1 Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Tzu-Hsuan Shan 2006/11/06 J. Winter, Y. Xu, and W.-C. Lee, “Prediction.

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1 Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Tzu-Hsuan Shan 2006/11/06 J. 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

2 Outline Introduction Background and Basic schemes The Prediction-based Energy Saving scheme (PES) Performance evaluation

3 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.

4 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.

5 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.

6 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.

7 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.

8 Basic schemes

9 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.

10 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.

11 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.

12 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.

13 Wake up mechanism

14 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.

15 Performance evaluation The simulation model : Number of nodes : 95 logical sensor nodes. Monitored region : 120 x 120 m 2. Sensing coverage range : 15m.

16 Performance evaluation

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

18 Performance evaluation

19 Performance evaluation

20 Performance evaluation Sampling duration = X.

21 Performance evaluation Sampling frequency = T.