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 2005
• Zhen Guo,
• Mengchu Zhou, Fellow, IEEE, (???,
  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 other
  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 units
• Micro-Controller Unit (MCU)
• Sensor component
• RF 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 tracking
• Prediction and reporting
• Recovery
• The targets are missed, then the recovery module
  is initiated
• ALL NBR recovery
• ALL 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
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Power Optimization and Quantitative Analysis 5/6
a0.0013, b0.025, and c0.062
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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 gtgt 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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Q A