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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. 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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  1. 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

  2. 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 DIWANS'06

  3. 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 DIWANS'06

  4. Surveillance of City Border Roads (1/2) DIWANS'06

  5. Surveillance of City Border Roads (2/2) DIWANS'06

  6. Vehicle Detection for Road Traffic Measurement DIWANS'06

  7. 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. DIWANS'06

  8. 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. DIWANS'06

  9. Sensor Network Model for Road Segment DIWANS'06

  10. 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. DIWANS'06

  11. Energy-Aware Sensor Scheduling • Our sensor scheduling consists of two phases: • Initialization Phase • Surveillance Phase • Working Period + Sleeping Period DIWANS'06

  12. Sensing Sequence for Vehicle Detection DIWANS'06

  13. Optimality of Sensor Scheduling • Sensor Network Lifetime • The following energy can be saved through sleeping: Working Period Sleeping Period Number of Surveillance Periods DIWANS'06

  14. 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 DIWANS'06

  15. QoSv-Guaranteed Sensor Scheduling • Average Detection Time for Constant Vehicle Speed • Approximate Average Detection Time (ADT) • Average Detection Time for Bounded Vehicle Speed DIWANS'06

  16. 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 DIWANS'06

  17. Sensor Scheduling for Complex Roads (1/4) • Road Network between the Inner and Outer Boundaries DIWANS'06

  18. 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. DIWANS'06

  19. 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. DIWANS'06

  20. 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. DIWANS'06

  21. 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. DIWANS'06

  22. Sensor Network Lifetime according to Working Time and Turn-on Energy DIWANS'06

  23. Average Detection Time according to Working Time and Road Segment Length DIWANS'06

  24. Required Average Scanning Number for Sensing Error Probability DIWANS'06

  25. 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. DIWANS'06

  26. Q & A Thanks for Attention DIWANS'06

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