Energy aware scheduling with quality of surveillance guarantee in wireless sensor networks
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Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks. Jaehoon Jeong , Sarah Sharafkandi and David Du {jjeong,ssharaf,du}@cs.umn.edu. Contents. Introduction Related Work Problem Formulation Energy-Aware Sensor Scheduling

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Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks

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Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks

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

DIWANS'06


Contents

  • Introduction

  • Related Work

  • Problem Formulation

  • Energy-Aware Sensor Scheduling

  • Optimality of Sensor Scheduling

  • QoSv-Guaranteed Sensor Scheduling

  • Sensor Scheduling for Complex Roads

  • Performance Evaluation

  • Conclusion

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Introduction

  • 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.

  • Applications of Our Sensing SchedulingAlgorithm

    • Surveillance for Security around City’s Border

    • Crossroad Signal Control in Transportation System

  • Objectives

    • Maximization of Lifetime of Wireless Sensor Network

    • Control of Detection Quality

      • Quality of Surveillance Guarantee (QoSv)

  • Contributions

    • Energy-aware Sensor Scheduling feasible for Mobile Target Detection and Tracking

    • QoSv-Guaranteed Sensor Scheduling for Complex Roads

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Surveillance of City Border Roads (1/2)

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Surveillance of City Border Roads (2/2)

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Vehicle Detection for Road Traffic Measurement

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Related Work

  • Temporally and Spatially Partial Coverage

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

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

  • Quality of Surveillance (QoSv)

    • Our QoSv is defined as the reciprocal of the average detection time.

    • Other QoSv was originally defined as the reciprocal value of the expected travel distance until the first detection.

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Problem Formulation

  • Assumptions

    • The sensors knows their location and are time-synchronized.

    • The sensing range is uniform-disk.

    • The cost of turn-off operation is ignorable.

    • The vehicle’s maximum speed is bounded.

  • 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 Road Segment

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Key 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.

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Energy-Aware Sensor Scheduling

  • Our sensor scheduling consists of two phases:

    • Initialization Phase

    • Surveillance Phase

      • Working Period + Sleeping Period

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Sensing Sequence for Vehicle Detection

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Optimality of Sensor Scheduling

  • Sensor Network Lifetime

    • The following energy can be saved through sleeping:

Working Period

Sleeping Period

Number of Surveillance Periods

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Considerations on Turn-On and Warming-UP Overheads

  • Each Sensor’s Lifetime without Sleeping

  • 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 Scheduling

  • Average Detection Time for Constant Vehicle Speed

    • Approximate Average Detection Time (ADT)

  • Average Detection Time for Bounded Vehicle Speed

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Determination of Scheduling Parameters

  • Scheduling Parameters are

    • The sensor network length (l)

    • The working time (w)

    • The sleeping time (s)

  • Sensor Network Length (l)

  • Working Time (w)

  • Sleeping Time (s)

where

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Sensor Scheduling for Complex Roads (1/4)

  • Road Network between the Inner and Outer Boundaries

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Sensor Scheduling for Complex Roads (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.

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Sensor Scheduling for Complex Roads (3/4)

  • Construction of Scheduling Plan in Road Network

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

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Sensor Scheduling for Complex Roads (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.

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Performance Evaluation

  • Metrics

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

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

    • Required Average Scanning Number for Sensing Error Probability

  • Validation of Numerical Analysis

    • We validated our numerical analysis of our scheduling algorithm through simulation.

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

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Average Detection Time according to Working Time and Road Segment Length

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Required Average Scanning Number for Sensing Error Probability

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Conclusion

  • 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).

  • Our Algorithm can be used for

    • Surveillance for City’s Border Roads, and

    • Traffic Signal Control in Crossroads.

  • Future Work

    • We develop the specific algorithm for traffic signal control in the transportation system.

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Q & A

Thanks for Attention

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