Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

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Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network. IEEE COMMUNICATIONS LETTERS, VOL. 9, NO. 9, SEPTEMBER 2005 Zhen Guo, Mengchu Zhou, Fellow, IEEE, ( , http://web.njit.edu/~zhou/) and Lev Zakrevski, Member, IEEE Presentation by Cheng-Ta Lee. Outline. - PowerPoint PPT Presentation

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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, (, http://web.njit.edu/~zhou/)and Lev Zakrevski, Member, IEEEPresentation byCheng-Ta Lee

  • OutlineIntroductionPredictive Tracking Sensor Network ArchitecturePower Optimization and Quantitative AnalysisConclusionFuture Work

  • Introduction 1/3Object tracking is an important application in wireless sensor networksTerrorist attack detectionTraffic monitoringMost of researchers concentrate on tracking objects and finding efficient ways to forward the data reports to the sinks

  • Introduction 2/3Tracking IntervalAs the tracking interval becomes lower, in other words more frequent, the tracking power consumption is increased As it increases , the miss probability increases , thereby lowering the tracking quality

  • Introduction 3/3This paper intends topropose a quantitative analytical model to find such an optimal tracking intervalstudy the effect of the tracking interval on the miss probabilitypropose a scheme called Predictive Accuracy-based Tracking Energy Saving (PATES) by exploiting the tradeoff between the accuracy and cost of sensing operation.

  • Predictive Tracking Sensor Network Architecture 1/2Object Tracking Sensor NetworksAn object tracking sensor network refers to a wireless sensor network designed to monitor and track the mobile targets in the covered areaGenerally, each sensor consists of three functional unitsMicro-Controller Unit (MCU)Sensor componentRF radio communication component

  • Predictive Tracking Sensor Network Architecture 2/2Predictive Accuracy-based Tracking Energy Saving (PATES)In PATES, three modules must be in use.Monitoring and trackingPrediction and reportingRecoveryThe targets are missed, then the recovery module is initiatedALL NBR recoveryALL NODE recovery

  • Power Optimization and Quantitative Analysis 1/6quadratic function

    s: tracking intervala, b, and c are the constantsmissing probability P(s)

  • Power Optimization and Quantitative Analysis 2/6m: number of the neighbor around the current node.N: total number of sensors in whole networkNotification: when a neighbor nodes detects the target, it sends notification to the currect node

  • Power Optimization and Quantitative Analysis 3/6T: Entire periods: Tracking interval

  • Power Optimization and Quantitative Analysis 4/6

  • Power Optimization and Quantitative Analysis 5/6a=0.0013, b=0.025, and c=0.062

  • Power Optimization and Quantitative Analysis 6/6Fig. 2 shows the relationship between the power consumption and tracking interval

  • ConclusionThe power consumption with respect to tracking intervals can be minimized with a quadratic miss probability function under a given prediction algorithmA predictive tracking scheme to optimize the power efficiency with two stages of recovery is proposedThe proposed scheme is demonstrated by the analytical results to be capable of successfully balancing the tradeoff between the prediction accuracy and tracking cost

  • Future Work 1/2Propose an algorithm to automatically model and validate the real-time relationship between miss probability and tracking intervalConsideration 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)

  • Future Work 2/2Decrease missing probabilityBecause Erecovery = 9656mJ >> Esuccess = 42mJFor 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)

  • Q & A

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