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Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

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Optimal Tracking Interval forPredictive Tracking in Wireless

Sensor Network

IEEE COMMUNICATIONS LETTERS, VOL. 9, NO. 9, SEPTEMBER 2005Zhen Guo, Mengchu Zhou, Fellow, IEEE, (周孟初 , http://web.njit.edu/~zhou/)and Lev Zakrevski, Member, IEEE

Presentation by

Cheng-Ta Lee


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Outline

Introduction Predictive Tracking Sensor Network

Architecture Power Optimization and Quantitative

Analysis Conclusion Future Work


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Introduction 1/3

Object tracking is an important application in wireless sensor networks Terrorist attack detection Traffic monitoring

Most of researchers concentrate on tracking objects and finding efficient ways to forward the data reports to the sinks


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Introduction 2/3

Tracking Interval As the tracking interval becomes lower↓, in oth

er words ”more frequent↑”, the tracking power consumption is increased ↑

As it increases ↑, the miss probability increases ↑, thereby lowering the tracking quality ↓


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Introduction 3/3

This paper intends to propose a quantitative analytical model to find

such an optimal tracking interval study the effect of the tracking interval on the

miss probability propose a scheme called Predictive Accuracy-

based Tracking Energy Saving (PATES) by exploiting the tradeoff between the accuracy and cost of sensing operation.


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Predictive Tracking Sensor Network Architecture 1/2

Object Tracking Sensor Networks An object tracking sensor network refers to a

wireless sensor network designed to monitor and track the mobile targets in the covered area

Generally, each sensor consists of three functional unitsMicro-Controller Unit (MCU)Sensor componentRF radio communication component


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Predictive Tracking Sensor Network Architecture 2/2

Predictive Accuracy-based Tracking Energy Saving (PATES) In PATES, three modules must be in use.

Monitoring and trackingPrediction and reportingRecovery

The targets are missed, then the recovery module is initiated

ALL NBR recoveryALL NODE recovery


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Power Optimization and Quantitative Analysis 1/6

quadratic function

s: tracking interval a, b, and c are the

constants missing probability

P(s)


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•m: number of the neighbor around the current node.

•N: total number of sensors in whole network

•Notification: when a neighbor nodes detects the target, it sends notification to the currect node


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•T: Entire period

•s: Tracking interval


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a=0.0013, b=0.025, and c=0.062


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Fig. 2 shows the relationship between the power consumption and tracking interval


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Conclusion The power consumption with respect to tracking

intervals can be minimized with a quadratic miss probability function under a given prediction algorithm

A predictive tracking scheme to optimize the power efficiency with two stages of recovery is proposed

The proposed scheme is demonstrated by the analytical results to be capable of successfully balancing the tradeoff between the prediction accuracy and tracking cost


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Future Work 1/2

Propose an algorithm to automatically model and validate the real-time relationship between miss probability and tracking interval

Consideration three stages recovery or other recovery mechanism (for example, wake up all the two steps’ neighbor nodes around the current sensor in ALL_NBR recovery stage)


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Future Work 2/2 Decrease missing probability

Because Erecovery = 9656mJ >> Esuccess = 42mJ

For example, (always) wake up all the neighbor nodes around the current sensor in next state (Optimal number of wake up the neighbor nodes around the current sensor in next state)

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Number of wake up the neighbor nodes around the current sensor in next state

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Number of wake up the neighbor nodes around the current sensor in next state


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Q & A