U of Minnesota

DIWANS'06 1

Energy-Aware Scheduling Energy-Aware Scheduling withwith Quality of Surveillance Guarantee Quality of Surveillance Guarantee in in

Wireless Sensor NetworksWireless Sensor Networks

Jaehoon Jeong, Sarah Sharafkandi and David Du{jjeong,ssharaf,du}@cs.umn.edu

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ContentsContents

1. Introduction2. Related Work3. Problem Formulation4. Energy-Aware Sensor Scheduling5. Optimality of Sensor Scheduling6. QoSv-Guaranteed Sensor Scheduling7. Sensor Scheduling for Complex Roads8. Performance Evaluation9. Conclusion

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IntroductionIntroduction1. Motivation

We investigate the properties of the Linear Sensor Network (e.g., Road Network in transportation system).

These properties can be used for a variety of applications: Localization, Vehicle Detection, and Vehicle Tracking.

2. Applications of Our Sensing Scheduling Algorithm① Surveillance for Security around City’s Border② Crossroad Signal Control in Transportation System

3. Objectives① Maximization of Lifetime of Wireless Sensor Network② Control of Detection Quality

Quality of Surveillance Guarantee (QoSv)

4. Contributions① Energy-aware Sensor Scheduling feasible for Mobile Target

Detection and Tracking② QoSv-Guaranteed Sensor Scheduling for Complex Roads

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Surveillance of City Border Roads (1/2)Surveillance of City Border Roads (1/2)

Inner BoundaryCITY

Outer Boundary

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Surveillance of City Border Roads (2/2)Surveillance of City Border Roads (2/2)

Inner BoundaryCITY

Outer Boundary

S1

Road Segment

S2 S3 Sn. . . . .

Sensing Coverage

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Vehicle Detection for Road Traffic Vehicle Detection for Road Traffic MeasurementMeasurement

54 St.

53 St.

52 St.

51 St.

EwingAve.

DrewAve.

ChowenAve.

BeardAve.

vehicle

vehicle

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Related WorkRelated Work1. Temporally and Spatially Partial Coverage

① The region under surveillance is covered partially in terms of time and space.

② Our scheduling algorithm utilizes this partial coverage to save sensing energy.

2. Quality of Surveillance (QoSv)① Our QoSv is defined as the reciprocal of the average

detection time.② Other QoSv was originally defined as the reciprocal

value of the expected travel distance until the first detection.

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Problem FormulationProblem Formulation1. Assumptions

① The sensors knows their location and are time-synchronized.② The sensing range is uniform-disk.③ The cost of turn-off operation is ignorable.④ The vehicle’s maximum speed is bounded.

2. Objective To maximize the sensor network lifetime to satisfy the

following conditions Provide the reliable detection of every vehicle, Guarantee the desired average detection time, and Facilitate the mobile target tracking after the target detection.

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Sensor Network Model for Sensor Network Model for Road SegmentRoad Segment

S1

Road Segment

Vehicle

S2 S3 Sn. . . . .

Sensing Coverage

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Key Idea to Our SchedulingKey Idea to Our Scheduling

How to have some sleeping time to save energy? We observe that the vehicle needs time l/v to pass the road segment. Time l/v is the sleeping time for all the sensors on the road segment.

S1

Road Segment Length = l

Vehicle

S2 S3 Sn. . . . .

Speed = v

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Energy-Aware Sensor SchedulingEnergy-Aware Sensor Scheduling

1. Our sensor scheduling consists of two phases:① Initialization Phase② Surveillance Phase

Working Period + Sleeping Period

sn ... s2 s1

0

sn ... s2 s1

Ener

gy C

onsu

mpt

ion

[J]

Time [sec]

Sleeping (I)Initialization

Working (W) Working (W)

. . . . . sn ... s2 s1

Working (W)

Sleeping (I)

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Sensing Sequence for Sensing Sequence for Vehicle DetectionVehicle Detection

S1 . . . . .(b)

Sensor Scheduling Sequnce

S2 S3 S4 SnSn-1Sn-2

S1 . . . . .(c) S2 S3 S4 SnSn-1Sn-2

S1 . . . . .(d) S2 S3 S4 SnSn-1Sn-2

S1 . . . . .(e) S2 S3 S4 SnSn-1Sn-2

S1 . . . . .(f) S2 S3 S4 SnSn-1Sn-2

Detected

S1Vehicle . . . . .(a)

All sensors are sleeping

S2 S3 S4 SnSn-1Sn-2

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Optimality of Sensor SchedulingOptimality of Sensor Scheduling

1. Sensor Network Lifetime

The following energy can be saved through sleeping:

Number of Surveillance Periods

Working PeriodSleeping Period

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Considerations on Turn-On and Considerations on Turn-On and Warming-UP OverheadsWarming-UP Overheads1. Each Sensor’s Lifetime without Sleeping

2. Sensor Network Lifetime through Sleeping

Case 1: Turn-On Overhead is greater than Sleeping benefit

Case 2: Turn-On Overhead is less than

Sleeping benefit

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QoSv-Guaranteed Sensor SchedulingQoSv-Guaranteed Sensor Scheduling

1. Average Detection Time for Constant Vehicle Speed

Approximate Average Detection Time (ADT)

2. Average Detection Time for Bounded Vehicle Speed

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Determination of Scheduling Determination of Scheduling ParametersParameters

1. Scheduling Parameters are① The sensor network length (l)② The working time (w)③ The sleeping time (s)

2. Sensor Network Length (l)

3. Working Time (w)

4. Sleeping Time (s)where

S1

Sensor Network Length

Vehicle

S2 S3 Sn. . . . .

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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (1/4)(1/4) Road Network between the Inner and Outer

Boundaries

O1

O2

I2

I3

I4

Outer Boundary Inner Boundary

I5

I1

Vehicle

CITY

Road

Network

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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (2/4)(2/4)

A Connected Graph for an Exemplary Road Network The Road Network is represented as a Connected Graph

between the Inner and Outer Boundaries.

O1

O2

I2

I3

I4

Outer Boundary Inner Boundary

I5

P1

P2

P3 P6

P5

P4

I1

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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (3/4)(3/4) Construction of Scheduling Plan in Road Network

Determine the starting points Si to satisfy the required QoSv through Search Algorithm.

O1

O2

I2

I3

I4

Outer Boundary Inner Boundary

I5

P1

P2

P3 P6

P5

P4

I1S1

S2 S3

S4

S6

S5

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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (4/4)(4/4) Scanning in Road Network

One scanning can be split into multiple scanning. Multiple scanning can be merged into one scanning for

sensing energy.

O1

O2

I2

I3

I4

Outer Boundary Inner Boundary

I5

P1

P2

P3 P6

P5

P4

I1S1

S2 S3

S4

S6

S5

split merge

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Performance EvaluationPerformance Evaluation1. Metrics

① Sensor Network Lifetime according to Working Time and Turn-on Energy

② Average Detection Time according to Working Time and Road Segment Length (i.e., Sensor Network Length)

③ Required Average Scanning Number for Sensing Error Probability

2. Validation of Numerical Analysis① We validated our numerical analysis of our scheduling

algorithm through simulation.

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Sensor Network LifetimeSensor Network Lifetime according to according to Working Time and Turn-on EnergyWorking Time and Turn-on Energy

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Average Detection TimeAverage Detection Time according to according to Working Time and Road Segment LengthWorking Time and Road Segment Length

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Required Average Scanning NumberRequired Average Scanning Number for for Sensing Error ProbabilitySensing Error Probability

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ConclusionConclusion1. We proposed an Energy-Aware Scheduling

Algorithm to satisfy the required QoSv in Linear Sensor Network. QoSv is defined as the reciprocal value of

Average Detection Time (ADT).

2. Our Algorithm can be used for ① Surveillance for City’s Border Roads, and② Traffic Signal Control in Crossroads.

3. Future Work We develop the specific algorithm for traffic signal

control in the transportation system.

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Q & AQ & A