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
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network
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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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Power Optimization and Quantitative Analysis 2/6
•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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Power Optimization and Quantitative Analysis 3/6
•T: Entire period
•s: Tracking interval
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Power Optimization and Quantitative Analysis 4/6
Power Optimization and Quantitative Analysis 5/6
a=0.0013, b=0.025, and c=0.062
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Power Optimization and Quantitative Analysis 6/6
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