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Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks

Tzu-Hsuan Shan2006/11/06J. Winter, Y. Xu, and W.-C. Lee, “Prediction Based Strategies for Energy Saving in Object Tracking Sensor Networks,” IEEE International Conference on Mobile Data Management (MDM'04), Berkeley, CA, Jan. 2004, pp. 346-357.

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Outline

Introduction

Background and Basic schemes

The Prediction-based Energy Saving scheme (PES)

Performance evaluation

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Introduction

What is Object Tracking Sensor Network?A sensor network that the task of the nodes is to report the position of a certain type of object to the base station periodically.

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Background

Application requirements :Suppose each sampling duration takes X seconds.

The application requires the nodes to report the objects’ location every T seconds.

Problem definition :Develop energy saving schemes which minimize overall energy consumption of the OTSN under an acceptable missing rate.

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Basic schemes

Naïve scheme :In this scheme, all the nodes stay in active mode to monitor their detection areas all the time.

The most energy cost scheme with 0 missing rate.

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Basic schemes

Scheduled monitoring scheme :In this scheme, nodes are activated only when needed.

All the nodes wake up every (T-X) seconds for X seconds and go to sleep.

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Basic schemes

Continuous monitoring scheme :In this scheme, only the node who has the object in its detection area will be activated.

An awake node actively monitors the object until the object enters a neighboring cell.

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Basic schemes

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Prediction-based Energy Saving scheme

The basic idea of PES is that all sensor nodes should stay in sleep mode as long as possible.

After a current node performs sensing for X seconds, it will predict the position of the object for the next (T-X) seconds and informs the target node, then go to sleep.

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Prediction-based Energy Saving scheme

PES consists of three parts :Prediction model ─ which anticipates the future movement of an object.

Wake up mechanism ─ decide which nodes will be the target node.

Recovery mechanism ─ is initiated when the network loses the track of an object.

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Prediction model

There are three heuristics for selecting the speed and the direction used by the prediction model :

Heuristics INSTANT ─ assumes that the objects will stay in the current speed and direction.

Heuristics AVERAGE ─ the speed and direction are derived from the average of the object movement history.

Heuristics EXP_AVG ─ it assigns different weights to the different stages of history.

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Wake up mechanism

Based on the different levels of conservativeness, three mechanisms are proposed :

Heuristic DESTINATION ─ only the destination node will be informed.

Heuristic ROUTE ─ the nodes on the route from the current node to the destination node will also be informed.

Heuristic ALL_NBR ─ the neighboring nodes surrounding the route, the current node and the destination node will also be informed.

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Wake up mechanism

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Recovery mechanism

The recovery mechanism contains two steps :Upon the object miss, the previous current node uses the heuristic ALL_NBR to wake up those nodes.

In case that ALL_NBR recovery fails, the previous current node will initiate flooding recovery which wakes up all of the nodes in the network.

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Performance evaluation

The simulation model :Number of nodes : 95 logical sensor nodes.

Monitored region : 120 x 120 m2.

Sensing coverage range : 15m.

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Performance evaluation

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Performance evaluation

Pause time = the time interval that the object changes its speed and direction.

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Performance evaluation

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Performance evaluation

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Performance evaluation

Sampling duration = X.

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Performance evaluation

Sampling frequency = T.