1. 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. 346-357.

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

3. 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. 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. 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. 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. 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. 8 Basic schemes

9. 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. 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. 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. 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. 13 Wake up mechanism

14. 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. 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. 16 Performance evaluation

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

18. 18 Performance evaluation

19. 19 Performance evaluation

20. 20 Performance evaluation Sampling duration = X.

21. 21 Performance evaluation Sampling frequency = T.