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Achieving Long- Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA
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Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Dec 23, 2015

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Page 1: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Achieving Long-Term Surveillance

in VigilNetPascal A. Vicaire

Department of Computer Science

University of Virginia

Charlottesville, USA

Page 2: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Motivating Application: Battlefield SurveillanceTo

Satellite

Page 3: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Other Applications

Wildlife Monitoring

Alarm System

Flock Protection

Border Surveillance

Page 4: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Power Consumption: An Important Issue in Surveillance Systems No power management 4 days lifetime!

Power management 10 months lifetime!

Page 5: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

State of the Art: Hardware

Power efficient hardware MICA2, MICAz, XSM, etc…

Energy scavenging hardware Vibrations. Roundy et al., “A Study of Low Level

Vibrations as a Power Source for Wireless Sensor Network”, Computer Communications, 2003.

Sun light. Perpetually powered Telos.

Page 6: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

State of the Art: Software

Synchronization and coordination: Nodes turn on only for specific tasks of which the execution time is known in advance.

Data aggregation and compression: Nodes reduce amount of transferred data to decrease energy costs.

Coverage control: Nodes providing redundant sensing coverage are turned off.

Duty cycle scheduling: Nodes alternate between on and off states at a fast rate, which still allow them to detect slow paced targets.

Page 7: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Power Management in VigilNet Combination of three schemes in real system.

Duty CycleScheduling

TripwireService

SentryService

Node Level

Group Level

Network Level

Page 8: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Putting Nodes Into a Sleep State

Toggle Period

Sleep 1% Wakeup Sleep

Preamble length = TogglePeriod * BitRate SYNC Bytes DATA CRC

Putting nodes to sleep as often and as long as possible. Sleeping mode: node wakeup 1% of the time.

Wakeup operation: send message with long preamble.

(1s)

(10ms)

Page 9: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection Redundant sensing coverage!

Page 10: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection Sentry selection and rotation.

Asleep

Awake

Sentry

Page 11: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection How are the sentries selected?

Page 12: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection 1. Neighbors exchange “hello” message

(ID + position + nb of neighbors + energy).

Page 13: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection 2. Each node selects a delay according to its

energy resources and coverage.

Delay = Function (Energy + Coverage)

Page 14: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection 3. Once the delay is elapsed, a node

announces itself as a sentry.

Page 15: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Group Level: Sentry Selection Tradeoff: detection probability versus density.

TargetDetectionProbability

Number of Sentries inArea

10010 1,000

Sensing Radius=20mSensing Radius=8mSensing Radius=2m

Page 16: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Node Level: Duty Cycle Scheduling Target takes time to go through the network.

Page 17: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Node Level: Duty Cycle Scheduling Target takes time to go through the network

duty cycle scheduling.

Toggle Period (1s)

Asleep(800ms)

Awake (200ms)

Page 18: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Node Level: Duty Cycle Scheduling Putting it all together.

Asleep

Awake

Sentry

Page 19: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Node Level: Duty Cycle Scheduling Tradeoff: detection probability versus duty cycle.

Target DetectionProbability

Proportion of the TimeSentries are Awake

40%

100%

0% 20%

1000 Nodes, V=10m/s1000 Nodes, V=30m/s

Page 20: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Exploiting knowledge about the target.

Page 21: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Putting it all together.

Asleep

Awake

Sentry

Tripwire

Page 22: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Each base station defines a tripwire; a node pertains

to the tripwire associated with the closest base.

Tripwire

Node

Base Station

Page 23: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling There are as many tripwires as base stations.

Tripwire

Node

Base Station

Page 24: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 25: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 26: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 27: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 28: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 29: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 30: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 31: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Network Level: Tripwire Scheduling Tripwire schedule specification:

Tripwire1

Tripwire2

Tripwire3

Tripwire4

Tripwire5

Tripwire6

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

1

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

1

Page 32: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

On-Demand Wakeup

Wakeup PathTo Base StationFor Prompt Reporting

Detection Wakeup SurroundingNodes for Better Target Tracking, Target Classification,& Velocity Estimation

Asleep

Awake

Base Station

Page 33: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Evaluation Methodology

Field

XSMsTripwire 1 Tripwire 2

Tripwire 3

Page 34: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Evaluation Methodology

0

10

20

30

40

50

60

70

Time (seconds)

En

erg

y(m

w)

Sentry

NonSentry

Initialization Duration = 5 minutes

Surveillance Duration = 1day

Without system rotation:NonSentry Life Time: 250 daysSentry LifeTime: 7 daysEne

rgy

(mW

s)

Time (s)

Initialization

Surveillance

SentryNon-Sentry

Page 35: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Evaluation Results: Lifetime

0

50

100

150

200

250

300

350

No PM + Sentry + Duty Cycle + Tripwire

Lifetime (days)

Page 36: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Evaluation Results: Target Detection and Classification Average detection delay: 2.42 seconds. Average classification delay: 3.56 seconds. Classification of humans, humans with

weapons, and vehicles. Average delay to get velocity estimation:

3.75 seconds (average error: 6%).

Page 37: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

Summary

Successfully integrate 3 power management strategies into real surveillance system having a 10 months lifetime (source code available online).

Analytical model to predict system performance under various system configurations.

Simulation results exposing tradeoffs between detection performance and lifetime of the network.

Page 38: Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.

My Webpage: www.cs.virginia.edu/~pv9f

Tian’s Webpage:www.cs.umn.edu/~tianhe

Research Group Webpage:www.cs.virginia.edu/~stankovic/sensornet.html

Questions?