Course Project or Survey • Mainly to give you choices and options… • The objective of this course is to introduce students to general WSN research without too much emphasis on a single specific approach for MAC, routing, in-network data processing, security, etc. – We want to avoid developing skewed views • Introduce yourself to WSN research by doing a project or survey. In terms of methodology, any CS research would take a similar path. Learn the technique and apply it to your area of research!
Transcript
Course Project or Survey
• Mainly to give you choices and options…
• The objective of this course is to introduce students to general WSN research without too much emphasis on a single specific approach for MAC, routing, in-network data processing, security, etc.– We want to avoid developing skewed views
• Introduce yourself to WSN research by doing a project or survey. In terms of methodology, any CS research would take a similar path. Learn the technique and apply it to your area of research!
Most CS research works in a similar way
1. Find a problem interesting to you
2. Identify key existing approaches. Some key papers are covered in class. Also, you can use, for example, the citation count provided by Google scholar.
3. Find pros and cons of existing methods by doing a project or literature survey. (In both cases, you need to think hard!)
4. Develop a new approach to alleviate a common problem of previously developed approaches – This is optional. Significant extra credit will be given, if
your work has quality submittable to a conference or journal
Possible topics for a project
• For example, do one of the following:– Experimentally compare 2 - 3 MAC protocols
• For example, do a simulation study in OMNeT++ or actually implement them in MoteLab
• If you work individually, implement and evaluate 2 protocols
• Implement and evaluate 3 protocols if you work in a team
Logistics
• Submit a printed 1-page project proposal at the beginning of the class on March 4 (If you are an Enginet student, email it to me)
• In your 1-page proposal, – Specify you plan to do a project or survey– Specify protocols you choose to evaluate or a
topic for a survey– Reason why you choose those protocols and
topic– Briefly describe how to evaluate them if you
choose to do a project
Course Schedule
• In-class student presentation of the proposal– March 11– Each team will give a 10 - 15 minute
presentation followed by a short Q&A session: Present a little more detail than the 1 page proposal
• Paper presentations by students– Each individual student is required to
present a paper after midterm exam in April
– You can present papers related to your project or survey
– Just email me your slides if you are an Enginet student
• Midterm exam– Mid April
• Project demo due 5pm May 7• Project report submission due
11:59pm May 14• Survey submission due 11:59pm May
7
Understanding Packet Delivery Performance in Dense Wireless Sensor
NetworksJerry Zhao & Ramesh
GovindanSenSys ‘03
Motivation
• WSNs can be deployed in harsh environment
• Measure packet delivery performance– Spatio-temporal charasteristics of packet
loss– Environmental dependence– Medium scale (up to 60 Mica motes)
indoor, habitat with moderate foliage, and open parking lot -> Implications for the design & evaluation of routing & MAC protocols
Why packet delivery performance is important?
• Determines energy efficiency & network lifetime
• Poor packet delivery may degrade application performance & consume a lot of energy
• Important for evaluating communication protocols
• Experimentally verify WSN design principles, for example, low-power RF transceivers for multiple short hops– More energy efficient than a single hop over a
long range– Spatial multiplexing
Backgrounds: Some Wireless Communication Vagaries
• Hidden node problem: Node A transmits to B, Node C cannot hear it and transmits to B -> Collision at B
A B C
Backgrounds: Some Wireless Communication Vagaries
• Exposed node problem: Node B is transmitting to A, Node C has a packet intended for node D -> C cannot transmit, although it’s OK
A B C D
Backgrounds: Some Wireless Communication Vagaries
• Multipath problem – A radio signal is reflceted by obstacles– Parts of the signal may take different
paths to the sink, confusing the receiver
Source: Wireless Lan, Multipath and Diversity http://www.cisco.com/en/US/tech/tk722/tk809/technologies_tech_note09186a008019f646.shtml
• Signal attenuation– Attenuation = (10/L) log10 (Pi/Po) where L
is the distance, e.g., meter or km– dB/m or dB/km– Signal strength drops exponentially
• Signal strength is proportional to 1/ra where r is the distance and 2 ≤ a ≤ 5
Packet delivery performance
• Physical layer– If there’s no interfering transmission, delivery
perf is largely determined by a function of environment, physical layer coding scheme, individual receiver
• MAC layer– Interfering transmissions contribute to poor
perf.– Evaluate the efficacy of carrier sense and link
layer retransmission
Contributions
• Experiments & observations – No new protocols or algorithms– Lack of the related work on delivery
performance measurement in a medium scale WSNs (when the paper was published)
– Although the results do not necessarily mean radio communications in WSNs are always like this, they provide important insights
Key Results
• Heavy-tailed distributions of packet losses– For example, in an indoor setting, half of the
links experience more than 10% packet loss, and a third suffer more than 30% loss
– Physical layer: Gray area within the communication range
• Receivers suffer choppy packet reception• In some case, gray area is 1/3 of the comm. range
– MAC layer: Packet loss is heavy-tailed• 50% - 80% comm. energy is wasted to overcome
packet collisions & environmental effects• About 10% of links exhibit asymmetric packet loss
Authors suggest
• Topology control, via actual measurement of actual perf, needs to carefully discard poorly performing links or neighbors to whom asymmetric links exist– Packet level mechanisms, e.g., RTS/CTS,
are not enough– Make decisions at the granularity of
links to neighbors
I. Packet delivery at the physical layer
• Disable TinyOS MAC to measure pure packet delivery at physical layer
• Vary three factors– Environments– Physical layer coding schemes– Transmit power settings
Environment 1• I: Indoor office building
– 2m * 40m hallway– 60 motes placed in a line
• 0.5m apart• 0.25m apart near the edge of
the comm range
– Harsh due to significant multipath reflection effects
– Generally, indoor wireless comm is much harder than outdoor
Environment 2
• H: 150m * 150m segment of a state park
• Downhill slope with foliage and rocks– Multi-path
problems due to foliage & rocks
Environment 3
• O: 150m * 150m open parking lot– No obstacles– Multipath only due to ground reflections– Not much to sense
Physical layer encoding scheme
• SECDED (Single Error Correction and Double Error Detection)– TinyOS default– Convert each byte into 24 bits– Can detect 2 bit errors & correct one bit error
• Manchester encoding– Convert a byte into 16 bits– Detect an error out of 2 bits
• 4-bit/6-bit scheme (4bsb)– Encode one byte into 12 bits– Detect 1 bit error out of 6 bits
Discrete control of transmit power in a mote
• Three settings are considered– High (potentiometer 0)– Medium (potentiometer 50)– Low (potentiometer 90)
• Potentiometer is an electric device with user-adjustable resistance
Aggregate packet delivery performance
• Packet loss with 4b6b coding, high Tx power -> Worst packet delivery perf.
I
H
O
Aggregate packet delivery performance
• Packet loss vs Tx power in I, 4b6b coding – Observe lower power improves dilivery perf considerably
possibly due to the reduced comm range and multi-path problems
H
ML
Aggregate packet delivery performance
• Pkt loss vs coding schemes in I, high Tx Power– SECDED is much better for the cost of consuming more
bandwidth than 4BSB and Manchester– Not much difference btwn 4BSB and Manchester
Spatial Characteristics of Packet Delivery
• How does reception rate vary with distance from the transmitter?– Gray area due to multipath problems
Spatial profile of packet delivery: 4B6B, High Tx Power
I O H
Why servere multipath problem?
• No frequency diversity– Motes use a single, narrow frequency
band– More recent sensor nodes use multiple
channel– ZigBee uses direct sequence spread
spectrum• Use broader band than data• Add pseudo random white noise
Lessons
• Selecting a shortest path simply based on the geographic distance or hop count is not sufficient!
• Nodes need to carefully select neighbors based on the measured packet delivery perf!
Signal strength & packet delivery
• Try to answer a question: “Can signal strength by itself estimate link quality?”
• Unfortunately, the answer is “NO”
High Tx Power, I
Coding Schemes• “Can sophisticated physical layer coding schemes mask the
gray area?”• Not necessarily, SECDED has the lowest effective bandwidth -
> Topology control to avoid pathological links in the gray area together with bandwith efficient coding scheme
Spatial Correlation• “Are two receivers in their linear topology likely to see
similar loss patterns?”• Significantly different correlation characteristics for
different environments: I & O show noticeably higher correlated packet loss than H
• At the physical layer, independent losses are a reasonable assumption
I O H
Temporal characteristics of packet delivery
• Large variations in average reception rate and big standard deviations imply time varying packet losses
II. Packet Delivery at the Medium Access Layer
• TinyOS– CSMA/CA: Random back off upon carrier
sense– Link layer ACK: Send 4 byte ACK to the
sender– Authors added retransmission scheme
• When there’s no ACK, retransmit up to 3 times
Packet loss distribution under the Retransmission Scheme
• Too many packet loss• 50% - 80% communication energy is wasted on
repairing lost transmissions• Better MAC, e.g., S-MAC, B-MAC, Z-MAC, is
required
Asymmetry in packet delivery
• Asymmetry in wireless communication is well known, but the extent is not
• Topology control should control pathological links
Conclusions
• Performed experiments to understand packet delivery perf in dense sensor network deployments
• Quantify the prevalence of gray area• Mostly “observations”
– “Causes” for phenomena are not for sure• Most of them are conjectures, guesses, etc.
partly confirmed by experiments• Still an open issue – No clear mathematical
