Wireless Sensor Networks for High Fidelity Sampling Sukun Kim Electrical Engineering and Computer...

Wireless Sensor Networks for High Fidelity Sampling

Sukun KimElectrical Engineering and Computer Sciences

University of California at Berkeley

Committee: David Culler, Ion Stoica, and Gregory Fenves

Dissertation TalkMay 14, 2007

High Fidelity Sampling Three categories 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]

Broadcast [10]

Challenges

Table of Contents Introduction Flush: A Reliable Bulk Transport Protocol

Algorithm Implementation Evaluation Discussion Related Work Conclusion

Deployment at the Golden Gate Bridge Data from the Golden Gate Bridge Conclusion

With Rodrigo Fonseca, Prabal Dutta, Arsalan Tavakoli, David Culler, Philip Levis, Scott Shenker and Ion Stoica

Overview Target applications

Where transfer completion time is more important than latency of each data point

Structural health monitoring, volcanic activity monitoring, bulk data collection

One flow at a time Reasonable restriction for target applications Remove inter-path interference Easier to optimize for intra-path interference

Algorithm Receiver-initiated Selective-NACK Rate Control Separation of Concerns

Correctness (all packets are delivered) Performance (achieve high bandwidth)

Reliability

2 4 5

4 9

2, 4, 5

4, 9

4, 9

Rate Control: Conceptual Model

Rate:

Assuming disk modelN: Number of nodes, I: Interference range

Rate Control

d8 = δ8 + H7

= δ8 + δ7 + f7

= δ8 + δ7 + δ6 + δ5

1. At each node, Flush attempts to send as fast as possible without causing interference at the next hop along the flow

2. A node’s sending rate cannot exceed the sending rate of its successor

8 7 6 5

8 6 5

4

4

3

Interference Range > Reception Range

However,

Signal Strength

Noise Floor Noise Floor+ SNR Threshold

Noise Floor+ 2 X SNR Threshold

SNR Threshold – minimum SNR to decode a packet

Jammer – a node which can conflict with the transmission, but cannot be heard

Jammer Vulnerable to Jammer No problem to Jammer

Identifying the Interference Set

0

0.2

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0.6

0.8

1

0 5 10 15 20 25 30

Fra

ctio

n of

Nod

es

Difference Between Received Signal Strength and Noise Floor (dBm)

Fract

ion o

f N

odes

CDF of the difference between the received signal strength from a predecessor and the local noise floor

A large fraction of interferers are detectable and avoidable

Implementation RSSI is measured by snooping Information is also snooped

δ, f, D are put into packet header, and exchanged through snooping

δ, f, D take 1 byte each, 3 bytes total Cutoff

A node i considers a successor node (i− j) an interferer of node i+1 if, for any j > 1, rssi(i+1) − rssi(i−j) < 10 dBm

The threshold of 10 dBm was chosen after empirically evaluating a range of values

Implementation 16-deep Rate-limited Queue

Enforces departure delay D(i) When a node becomes congested (depth 5), it

doubles the delay advertised to its descendants

But continues to drain its own queue with the smaller delay until it is no longer congested

Packet Throughput of Different Fixed Rates

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45

0 1 2 3 4 5 6

Effe

ctiv

e T

hrou

ghpu

t (pk

t/s)

Hops from Sink

Fixed 10msFixed 20msFixed 40ms

Eff

ect

ive T

hro

ughput

(pkt

/s)

Packet throughput of fixed rate streams over different hop counts

The optimal fixed rate depends on the distance from the sink

Packet Throughput of Flush

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60

0 1 2 3 4 5 6

Effe

ctiv

e T

hrou

ghpu

t (pk

t/s)

Hops from Sink

FlushBest Fixed Rate

Eff

ect

ive T

hro

ughput

(pkt

/s)

Effective packet throughput of Flush compared to the best fixed rate at each hop

Flush tracks the best fixed packet rate

Bandwidth of Flush

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0 1 2 3 4 5 6

Effe

ctiv

e B

andw

idth

(B

/s)

Hops from Sink

FlushBest Fixed Rate

Eff

ect

ive B

and

wid

th

(B/s

)

Effective bandwidth of Flush compared to the best fixed rate at each hop

Flush’s protocol overhead reduces the effective data rate

Fraction of Data Transferred in Different Phases

• Fraction of data transferred from the 6th hop during the transfer phase and acknowledgment phase• Greedy best-effort routing is unreliable, and exhibits a loss rate of 43.5%. A higher than sustainable rate leads to a high loss rate

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1.2

Flush Fixed 40 Fixed 20 Fixed 10 Routing

Fra

ctio

n

ACK PhaseTransfer Phase

Amount of Time Spent in Different Phases

• Fraction of time spent in different stages• A retransmission during the acknowledgment phase is expensive, and leads to a poor throughput

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5

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Flush Fixed 40 Fixed 20 Fixed 10 Routing

Tim

e (s

)

IntegrityCheckACK Phase

TransferPhaseTopologyQuery

Packet Throughput at Transfer Phase

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100

0 1 2 3 4 5 6

Effe

ctiv

e G

oodp

ut (

pkt/s

)

Hops from Sink

FlushFixed 40Fixed 20Fixed 10Routing

Eff

ect

ive G

oodput

(pkt

/s)

Effective goodput during the transfer phase

Flush provides comparable goodput as a lower loss rate which reduces the time spent in the expensive acknowledgment phase, which increases the effective bandwidth

Packet Rate over Time for a Source

• Source is 7 hops away, Data is smoothed by averaging 16 values• Flush approximates the best fixed rate with the least variance

Maximum Queue Occupancy across All Nodes for Each Packet

• Flush exhibits more stable queue occupancies than Flush-e2e

Sending Rate at Lossy Link

Both Flush and Flush-e2e adapt while the fixed rate overflows its queue

6

5 43

2

10

Packets were dropped from hop 3 to hop 2 with 50% probability between 7 and 17 seconds

Queue Length at Lossy Link

Flush and Flush-e2e adapt while the fixed rate overflows its queue

Route Change Experiment

• We started a transfer over a 5 hop path• Approximately 21 seconds into the experiment forced the node 4 hops from the sink to switch its next hop• Node 4’s next hop is changed, changing all nodes in the subpath to the root• No packets were lost, and Flush adapted quickly to the change• Flush adapts when the next hop changes suddenly0

1a

2a

3a

1b2b

3b

45

Scalability Test

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1400

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Effe

ctiv

e B

andw

idth

(B

/s)

Hops from Sink

FlushFixed 20msFixed 40msFixed 60ms

Eff

ect

ive B

and

wid

th

(B/s

)

Effective bandwidth from the real-world scalability test where 79 nodes formed 48 hop network

Flush closely tracks or exceeds the best possible fixed rate across at all hop distances that we tested

Discussion High-power node

reduces hop count and interference Not an option on the Golden Gate Bridge due

to power and maintenance problems Interactions with Routing

Link estimator of a routing layer breaks down under heavy traffic

Related Work Li et al – capacity of a chain of nodes

limited by interference using 802.11 ATP, W-TCP – rate-based transmission in

the Internet Wisden – concurrent transmission without

a mechanism for a congestion control Fetch – single flow, selective-NACK, no

mention about rate control

Conclusion A reasonable assumption (single flow)

simplifies a problem (eliminates inter-path congestion control)

Light-weight solution reduces complexity Overhearing is used to measure interference

and to exchange information Two rules to determine a rate

At each node, Flush attempts to send as fast as possible without causing interference at the next hop along the flow

A node’s sending rate cannot exceed the sending rate of its successor




With Shamim Pakzad, David Culler, James Demmel, Gregory Fenves, Steve Glaser, and Martin Turon

Node Layout (1st phase)

Distance between nodes on the span is either 100ft or 50ft

Initially designed as 150ft Difference in MicaZ radio output power

was up to 7.5dBm

8 nodes

56 nodes

1125 ft 4200 ft

500 ft

246 ft

SF(south)

Sausalito(north)

Environment

FogStrong and salty windRapidly changing... high and scary

Node

Node (Mote + Accelerometer Board)

Battery (4 X 6V Lantern Battery)

Bi-directionalPatch Antenna

Node

Extreme Rusting of C-clamp

Zip tie aroundAntenna

Base Station

Laptop

Students At Work

Installation

Hard Hat

Harness

Sharp Edge

Ouch

However…

Crawling and Installing

Done!Strong Wind

Reliable Data Collection at GGB

Bandwidth versus Hop Count

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1400

0 10 20 30 40 50Hop Count

Ban

dwid

th (

B/s

)

Aug 1stAug 7thSep 20th

Data is collected reliably over a 46-hop network, with a bandwidth of 441B/s at the 46th hop

Vibration Data of GGB

The vertical modal properties match among (1) simulation model, (2) previous study, and (3) this study

(1)

(2)

(3)

Conclusion As a concrete example of HFS, SHM is

designed, implemented and deployed Requirements are identified and solutions

are proposed The system satisfied requirements, and

provided meaningful data for the research of structural analysis

Bonus – Spectacular Views

Acknowledgement David Culler GGB – Shamim Pakzad, James Demmel, Gregory

Fenves, Steve Glaser, and Martin Turon Reliable Data Collection – Rodrigo Fonseca, Prabal

Dutta, Arsalan Tavakoli, Philip Levis, Scott Shenker and Ion Stoica

Jaein Jeong, Xiaofan Jiang, Jay Taneja, Jorge Ortiz, Robert Szewczyk, Tom Oberheim, Anthony Joseph, Joe Polastre, Alec Woo, Kamin Whitehouse, Phil Buonadonna

Average Number of Transmissions per node for sending 1,000 packets

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rage

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nsm

issi

ons

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Nod

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Hops from Sink

FlushFixed 10msFixed 20msFixed 40msOptimal

Bandwidth at Transfer Phase

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Effe

ctiv

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oodp

ut (

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Hops from Sink

FlushFixed 40Fixed 20Fixed 10Routing

Eff

ect

ive G

oodput

(B/s

)

Effective goodput during the transfer phase

Effective goodput is computed as the number of unique packets received over the duration of the transfer phase

Details of Queue Length for Flush-e2e

Memory and Code Footprint

The Golden Gate Bridge

More on Node

Signal Splitter

Antenna CableTo Base Station

(1)

(2)

(3)

The torsional modal properties match among (1) simulation model, (2) previous study, and (3) this study

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Wireless Sensor Networks for High Fidelity Sampling Sukun Kim Electrical Engineering and Computer...

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