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Wireless Sensor Networks for High Fidelity Sampling
Sukun KimQualifying Examination
Dec 1, 2005
High Fidelity Sampling Three classes of WSN applications
Monitoring environments Great duck island [11], Redwood forest [12] Focus on low-duty cycle and low power consumption
Monitoring objects – High Fidelity Sampling machine health monitoring [13], condition-based
monitoring, earthquake monitoring [14], structural health monitoring [15]
Focus on fidelity (quality) of sample Interactions with space and objects
Lighting control [16] Focus on control
Structural Health Monitoring
High Fidelity Data High Frequency Sampling
with Low Jitter Time Synchronized
Sampling Large-scale Multi-hop
Network Reliable Command
Dissemination Reliable Data Collection
FTSP [8]
Mint [9]
Drip [10]
Challenges
Reliable Data Collection- Problem Statement Every data from every node needs be
collected to PC over a multi-hop network without loss High throughput Small number of packet injections to network Overcome interference
Assumptions Powerful receiver, resource constrained sender Receiver (PC) can arbitrate flow
Low congestion Low loss rate
Hypothesis (Proposed Solution) High Fidelity Data – Low-cost low-power
MEMS accelerometer board with proper signal processing and calibration, produces data of meaningful fidelity
High Frequency Sampling with Low Jitter – WSN mote and TinyOS with guaranteed worst-case jitter, provide real time operation of meaningful level
Reliable Data Collection – Rate-based alternating-flow protocol with complex receiver and simple sender and pipelining, achieve reliable collection efficiently
Table of Contents Related Work
High Fidelity Data
High Frequency Sampling with Low Jitter
Reliable Data Collection
Future Work
Structural Health Monitoring System
Spencer, et al [5]
Lynch, et al [6]
Wisden [7]
High Fidelity Data 2.0μG/√Hz *
0.5mG/√Hz
?
High Frequency Sampling with Low Jitter
? 977Hz 160Hz
Time Synchronized Sampling ? <1μs no
Large-scale Multi-hop Network ? no yes
Reliable Command Dissemination ? yes N/A
Reliable Data Collection ?B/s ?B/s 250B/s
Preliminary customized systems: Kruger, et al [1],Qiang, et al [2], Engel, et al [3], Caicedo, et al [4]
TCP on Wireless Networks Blind link-level-retransmission (LLR) can
decrease throughput – DeSimone, et al [17]
Support for mobile host I-TCP, Balakrishnan, et al [18]
Support for wireless ad-hoc network – WTCP, ATP Rate-based transmission Selective ACK contains congestion information No sender timeout for retransmission – WTCP
Reliable Transfer on WSN Reliable diffusion
PSFQ, RMST, Garuda, Drip, Deluge Congestion Control
ESRT, CODA, Fusion, Ee, et al [19] Better best-effort convergence
RBC Reliable convergence
Wisden Sender sends data at static rate In a routing tree, mote sends NACK to get missing packet
from child for efficiency PC sends NACK to source mote for e2e reliability Incorrectly tuned rate and topology change make the
network collapse Compared to hardware, low bandwidth
Table of Contents Related Work
High Fidelity Data In IWSHM ‘05
High Frequency Sampling with Low Jitter
Reliable Data Collection
Future Work
Accelerometer Board
Signal processing: averaging in software Calibration for manufacturing variation and
temperature System noise floor: 30(μG/√Hz) Gives desired quality in static, dynamic test
Silicon Designs 1221LADXL 202E
Two accelerometers for two axis
Thermometer, 16bit ADC, Low-pass filter
Table of Contents Related Work
High Fidelity Data
High Frequency Sampling with Low Jitter In IWSHM ‘05
Reliable Data Collection
Future Work
Analysis of JitterSampling
Other jobs like EEPROM write
Non-preemptible portion Preemptible portion
Probability
Jitter
0 C W T1+CT2+C
P1/T1
P2/T2
1. Remove unnecessary blocking atomic section, interrupts Turn off unnecessary components
2. Verify maximum blocking section is small enough
Time
450 460 470 480 490 500 510 520 530 540 550-1
0
1
2
3
4
5
6
7
8
9
10Interval: 150ms
Sample
Jitt
er (
us)
Verification of Jitter (6.67KHz)
0μs
10μs
-1 0 1 2 3 4 5 6 7 8 9 100
100
200
300
400
500
600
700
800
900
1000Interval: 150ms
Jitter (us)
Sam
ple
0μs 10μs
Jitter is within 10µs (6.67%), 0.2% at 200Hz Tradeoff: turning off radio WSN mote and TinyOS are not inherently limited in
real time operation It is a matter of the hardness of real time requirement and
the tradeoff for the loss of functionality
Time Series Histogram
Table of Contents Related Work
High Fidelity Data
High Frequency Sampling with Low Jitter
Reliable Data Collection Straw
Future Work
Overview
Application
PC Mote
Application
Straw Straw
Multi-hop Routing
PC application arbitrates flow Determines who sends when Triggers one flow at a time
Adjust RTT, adjust transmission rate to avoid interference Cross-layer information
read(dest, *start, size)
Routing layer is assumed to deliver packets end-to-end
ProtocolPC Mote
Straw Straw
Selective NACK No need for flow control, rate-based transmission No congestion control Pipelining, no link level retransmission
Alternating flow, no concurrent bidirectional flow
Complex Simple1. Data Request2. Data Transfer
3. Request missing holes
4. Transfer missing holesSelective NACK
Rate = if (Depth < Interference Radius)then (UART Delay) + Depth * (Radio Delay)else (Interference Radius) * (Radio Delay)
Optimization
Read
Send
Transfer the checksum of the whole data to guarantee the integrity
Parallelize reading from the memory and sending to the network
MemoryNetwork
95.6%560B/s96.6%
576B/s
91.4%296B/s
91.8%304B/s
93.2%299B/s
* End-to-end Raw Reliability Effective Bandwidth (Byte/s)
10KB of data 500 packets
Mica2dot, 36 bytes/pkt Comparison to routing layer
630B/s for 1 hop Up to 91.4% efficiency
352B/s for 2 hops Up to 86.4% efficiency
Test Result
Channel Capacity Utilization Hardware capacity limit
UART: 57.6Kbps Radio: 19.2Kbps 1 hop: 14.4Kbps
Measured actual capacity usage UART: 27.8Kbps Radio: 9.74Kbps Routing: 5.46Kbps (1 hop) Reliable: 4.7Kbps (1 hop)
Mica2, 36bytes/pkt33%
Mica2, 36bytes/pkt
Packet Time (for 1bit)
8%
25%
5%
14%13%
0%
21%
5%9% UART Channel
Radio ChannelUART OverheadRadio OverheadGenericComm HeaderRouting OverheadRouting HeaderReliable OverheadReliable Header
43% header transfer & overhead
212.7μs
33%data transfer
24%overhead for transferring data
Effect of Packet Size on Bandwidth Doubled packet size: 36B 72B Payload: 20B 56B (2.8 times) Packets/sec: 29.4 20.9 (71%) Bandwidth doubled: 588B/s 1172B/s*
(1.99 times)
RAM usage jump from 3437B to 4733B for SHM application (Sentri) 36 packet buffers Basic services (Comm + TimeSync + Routing +
Bcast + Reliable) can go beyond 4KB of RAM
*Loss rate was 0.2%
RAM
GenericComm
QueuedSend
Routing Cmpnt1DripBcast
Cmpnt2
Cmpnt5
Cmpnt3Cmpnt4 Straw
Cmpnt6
Forward Forward
Forward
Forward
1. At least oneat each endComponent(12 out of 36)
2. Forwarding Queue(20 out of 36)
Sharing packet buffer?
Reliability of system versus Efficient use of resource
Why so much RAM (packet buffer)?
Sensornet Network Layer
Reliable Data Collection- Problem Statement Every data from every node needs be
collected to PC over a multi-hop network without loss High throughput Small number of packet injections to network Overcome interference
Assumptions Powerful receiver, resource constrained sender Receiver (PC) can arbitrate flow
Low congestion Low loss rateIF
Revisited
Destination
Source
5 hops, 26.28% link loss rate (78.23% E2E),300 packets, separated by 1 sec, on BVR
Testbed In SECON ‘04
Reliability
0
0.2
0.4
0.6
0.8
1
1.2
none 1 2 3 4 5 5+RFMaximum number of retransmission
Suc
cess
Rat
e
0 1 2
3 4 5
6 7 8
8 original messages
Transmission Stretch
0
0.5
1
1.5
2
2.5
3
3.5
4
none 1 2 3 4 5 5+RFMaximum number of retransmission
Num
ber
of P
acke
ts
0 1 23 4 56 7 8
8 original messages
OverheadEnd-to-end retransmission (Straw): 8.6% at 96.6%(1hop) success rate 13.6% at 91.8%(2hops) success rateErasure code: 12.5%, 25%, …
Table of Contents Related Work
High Fidelity Data
High Frequency Sampling with Low Jitter
Reliable Data Collection
Future Work
Link Level Retransmission + Pipelining Link level retransmission is effective when
loss rate is high
Pipelining is effective for long path
Combining two can intensify interference- higher correlated losses
Throughput = (e2e success rate) * (pkts/s at sender)
Congestion Control In case Straw is used together with
constant upstream traffic, congestion control will be needed
Congestion control from the receiver Include congestion information in NACK packet Sender adjusts rate using congestion
information
Using Sentri (structural health monitoring toolkit)Berkeley SF Bay
mid-spanquarter-span
59
Base Station
260ft
16ft27 1
1310 38 4
121114
L3L5 L1L4 L2
Deployment at Footbridge
V9 V7
V13 V4
V2
Berkeley SF Bay
0 1 2 3 4 5 6 7 8 9 10
-8
-6
-4
-2
0
2
4
6
8
Time (sec)
Acc
eler
atio
n (
mg)
Time plot, vertical sensors at L1-L5
V2
V4
V13V7
V9
Plots of calibrated data
0 2 4 6 8 10 12 14 16 18 20
10-2
100
102
104
Frequency (Hz)
abs(
FF
T(.
))Frequency plot, vertical sensors at L1-L5
V2
V4
V13
V7
V9
-0.99
0.19-0.73
1.000.74
Model Properties
First Vertical Modeof Vibration
Match with SAP bridge model
Timeline Mar 2006 for SenSys – Deployment on the
Golden Gate Bridge April 2006 – Study correlation between
(link level retransmission + pipelining) and interference
Jul 2006 – Implement link level retransmission + pipelining
Dec 2006 – Congestion control with rate adjustment
Questions and Discussions
Backup Slides
References[1] M. Kruger and C. U. Grosse. Structural health monitoring with wireless sensor
networks. Otto-Graf-Journal, 15:77–90, 2004.[2] P. Qiang, G. Xun, and Z. Chang-you. A wireless structural health monitoring system in
civil engineering. The Third International Conference on Earthquake Engineering (3ICEE), Nanjing, China, October 18-20, 2004.
[3] J. M. Engel, L. Zhao, Z. Fan, J. Chen, and C. Liu. Smart brick - a low cost, modular wireless sensor for civil structure monitoring. International Conference on Computing, Communications and Control Technologies (CCCT 2004), Austin, TX USA, August 14-17, 2004.
[4] J. M. Caicedo, J. Marulanda, P. Thomson, and S. J. Dyke. Monitoring of bridges to detect changes in structural health. the Proceedings of the 2001 American Control Conference, Arlington, Virginia, June 2527, 2001.
[5] B. S. Jr., M. Ruiz-Sandoval, and N. Kurata. Smart sensing technology: Opportunities and challenges. Journal of Structural Control and Health Monitoring, in press, 2004.
[6] J. P. Lynch. Overview of wireless sensors for real-time health monitoring of civil structures. Proceedings of the 4th International Workshop on Structural Control (4th IWSC), New York City, NY, USA, June 10-11, 2004.
[7] N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. the Proceedings of the ACM Conference on Embedded Networked Sensor Systems, November 2004.
References (continued)[8] A. DeSimone, M. C. Chuah, O. Yue, Throughput performance of transport-layer
protocols over wireless LANs. In Proceedings of IEEE Globecom 93, Houston, USA, 1993.
[9] A. Bakre, B. R. Badrinath, I-TCP: indirect TCP for mobile hosts, Proceedings of the 15th International Conference on Distributed Computing Systems (ICDCS'95).
[10] H. Balakrishnan, S. Seshan, and R. H. Katz, Improving reliable transport and handoff performance in cellular wireless networks. ACM Wireless Networks, December 1995.
Reliable Data Collection- Problem Statement Every data from every node needs be
collected to PC over a multi-hop network without loss in a way that gives high throughput with small number of packet injections
The collection must overcome interference with the flow in the same and the opposite direction
Reliable Data Collection- Problem Statement Every data from every node needs be
collected to PC over a multi-hop network without loss in a way that gives high throughput with small number of packet injections
The collection must overcome interference with the flow in the same and the opposite direction
Data ACK or NACK
Accelerometer Board Design
ADXL 202E Silicon Designs 1221L
Range -2G ~ 2G -0.1G ~ 0.1G
System noise floor 200(μG/√Hz) 30(μG/√Hz)
Price $10 $150
Two accelerometers for two axis
Thermometer 16bit ADC
Silicon DesignsADXL
Signal Processing As an analog signal processing low-pass
filter is used, which filters high frequency noise Low-pass filter with threshold frequency 25Hz
is used As a digital signal processing, averaging is
used If noise follows Gaussian distribution, by
averaging N numbers, noise decreases by a factor of sqrt(N)
Sensor Calibration
Temperature Calibration
Temperature
Acceleration
27.3
3.9
11.7
19.5
81.1
67.1
53.0
39.0
0
F C
mG
27.5
-27.5
Thanks to Crossbow
Power ConsumptionOperation Mode Consumption (mW)
Board Only 240.3
Idle 358.2
One LED On 383.4
Erasing Flash 672.3
Sampling 358.2
Transferring Data 388.8
3 of Tadiran 5930 (lithium-ion, 3.6V, 19Ah, $17, D size) are used
Power Consumption (cont) With optimal sleeping, 30 days
Board itself consumes significant amount of energy
Power source
Sensor
ADC
Mote
Switch
Power source
Sensor ADC
Mote
Switch
Verification of Jitter – Time Series (1KHz, 5KHz, 6.67KHz)
450 460 470 480 490 500 510 520 530 540 550-1
0
1
2
3
4
5
6
7
8
9
10Interval: 1000ms
Sample
Jitt
er (
us)
450 460 470 480 490 500 510 520 530 540 550-1
0
1
2
3
4
5
6
7
8
9
10Interval: 200ms
Sample
Jitt
er (
us)
450 460 470 480 490 500 510 520 530 540 550-1
0
1
2
3
4
5
6
7
8
9
10Interval: 150ms
Sample
Jitt
er (
us)
Peak to Peak is time to fill up buffer
Spiky portion is time to write buffer to flash
Can sample as long as the former is larger than the latter
0μs
10μs
Verification of Jitter – Histogram (1KHz, 5KHz, 6.67KHz)
-1 0 1 2 3 4 5 6 7 8 9 100
100
200
300
400
500
600
700
800
900
1000Interval: 1000ms
Jitter (us)
Sam
ple
-1 0 1 2 3 4 5 6 7 8 9 100
100
200
300
400
500
600
700
800
900
1000Interval: 200ms
Jitter (us)
Sam
ple
-1 0 1 2 3 4 5 6 7 8 9 100
100
200
300
400
500
600
700
800
900
1000Interval: 150ms
Jitter (us)
Sam
ple
Jitter is within 10µs Peak at 625ns – Wakeup
time from sleep mode
0μs 10μs
Real-time System Use separate MCU for sensor board, or two
motes
Sensor MCU
Buffer
MCU
Radio
Constraints and Options
Sources of Failure• Link Failure• Mote Failure• …
How to obtain reliability? (possible options)1. Add redundancy of information
1. Retransmission – link-level, end-to-end2. Data redundancy – duplication, erasure code3. Path redundancy – Use thick path
2. Increase success rate1. Alternative Route2. Congestion Control
(# of pkts received) = Psuccess × (# of pkts sent)
Problem Statement Goal
Reliable communication in multi-hop Wireless Sensor Networks
Assumption Wireless communication Resource-constrained mote
Erasure CodeEncoding Channel Decoding
M8 msgs
N
21 code words
N’≥8 code words
M
8 original msgs
Systematic Code
Benefit: if receiver has codes containing original messages• Encoding, Decoding are faster• Even if receiver get less than 8 packets, we don’t lose
every message
Systematic Code
Encoding one code word takes 1.7msDecoding time varies from 0 to 27msReal time processing is possible
In MICA2
Alternative Route Discovery
Find Alternative Route
What if
Get 6 best candidates for the next hop from routing table. And try from the best
And if
Effect of Erasure Code
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Raw Loss Rate
Fin
al L
oss
Rat
e
12481664247
8 original messages
Effect of Systematic Code
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
Raw Loss Rate
Fin
al L
oss
Rat
e
12481664247
8 original messages
Encoding Speed
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10
Number of Redundant Codes
Tim
e (
ms)
Decoding Speed
0
5
10
15
20
25
30
0 2 4 6 8 10
Number of Non-Original Message Code
Tim
e (
ms)
Decoding Time versus Loss Rate
0
5
10
15
20
25
30
0 0.2 0.4 0.6 0.8 1
Loss Rate
Tim
e (
ms)
Histogram of Decoding Time
0
0.1
0.2
0.3
0.4
0.5
0.6
11 12 13 14 15 16
Decoding Time (ms)
Fre
quen
cy
Design Space – Routing Layers
Point-to-point Convergence Divergence
Implementation on Beacon Vector RoutingHowever, solutions are applicable to all routing layers
Overhead versus Reliability (zoom)
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
1 2 3 4Overhead
Rel
iab
ility
123455+RF
8
7
6
87
4
3
2
1
1
23
0
4 51
0
8 original messages
Overhead versus Reliability
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4Overhead
Rel
iabi
lity
0123455+RF
8
02
1 2 30 4 5
10
1
3
8
0
8 original messages
Given a threshold reliability requirement, what is the retransmission and redundancy combination that has the smallest overhead?
Decomposing causes of failures (5 ret, no RF)
Findings Link-level retransmission – efficiently
handle transient link failure Route fix – cure stale routing table
problem, increase usefulness of erasure code
Erasure code – relieve the burden of last a few packets, which is very expensive
Some options addresses some problems efficiently, but not all failures Combining options would provide a sweet spot
Run Length Encoding (RLE) 94720, 94704, 94715, 94708 becomes 947
+ 20, 04, 15, 08 Exception
94720, 94704, 92345, 94708 becomes 947 + 20, 04, \92345, 08
Run simulation on footbridge vibration data
Fragment Size: 4
Threshold: 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
5
40
3200
20
40
60
80
100
120
140
160
Threshold
Fragment Size
Compression Ratio
High ResolutionFootbridge data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
5
40
3200
20
40
60
80
100
120
140
Threshold
Fragment Size
Compression Ratio
Low ResolutionFootbridge data
AnalysisHigh Resolution Low Resolution
RLE 66% 45%
gzip * 68% 49%
Theory 56.25%(9 dynamic bits)
37.5%(6 dynamic bits)
Algorithm of RLE fits better to sensor data Basic algorithm of gzip utilizes repetition of same pattern
Compression ratio is sensitive to parameters (even go above 100%) Selecting RLE parameter (either statically or dynamically)
is critical
* Windows zip showed 0.64% increase
Analysis (continued) There exists room for lossless or lossy
compression
Compression ratio is sensitive to parameters (even go above 100%)
Selecting RLE parameter (either statically or dynamically) is critical
33789679201Similar
CompressRandom garbage Drop
State Diagram of Sender
More
Start
Request / Set Timer
Yes / Read
Timer Fired /Send
No / Stop Timer
Simple (intelligence in receiver) Interface is simple
read(start, size, *buffer)
Send everything once, and fill holes
Read depth in routing tree, and adjust transmission rate, packet interval, RTT estimate
State Diagram of ReceiverStart
More
Send Network-Info Request
Receive / Send Transfer Request, Set Timer
Receive & Last || Timeout / count = 0
Timeout / FAIL
Receive & not Last / Set Timer
Yes / Send ReadRequest, Set Timer
Receive / count = 0
More in Round
count < threshold
Yes
No
Yes /++count
No /FAIL
No / SUCCESS
Timeout
Mica2, 36bytes/pkt
Routing
75%
80%
85%
90%
95%
100%
28 29 30 31Interval (ms)
Su
cce
ss R
ate
Channel Capacity Utilization Hardware capacity limit
UART: 57.6Kbps = 200pkts/s Radio: 19.2Kbps = 66.7pkts/s 1 hop: 14.4Kbps = 50pkts/s
Measured actual capacity usage UART: 27.8Kbps = 120pkts/s Radio: 9.74Kbps = 42pkts/s Routing: 5.46Kbps = 31pkts/s (1 hop) Reliable: 4.7Kbps = 29.4pkts/s (1 hop)
Mica2, 36bytes/pkt
Mica2, 36bytes/pkt
Packets per second (Out of 50pkts/s)
14%
24%
0%
3%
59%
UARTRadioRoutingReliableCapacity
Mica2, 36bytes/pkt
Effect of header is considered here
Bandwidth (Out of 14400bps)
31%
19%12%
5%
33%UARTRadioRoutingReliableCapacity
For Mica2, packet size = 36 bytes
Top, Left: Packet SizeBottom, Right: pkts/sec
0 10 20 30 40
CapacityReliableRoutingGenericComm
0 20 40 60
CapacityReliableRoutingRadioUART
RAM space From 3437 to 4733 (SHM application – Sentri)
36Bytes RAM increase per 1Byte increase in packet size Reason – Packet buffer space
4 below GenericComm 3 in TimeSync 16 in Routing 4 in Bcast 2 + 5 in Reliable 2 in application
Basic services (Comm + TimeSync + Routing + Bcast + Reliable) can go beyond 4KB RAM with packet size = 72Bytes
Findings Reliable Data Collection (Straw)
5.2% decrease in packet throughput 13.8% decrease in bandwidth
Packet is small compared to the size of header, so doubling packet size doubles bandwidth RAM limit due to many packet buffers –
Reliability of system versus Efficient use of resource
Loss Rate at FootbridgeLoss Rate versus Distance
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
0 10 20 30 40 50 60 70 80 90Distance (ft)
Loss
Rat
e
Loss Rate
Mica2, 36bytes/pkt
Loss Rate at Golden Gate Bridge
Loss Rate vs Distance
0
0.2
0.4
0.6
0.8
1
1.2
0 50 100 150 200Distance (ft)
Loss
Ra
te
0.5ft3ft
Mica2, 36bytes/pkt
V9 V7
V13 V4
V2
Berkeley SF Bay
0 50 100 150 200 250-15
-10
-5
0
5
10
15
Time (sec)
Acc
eler
atio
n (
mg)
Time plot, vertical sensors at L1-L5
V2
V4
V13V7
V9
0 1 2 3 4 5 6 7 8 9 10
-8
-6
-4
-2
0
2
4
6
8
Time (sec)
Acc
eler
atio
n (
mg)
Time plot, vertical sensors at L1-L5
V2
V4
V13V7
V9
50 51 52 53 54 55 56 57 58 59 60-15
-10
-5
0
5
10
15
Time (sec)
Acc
eler
atio
n (
mg)
Time plot, vertical sensors at L1-L5
V2
V4
V13V7
V9
Time-plots of calibrated data
0 2 4 6 8 10 12 14 16 18 200
1
2
3
4
5
6
7
8
x 104
Frequency (Hz)
abs(
FF
T(.
))
Frequency plot, vertical sensors at L1-L5
V2
V4
V13V7
V9
0 2 4 6 8 10 12 14 16 18 20
10-2
100
102
104
Frequency (Hz)
abs(
FF
T(.
))Frequency plot, vertical sensors at L1-L5
V2
V4
V13
V7
V9
Frequency-plots of calibrated data
0.940.330.681.00
0.41
Frequency: 1.78 HzDamping Ratio: 1%
Second Vertical Mode of Vibration
Custody Transfer Efficient for unreliable media Benefit from small packet header Pipelining gets complicated How to do custody transfer without
interfering pipelining?
Low-power Reliable Transfer Lessons from NEST FE
Duty cycle affects retransmission timeout – timeout should consider duty cycle
Aggressive transfer depletes battery – mote stop responding, causes transfer failure
Power information from lower layer are very useful for proper and efficient operation Power stack – duty cycle info, warning for low
energy budget