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U of Minnesota DIWANS'061 Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and.

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Presentation on theme: "U of Minnesota DIWANS'061 Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and."— Presentation transcript:

1 U of Minnesota DIWANS'061 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

2 2DIWANS'06 Contents 1.Introduction 2.Related Work 3.Problem Formulation 4.Energy-Aware Sensor Scheduling 5.Optimality of Sensor Scheduling 6.QoSv-Guaranteed Sensor Scheduling 7.Sensor Scheduling for Complex Roads 8.Performance Evaluation 9.Conclusion

3 3DIWANS'06 Introduction 1.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 Algorithm  Surveillance for Security around City’s Border  Crossroad Signal Control in Transportation System 3.Objectives  Maximization of Lifetime of Wireless Sensor Network  Control of Detection Quality  Quality of Surveillance Guarantee (QoSv) 4.Contributions  Energy-aware Sensor Scheduling feasible for Mobile Target Detection and Tracking  QoSv-Guaranteed Sensor Scheduling for Complex Roads

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

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

6 6DIWANS'06 Vehicle Detection for Road Traffic Measurement

7 7DIWANS'06 Related Work 1.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. 2.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.

8 8DIWANS'06 Problem Formulation 1.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. 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.

9 9DIWANS'06 Sensor Network Model for Road Segment

10 10DIWANS'06 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.

11 11DIWANS'06 Energy-Aware Sensor Scheduling 1.Our sensor scheduling consists of two phases:  Initialization Phase  Surveillance Phase  Working Period + Sleeping Period

12 12DIWANS'06 Sensing Sequence for Vehicle Detection

13 13DIWANS'06 Optimality of Sensor Scheduling 1.Sensor Network Lifetime The following energy can be saved through sleeping: Number of Surveillance Periods Working Period Sleeping Period

14 14DIWANS'06 Considerations on Turn-On and Warming- UP Overheads 1.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

15 15DIWANS'06 QoSv-Guaranteed Sensor Scheduling 1.Average Detection Time for Constant Vehicle Speed Approximate Average Detection Time (ADT) 2.Average Detection Time for Bounded Vehicle Speed

16 16DIWANS'06 Determination of Scheduling Parameters 1.Scheduling Parameters are  The sensor network length (l)  The working time (w)  The sleeping time (s) 2.Sensor Network Length (l) 3.Working Time (w) 4.Sleeping Time (s) where

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

18 18DIWANS'06 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.

19 19DIWANS'06 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.

20 20DIWANS'06 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.

21 21DIWANS'06 Performance Evaluation 1.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 2.Validation of Numerical Analysis  We validated our numerical analysis of our scheduling algorithm through simulation.

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

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

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

25 25DIWANS'06 Conclusion 1.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  Surveillance for City’s Border Roads, and  Traffic Signal Control in Crossroads. 3.Future Work We develop the specific algorithm for traffic signal control in the transportation system.

26 26DIWANS'06 Q & A


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