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

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

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contents
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
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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related work
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
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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key idea to our scheduling
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
Energy-Aware Sensor Scheduling
  • Our sensor scheduling consists of two phases:
    • Initialization Phase
    • Surveillance Phase
      • Working Period + Sleeping Period

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optimality of sensor scheduling
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
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
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
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
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
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
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
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
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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conclusion
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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slide26
Q & A

Thanks for Attention

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