a little lower than the simple addition of the signals of neighboring nodes
[80]. Furthermore, the low transmission power of -25 dBm adopted could reduce the
proportion of received packets. In addition, simulators do not usually take account of
the operating system and layer code execution delays which might have increased the
end-to-end delay obtained from experiments in this study relative to real world
scenarios [123]. Compared to a similar study, the STA body temperature and heart rate
variants decongested the channel by 1.80 and 1.56 times with no duty cycling, and
100
between 1.57 and 2.24 times with duty cycling respectively relative to periodic
transmissions; while the Send-on-delta scheme decongested the channel between 1.57
and 5.85 times. It is however worthy of note that the Send-on-delta concept was only an
analytical model to investigate performance gains of restricting sensed and transmitted
signals to those conforming to a given threshold, meanwhile the Selective Transmission
Algorithm performs a similar function though with multiple thresholds, and has been
implemented in a near-real-world environment provided by Castalia Simulator.
6.2 FUTURE WORK
The focus of this research was a single static wireless body area network. In future, the
intent is to conduct experiments with many more WBANs, in order to evaluate
performance of the Selective Transmission algorithm in high network density and
mobile scenarios, reminiscent of the real world. Additional work envisaged for the
future is, implementing the Selective Transmission Algorithm on physical sensors,
using human subjects.
101
REFERENCES
[1] Y. Wang et al., "WiCop: Engineering WiFi Temporal White-Spaces for Safe
Operations of Wireless Personal Area Networks in Medical Applications," IEEE
Trans. Mobile Comput., vol.13, no.5, pp. 1145-1158, May 2014.
[2] A. Parand. (2011, Nov. 10). Value reporting [Online]. Available:
https://groups.google.com/forum/#!topic/castalia-simulator/. [Accessed: Jun. 24,
2014].
[3] IEEE Standard for Information technology– Telecommunications and
information exchange between systems- Local and metropolitan area networks-
Specific requirements-, IEEE Std 802.15.4-2006.
[4] Y. O. Mohammed and U. A. Baroudi, "Partially observable Markov decision
processes (POMDPs) and wireless body area networks (WBAN): A survey,"
KSII Trans. Internet and Inf. Syst., vol. 7, no. 5, pp. 1036-1057, May 2013.
[5] M. Holland et al., “Optimizing physical-layer parameters for wireless sensor
networks,” ACM Trans. Sensor Netw. (TOSN), vol. 7, no. 28, p. 28, Feb. 2011.
[6] M. S. Dawood et al., "A review on energy efficient modulation and coding
techniques for clustered wireless sensor networks," Int. J. Adv. Res. Comput.
Eng. Tech., vol. 2, pp. 319-321, Feb. 2013.
[7] F. Despaux et al., "Measurement-based analysis of the effect of duty cycle in
IEEE 802.15.4 MAC performance," in 10th Int. Conf. Mobile Ad-Hoc and
Sensor Systems, 2013 © IEEE. doi: 10.1109/MASS.2013.102
[8] R. R. Rout and S. K. Ghosh, “Enhancement of Lifetime using Duty Cycle and
Network Coding in Wireless Sensor Networks,” IEEE Trans. Wireless
Commun., vol. 12, no. 2, pp. 656-667, Feb. 2013.
[9] M. Li et al., “Throughput optimization with fairness consideration for coexisting
WBANs,” in 2015 IEEE Int. Conf. Communications (ICC), London, 2015, pp.
6418-6423.
[10] A. Muthulakshmi and K. Shyamala, “Queuing based emergency handling in
WBAN,” Int. J. Eng. Technol., vol. 7, no. 6, pp. 2231-2239, Jan. 2016.
[11] S. T. Shreyas et al., “Data Authentication in Wireless Body Area Network
(WBAN) Using a Biometric-Based Security,” Int. J. Res. Emerg. Sci. Technol.,
vol. 2, no. 1, pp. 136-142, Mar. 2015.
[12] X. Zhou et al., “Energy Efficient Optimization by Resource Allocation in
Wireless Body Area Networks,” in 79th Vehicular Technology Conf. (VTC
Spring), 2014 © IEEE. doi: 10.1109/VTCSpring.2014.7022907.
102
[13] J. Zhou et al., “A reliable medium access mechanism based on priorities for
wireless body sensor networks,” in 35th Annual Int. Conf. IEEE Engineering in
Medicine and Biology Society (EMBC), Osaka, 2013, pp. 1855-1858.
[14] B. Liu et al., “MAC protocol in wireless body area networks for E-health:
challenges and a context-aware design,” IEEE Wireless Commun., vol. 20, no. 4,
pp. 64-72, Aug. 2013.
[15] H. Karvonen et al., “A cross-layer energy efficiency optimization model for
WBAN using IR-UWB transceivers,” Telecommun. Syst., vol. 58, no. 2, pp. 165-
177, Feb. 2015.
[16] C. Kalra et al., “Lifetime and Throughput Optimization in Wireless Body Area
Network,” Int. J. Sci. Res. & Develop., vol. 3, no. 12, pp. 59-60, 2016.
[17] M. Miskowicz, “Send-on-delta concept: An event-based data reporting strategy,”
Sensors, vol. 6, pp. 49-63, 2006.
[18] A. Moschitta and I. Neri, “Power consumption assessment in wireless sensor
networks,” in ICT energy concepts towards zero power information and
communication technology, Intech, 2014, ch. 9, pp. 203-224.
[19] J. Lunze and D. Lehmann, “A state-feedback approach to event-based control,”
Automatica, vol. 46, no. 1, pp. 211-215, Jan. 2010.
[20] L. Filipe et al., “Wireless Body Area Networks for Healthcare Applications:
Protocol Stack Review,” Int. J. Distrib. Sens. N., vol. 2015, 23 pages, 2015.
[21] R. Cavallari et al., “A Survey on Wireless Body Area Networks: Technologies
and Design Challenges,” IEEE Commun. Surveys Tuts., vol. 16, No. 3, p. 1639,
2014.
[22] S. B. Nji et al., “A survey of major inter-user interference mitigation techniques
in wireless body area networks,” in Proc. 19th International Conf.
Transformative Science and Engineering, Business and Social Innovation (SDPS
2014), Kuching, Malaysia, 2014, pp. 149-155.
[23] A. Singh et al., “Body Sensor Network: A Modern Survey & Performance Study
in Medical Perspect,” Netw. and Compl. Syst., vol. 3, no. 1, pp. 12-17, 2013.
[24] N. Ullah et al., “A Very Low Power MAC (VLPM) Protocol for Wireless Body
Area Networks,” Sensors, vol. 11, no. 4, pp. 3717-3737, Mar. 2011.
[25] P. T. Sharavanan et al., “A Comparative Study of Cross Layer Protocols in
WBAN,” Aust. J. Basic and Appl. Sci., vol. 9, no. 16, pp. 294-300, May 2015.
[26] H. Karl and A. Willig, “Single-node architecture,” in Protocols and
architectures for wireless sensor networks, Chichester, England: Wiley, 2005,
ch. 2, sec. 2.1, pp. 18-32.
103
[27] P. Chauhan and T. Kumar, “Power optimization in Wireless Sensor Networks: A
Perspective,” Int. J. Eng. and Tech. Res., vol. 3, no. 5, May 2015.
[28] W. Guo et al., “Impacts of 2.4-GHz ISM band interference on IEEE 802.15.4
wireless sensor network reliability in buildings,” IEEE Trans. Instrum. Meas.,
vol. 61, pp. 2533-2544, Sept. 2012.
[29] A. Monica and J. Sharma, “Maximizing throughput of Cluster-Based WBAN
with IEEE 802.15.6 CSMA/CA,” Int. J. Multim. and Ubiq. Eng., vol. 9, no. 5,
pp. 391-402, 2014.
[30] S. Movassaghi et al., "Wireless body area networks: A survey," in IEEE
Commun. Surveys Tuts., vol. 16, no. 3, p. 5, 2014.
[31] A. Rahim et al., "A comprehensive survey of MAC protocols for wireless body
area networks," in 7th International Conf. Broadband, Wireless Computing,
Communication and Applications (BWCCA), Victoria, BC, 2012, pp. 434-439.
[32] K. M. S. Thotahewa et al., “MAC Protocols for UWB-Based WBAN
Applications,” in Ultra Wideband Wireless Body Area Networks, 1st ed. Basel,
Switzerland: Springer, 2014, ch. 2, pp. 19-36.
[33] S. M. R. A. Masud, “QoS Taxonomy towards Wireless Body Area Network
Solutions,” Int. J. Application In. Eng. & Manage., vol. 2, no. 4, pp. 221-234,
Apr. 2013.
[34] S. Saleem et al., “On the Security Issues in Wireless Body Area Networks,” Int.
J. Digital Content Tech. and its Appl., vol. 3, no. 3, pp. 178-184, Sept. 2009.
[35] M. Koohestani et al., “A Novel, Low-Profile, Vertically-Polarized UWB
Antenna for WBAN,” IEEE T. Antenn. Propag., vol. 62, no. 4, pp. 1888-1894,
Apr. 2014.
[36] A. K. M. F. Hoque et al., “A study on specific absorption rate (SAR) due to non-
ionizing radiation from wireless/telecommunication in Bangladesh,” Am. J.
Phys. Appl., vol. 1, no. 3, pp. 104-110, Jan. 2013.
[37] Y. Liao et al., “Analysis of in-to-out wireless body area network systems:
Towards QoS-Aware health Internet of things applications,” Electronics, vol. 5,
no. 38, pp. 1-26, Jul. 2016.
[38] S. B. Nji et al., "Wireless body area networks channel decongestion algorithm,"
in 9th International Conf. on IT in Asia (CITA), Kota Samarahan, Malaysia,
2015, pp. 1-6.
[39] G. K. Ragesh and K. Baskaran, “An overview of applications, standards and
challenges in futuristic wireless body area networks,” Int. J. Comput. Sci. Issues,
vol. 9, no.2, pp. 180-186, Jan. 2012.
104
[40] H. Thaier et al., “A survey of wireless technologies coexistence in WBAN:
Analysis and open research issues,” Wirel. Netw., vol. 20, no. 8, pp. 2165-2199,
Nov. 2014.
[41] IEEE Standard for Information technology-Telecommunications and
information exchange between systems-Local and metropolitan area networks-
Specific requirements, IEEE Standard 802.15.4-2006, 2006.
[42] L. Hughes et al., “A review of protocol implementations and energy efficient
cross-layer design for wireless body area networks,” Sensors, vol. 12, pp. 14730-
14773, Nov. 2012.
[43] N. Salman et al., "Overview of the IEEE 802.15.4 standards family for Low Rate
Wireless Personal Area Networks," 7th Int. Symp. Wireless Communication
Systems (ISWCS), York, 2010, pp. 701-705.
[44] J. P. Carmo and J. H. Correia, “Wireless Instrumentation and Biomedical
Applications,” in Telemedicine and Electronic Medicine, H. Eren and J. G.
Webster, Eds. Boca Raton, FL: CRC Press, 2015, pp. 143-147.
[45] A. Sharma et al., “Efficient IEEE 802.15.4 physical layer system using spatial
diversity,” Int. J. Eng. Trends and Tech., vol. 19, no. 1, pp. 35-38, Jan. 2015.
[46] M.S. Obaidat et al., Eds., “IEEE 802.15.4 Based Wireless Sensor Network,” in
Handbook of Green Information and Communication Systems. Waltham, MA:
Academic Press, 2012, pp. 104-108.
[47] G. Shi and K. Li, “Fundamentals of ZigBee and WIFI,” in Signal Interference in
WIFI and ZigBee Networks, 1st ed. Cham, Switzerland: Springer, 2017, ch.2,
sec. 2.1.2, pp. 11-18.
[48] S. Nepal et al., “Does the IEEE 802.15.4 MAC protocol work well in wireless
body area networks?,” J. Adv. Comput. Netw., vol. 4, no. 1, pp. 52-57, Mar.
2016.
[49] M. M. Chandane et al., “Performance analysis of IEEE 802.15.4,” Int. J.
Comput. Appl., vol. 40, no. 5, pp. 23-29, Feb. 2012.
[50] F. Xia et al., “Service Differentiated and Adaptive CSMA/CA over IEEE
802.15.4 for Cyber-Physical Systems,” The Sci. World J., vol. 2013, no. 947808,
12 pages, Aug. 2013, doi: 10.1155/2013/947808.
[51] V. P. Rao and D. Marandin, “Adaptive channel access mechanism for Zigbee
(IEEE 802.15.4),” J. Commun. Softw. and Syst., vol. 2, no. 4, pp. 283-293, Dec.
2006.
[52] A. Segun et al., “A survey of medium access control protocols in wireless sensor
network,” Int. J. Comput. Appl., vol. 116, no. 22, pp. 1-7, Apr. 2015.
105
[53] A. Koubaa et al., "A comprehensive simulation study of slotted CSMA/CA for
IEEE 802.15.4 wireless sensor networks," in IEEE Int. Workshop Factory
Communication Systems, 2006 © IEEE. doi: 10.1109/WFCS.2006.1704149.
[54] U. Pešović et al., "Effect of hidden nodes in IEEE 802.15.4/ZigBee wireless
sensor networks," in 17th Telecommunications Forum, Serbia, 2009, pp. 161-
164.
[55] G. Yingying et al., “The P-persistent CSMA protocol with monitoring function
in WSN and the Analysis of the Protocol,” Sensors & Transducers, vol. 160, no.
12, pp. 209-214, Dec. 2013.
[56] S. Sharma et al., “Performance evolution of GTS based wireless sensor networks
under CSMA/CA with variable clear channel assessment exponent,” Int. J. Sci.
Research & Development, vol. 3, no. 11, pp. 182-185, 2016.
[57] V. Singh and R. Sharma, “Performance analysis of MAC protocols for WBAN
on varying transmitted output power of nodes,” Int. J. Comput. Appl., vol. 67,
no. 7, pp. 32-34, Apr. 2013.
[58] P. Park et al., “A generalized Markov chain model for effective analysis of
slotted IEEE 802.15.4,” IEEE 6th Int. Conf. Mobile Adhoc and Sensor Systems,
Macau, 2009, pp. 130-139.
[59] M. Z. Zamalloa and B. Krishnamachari, “An analysis of unreliable and
asymmetry in low-power wireless links,” ACM Trans. Sensor Netw., vol. 3, no.
2, pp. 34 pages, Jun. 2007.
[60] G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination
Function”, IEEE J. on Selected Areas in Communications, vol. 18, no. 3, pp. 55-
547, Sept. 2006.
[61] X. Liang and I. Balasingham, “Performance analysis of the IEEE 802.15.4 based
ECG monitoring network,” in Proc. 7th seventh IASTED Int. Conf. Wireless and
Optical Communications, Montreal, QC, 2007, pp. 99-104.
[62] C. T. Kone et al., “Performance management of IEEE 802.15.4 wireless sensor
network for precision agriculture,” IEEE Sensors. J., vol. 15, no. 10, pp. 5734-
5747, Oct. 2015.
[63] M. H. Zayani et al., “A joint model for IEEE 802.15.4 physical and medium
access layers,” in Proc. 7th Int. Wireless Communications and Mobile
Computing Conf. (IWCMC), 2011 © IEEE.
doi: 10.1109/IWCMC.2011.5982651
[64] V. Bijalwan et al., “Analysis and Design of Joint PHY-MAC Model of IEEE
802.15.4,” International Journal of Science, Engineering and Technology
Research, vol. 2, no. 9, pp. 1706-1713, Sept. 2013.
106
[65] P. Park et al., “Adaptive IEEE 802.15.4 protocol for energy efficient, reliable
and timely communications,” in Proc. 9th ACM/IEEE Int. Conf. information
processing in sensor networks, 2010 © ACM. doi: 10.1145/1791212.1791251
[66] A. Dâmaso et al., “Evaluating the Power Consumption of Wireless Sensor
Network Applications Using Models,” Sensors, vol. 13, pp. 3473-3500, Mar.
2013.
[67] F. Qin et al., “Effective-SNR estimation for wireless sensor network using
Kalman filter,” Ad Hoc Netw., vol. 11, pp. 944-958, Nov. 2012.
[68] S. Omer et al., “An LQI based dual-channel routing protocol for wireless body
area networks,” in Int. Telecommunication Networks and Applications conf.
(ITNAC), 2015 © IEEE. doi: 10.1109/ATNAC.2015.7366833
[69] M. Goyal et al., “Evaluating the impact of Signal to Noise Ratio on IEEE
802.15.4 PHY-Level Packet Loss Rate,” in 13th Int. Conf. Network-Based
Information Systems, 2010 © IEEE Computer Society. doi:
10.1109/NBiS.2010.97
[70] Rachita et al., “Simulative investigations of wireless body area network through
varied channel conditions,” Int. J., Comput. Appl., vol. 74, no. 7, pp. 6-10, Jul.
2013.
[71] S. Choudhary and D. K. Dhaka, “Path loss prediction models for wireless
communication channels and its comparative analysis,” Int. J. Eng. Management
& Sciences, vol. 2, no. 3, pp. 38-43, Mar. 2015.
[72] S. M. Rakshit, “HTNMote: A platform for onboard real-time monitoring of
railcars,” in Technological breakthroughs in modern wireless sensor
applications, 1st ed. Hershey, PA: IGI Global, 2015, ch. 1, pp. 19-20.
[73] W. Pak et al., “Duty cycle allocation to maximize network lifetime of wireless
sensor networks with delay constraints,” Wirel. Commun. Mob. Comput., vol.
14, pp. 613-628, 2014.
[74] J. Bhar, “A Mac Protocol Implementation for Wireless Sensor Network,” J.
Comput. Netw. and Commun., vol. 2015, no. 697153, 12 pages, Mar. 2015.
[75] P. Park et al., “Duty-Cycle optimization for IEEE 802.15.4 Wireless Sensor
Networks,” ACM T. Sensor Network, vol. 10, no. 1, pp. 12:1-12:32, Nov. 2013.
[76] G. Sharma et al., “Impact of BO and SO on beacon-enabled IEEE 802.15.4,” Int.
J. Comput. Appl. Vol. 66, no. 18, pp. 34-38, Mar. 2013.
[77] M. Petrova et al., “Performance Study of IEEE 802.15.4 Using Measurements
and Simulations,” in IEEE Wireless Communications and Networking Conf.
(WCNC 2006), Las Vegas, NV, 2006, pp. 487-492.
107
[78] J. Charan and T. Biswas, “How to calculate sample size for different study
designs in medical research?,” Indian J. Psychol. Med. vol. 35, no. 2, pp. 121-
126, Apr. 2013.
[79] N. Hirpara et al., “Interpreting research findings with confidence interval,” J.
Orthodontics & Endodontics, vol. 1, no. 1:8, pp. 1-4, 2015.
[80] A. Boulis. (2011, Mar.). Castalia: A simulator for Wireless Sensor Networks and
Body Area Networks. NICTA. Sydney, Australia [Online]. Available:
https://github.com/boulis/Castalia/blob/master/Castalia%20
-%20User%20Manual.docx
[81] G. Kelly, “Body temperature variability (part 1): A review of the history of body
temperature and its variability due to site selection, biological rhythms, fitness
and aging,” Altern. Med. Rev., vol. 11, no. 4, pp. 278-293, Dec. 2006.
[82] J. Tousseau, “Normal temperature and the periodic fever syndromes,” Systemic
autoinflammatory disease (SAID) support, 2016. [Online]. Available:
http://saidsupport.org/normal-body-temperature-periodic-fever-syndromes/
[Accessed: Feb. 3, 2017].
[83] E. J. Walter et al., “The pathophysiological basis and consequences of fever,”
Crit. Care, vol. 20, no. 1, pp. 1-10, Jul. 2016.
[84] HealthLink BC, “Body temperature,” J. Intensive Care, vol. 2, no. 1, 14 pages,
Feb. 2014.
[85] V. D. Hooper and J. O. Andrews, “Accuracy of noninvasive core temperature
measurement in acutely ill adults: The stat of the science,” Biol. Res. Nurs., vol.
8, no. 1, pp. 24-34, Jul. 2006.
[86] J. Y. Khan and M. R. Yuce, “Wireless body area network (WBAN) for medical
applications,” in New developments in biomedical engineering, D. Campolo, Ed.
Rijeka, Croatia: InTech, 2010, p. 598.
[87] H. Tanaka et al., “Age-predicted maximal heart rate revisited,” J. Am. Coll.
Cardiol., vol. 37, no. 1, pp. 153-156, Jan. 2001.
[88] IEEE 802.15.4 Stack User Guide, ver. 2.4, NXP Semiconductors, East Kilbride,
SL, UK, pp. 31-33, June 2015.
[89] Texas Instruments, “CC2420 Single-Chip 2.4 GHz IEEE 802.15.4 Compliant
and ZigBee Ready RF Transceiver,” CC2420 Errata Notes, 2015.
[90] R. Naima and J. Canny, “Wireless body area network implementation for
ambulatory health monitoring,” in Wireless Body Area Networks: Technology,
Implementation, and Applications, M. R. Yuce and J. Khan, Eds. Boca Raton:
CRC Press, 2011, p. 217.
108
[91] R. Lacuesta et al., “Spontaneous Ad Hoc cloud computing network,” The
Scientific World Journal, vol. 2014, no. 232419, 19 pages, Aug. 2014.
[92] A. Pandya and M. Mehta, “Performance evaluation of Multipath Ring Routing
protocol for wireless sensor network,” Proc. Of the Int. Conf. on Advance in
Computing, Electron. and Elect. Eng., 2012 © Universal Association of
Computer and Electronics Engineers. doi: 10.3850/978-981-07-1847-3 P0924
[93] E. Lea͠o et al., “CT-SIM: A simulation model for wide-scale cluster-tree
networks based on the IEEE 802.15.4 and ZigBee standards”, Int. J. Distrib.
Sens. N., vol. 13, no. 3, pp. 1-17, Feb. 2017.
[94] L. M. Camarinha-Matos, “Scientific research methodologies and techniques:
Unit 2: Scientific method,” PhD thesis, Elect. and Comput. Eng., Instituto de
Desenvolvimento de Novas Tecnologias, Caparica, Setúbal, 2012.
[95] O. Chughtai et al., “Congestion Detection and Alleviation in Multihop Wireless
Sensor Networks,” Wirel. Commun. Mob. Com., vol. 2017, no. 9243019, 13
pages, Feb. 2017.
[96] B. Sharma and T. C. Aseri, “A Comparative Analysis of Reliable and
Congestion-Aware Transport Layer Protocols for Wireless Sensor Networks,”
Int. Scholarly Res. Netw., vol. 2012, no. 104057, 14 pages, Nov. 2012.
[97] J. Korhonen and Y. Wang, “Effect of packet size on loss rate and delay in
wireless links,” IEEE Wireless Commun. and Networking Conf., vol. 3, pp.
1608-1613, 2005.
[98] H. Liu and Y. Liu, “Distributed Congestion Mitigation and Medium Access in
Duty Cycling Wireless Sensor Networks,” Int. J. Distrib. Sens. Netw., vol. 2014,
no. 748264, 11 pages, Aug. 2014.
[99] V. Nithya et al., “BER evaluation of IEEE 802.15.4 compliant wireless sensor
networks under various fading channels,” Wireless Pers. Commun., vol. 2014,
no. 77, pp. 3105-3124, Mar. 2014.
[100] L. T. Carr, “The strengths and weaknesses of quantitative and qualitative
research: what method for nursing?,” J. Adv. Nurs., vol. 1994, no. 20, pp. 716-
721, Jan. 1994.
[101] L. Isserlis, “On the value of a mean as calculated from a sample,” J. R. Stat.
SOC. vol. 81, no. 1, pp. 75-81, Jan. 1918.
[102] F. Lateef, “Simulation-based learning: Just like the real thing,” J. Emerg.
Trauma Shock, vol. 3, no. 4, pp. 348-352, Dec. 2010.
[103] X. Cai et al., “Energy-aware adaptive topology adjustment in wireless body area
networks,” Telecommun. Syst., vol. 58, no. 2, pp. 139-152, Feb. 2015.
109
[104] T. Alam, “Specific absorption rate analysis of broadband mobile antenna with
negative index metamaterial,” Appl. Phys. A., vol. 122, no. 170, 6 pages, Feb.
2016.
[105] R. Serra and M. Nabi, “Wireless coexistence and interference test method for
low-power wireless sensor networks,” IET Sci. Meas. Technol., vol. 9, no. 5, pp.
563-569, Apr. 2015.
[106] Y. Xu, “Minimize end-to-end delay through cross-layer optimization in multi-
hop wireless sensor networks,” M.S. thesis, Dept. Comput. Sci., Missouri S&T,
Rolla, 2010.
[107] A. Boulis. (2012, Nov. 22). Average Delay Definition [Online]. Available:
https://groups.google.com/forum/
[108] S. Nepal, “A fast channel assignment scheme for emergency handling in
wireless body area networks,” Sensors, vol. 17, no. 477, pp. 1-16, Feb. 2017.
[109] M. A. Ameen, “A power efficient MAC protocol for wireless body area
networks,” EURASIP J. Wirel. Comm., vol. 2012, no. 33, pp. 1-17, Feb. 2012.
[110] K. Ahmed and M. A. Gregory, “Wireless sensor network data centric storage
routing using Castalia,” in Australasian Telecommunications Networks and
Application Conf. (ATNAC), Brisbane, QLD, 2012, pp. 1-8.
[111] Erik Aguirre et al., “Design and performance analysis of wireless body area
networks in complex indoor e-health hospital environments for patient remote
monitoring,” Int. J. Distrib. Sens. N., vol. 12, no. 9, 17 pages, Aug. 2016.
[112] W. Q. Malik et al., “Impact of bandwidth on small-scale fade depth,” in Proc.
Global Telecommun. Conf. (GLOBECOM ’07), 2007 © IEEE Communications
Society. doi: 10.1109/GLOCOM.2007.729
[113] A. K. Gangwar and F. Chishti, “Study of electromagnetic field and its effect on
human body,” Int. J. Adv. Res. Elect. Electron. and Instr. Eng., vol. 3, no. 6, pp.
10156-10161, Jun. 2014.
[114] J. Dhiman et al., “Determination and analysis of bit error rate using theoretical
semi-analytic and Monte-Carlo simulation method,” International Journal of
Advanced Research in Computer Science and Software Engineering, vol. 3, no.
2, pp. 45-51, Feb. 2013.
[115] B. Yu, “Wireless body area networks for healthcare: A feasibility study,”
University of Florida, unpublished.
[116] N. Pustchi and T. Korkmaz, “Improving packet reception rate for mobile sinks in
wireless sensor networks,” in IEEE Int. Symp. on a World of Wireless, Mobile
and Multimedia Networks (WoWMoM), San Francisco, CA, 2012, pp. 1-9.
110
[117] D. B. Smith and L. W. Hanlen, “Channel Modeling for Wireless Body Area
Networks,” in Ultra-Low-Power Short-Range Radios, 1st ed. P. P. Mercier and
A. P. Chandrakasan, Eds. Cham, Switzerland: Springer, 2015, ch. 2, pp. 25-55.
[118] Y. Zhang and F. Shu, “Packet size optimization for goodput and energy
efficiency enhancement in slotted IEEE 802.15.4 networks,” in IEEE Wireless
Communications and Networking Conf., Budapest, 2009, pp. 1-6.
[119] K. Singh et al., “Impact of packet size on the performance of IEEE 802.15.4 for
wireless sensor network,” International Journal of Advanced Technology in
Engineering and Science, vol. 3, no. 1, pp. 78-85, Jan. 2015.
[120] Z. Dahham et al., “An efficient backoff algorithm for IEEE 802.15.4 wireless
sensor networks,” Wireless Pers. Commun. vol. 75, no. 4, pp. 2073-2088, Oct.
2013.
[121] I. Salhi et al., “CoZi: basic coding for better bandwidth utilization in ZigBee
sensor networks,” in IEEE GLOBECOM’ 2010, Miami, 2010, pp. 1-6.
[122] D. Zeng et al., “The exploration of network coding in IEEE 802.15.4 networks,”
Int. J. Digit. Multim. Broadcast., vol. 2011, no. 310647, pp. 1-9, Apr. 2011.
[123] A. K. Dwivedi and O.P. Vyas, “An Exploratory Study of Experimental Tools for
Wireless Sensor Networks,” Wirel. Sens. Netw., vol. 3, pp. 215-240, Jun. 2011.
[124] M. N. Hassan et al., “Traffic differentiation and dynamic duty cycle adaptation
in IEEE 802.15.4 beacon enabled WSN for real-time applications,”
Telecommun. Syst. vol. 62, no. 2, pp. 303-317, Jun. 2016.
[125] W. Du et al., “Performance evaluation of IEEE 802.15.4 sensor networks in
industrial applications,” Int. J. Commun. Syst. vol. 28, no. 10, pp. 1657-1674,
Feb. 2014.
[126] A. M. Peinado and J. C. Segura, “Review of channel coding techniques,” in
Speech recognition over digital channels: Robustness and standards, Chichester,
England: Wiley, 2006, ch. c, sec. c.1, pp. 229-236.
[127] J. Misic and V. Misic, Eds., “Congestion of packets deferred to the next
superframe,” in Wireless personal area networks: Performance, interconnection,
and security with IEEE 802.15.4, Chichester, England: Wiley, 2008, ch. 5, sec.
5.1, pp. 95-97.
111
APPENDIX
APPENDIX A: DETAILED SLOTTED CSMA/CA FLOWCHART
Source: [129].
Where: RT = Retransmission stages
r = Maximum value of retransmission stages
m = Maximum number of back-off periods (NB)
Wi = Back-off counters
W0 = Initial back-off counter value (0).
112
APPENDIX B: SLOTTED AND UNSLOTTED CSMA/CA FLOWCHART
Source: http://biometric-
badge.googlecode.com/svn/trunk/Fonti%20presentazione/immagini%20presentazione/
113
APPENDIX C: POSITION AND RANGE OF WBANs WITH RESPECT TO OTHER WIRELESS NETWORKS
Source: http://image.slidesharecdn.com/testingwirelessmedicaldevices-gregkiemel-5-8-
2015-151229194509/95/testing-wireless-medical-devices-8-638.jpg?cb=1451418583
114
APPENDIX D: DATA RATES AND POWER CONSUMPTION OF WBAN WITH RESPECT TO OTHER 802.15 VARIANTS.
Source: http://www.ni.com/cms/images/devzone/tut/Untitled_20121003144458.jpg
Source: [133].
115
LIST OF PUBLICATIONS
PUBLISHED CONFERENCE PAPERS
Soh Brendard Nji, Mujahid Tabassum, Dr. Chua Hong Siang, “A Survey of Major Inter-
User Interference Mitigation Techniques in Wireless Body Area Networks,” 19th
International Conference on Transformative Research in Science and Engineering,
Business and Social Innovation (SDPS 2014), Kuching, Malaysia, 2014, pp. 149-155.
doi: 10.13140/2.1.2593.6642
S. Brendard, M. Tabassum and HongSiang Chua, "Wireless Body Area Networks
channel decongestion algorithm," 9th International Conference on IT in Asia (CITA
2015), Kota Samarahan, 2015, pp. 1-6.
doi: 10.1109/CITA.2015.7349831
S. Brendard, “A lonesome journey in a crowd of peers: Case of a telecommunication
Researcher,” 4th Borneo Research Education Conference (BREC 2016), Kota
Samarahan, 2016, pp. 345-351.
eISBN 978-967-0828-10-7
JOURNAL ARTICLES AWAITING PUBLICATION
Soh Brendard Nji, Mujahid Tabassum and Hong Siang Chua, “Performance
Enhancement of Wireless Body Area Networks using the Selective Transmission
Algorithm,” unpublished.
Soh Brendard Nji, Mujahid Tabassum and Hong Siang Chua, “Payload variability:
Effect on Wireless Body Area Network Performance using a Realistic Transmission
Medium,” unpublished.
Soh Brendard Nji, Mujahid Tabassum and Hong Siang Chua, “The Impact of Duty
Cycling on Wireless Body Area Network Implementation for Medical Applications,”
unpublished.
