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

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. 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 m2. • 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.