1. Dual Prediction-based Reporting for Object Tracking Sensor Networks Yingqi Xu, Julian Winter, Wang-Chien Lee Department of Computer Science and Engineering, Pennsylvania State University International Conference on Mobile and Ubiquitous Systems: System and Services (MobiQuitous 2004) Speaker: Hao-Chun Sun

2. Outline Introduction Related Work Dual Prediction Based Reporting Performance Evaluation Conclusion

3. Introduction -background- Object Tracking Sensor Network (OTSN)  Energy conservation is the most critical issue. Monitoring Reporting OTSN Base Station T seconds

4. Introduction -background- Object Tracking Sensor Network (OTSN)  Sensor Fusion Problem Deciding the states of the tracked objects may need several sensor nodes to work together.

5. Introduction -background- Factors impact on the energy consumption  Network workload  Reporting frequency  Location models  Data precision OTSN Base Station T seconds

6. Related Work -PES- Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks (IEEE MDM 2004) RF Radio SensorMCU Sensor Node OTSN Base Station T seconds

7. Related Work -PES- Basic monitoring schemes  Naïve Space: All sensor nodes Time: All time  Scheduled Monitoring (SM) Space: All sensor nodes Time: activated for X (s), sleep for (T-X) (s)  Continuous Monitoring (CM) Space: One sensor node Time: All time

8. Related Work -PES- Base Station Monitored region SM

9. Related Work -PES- Base Station Monitored region SM

10. Related Work -PES- Base Station Monitored region CM

11. Related Work -PES- Monitoring Solution Space Ideal Scheme Energy consumption decreases Missing rate increases Naive SM CM Number of Nodes Sampling Frequency 1 S Lowest Frequency(=1) Highest Frequency(=T/X) Legend Basic schemes Possible schemes Legend Basic schemes Possible schemes

12. Related Work -PES- Prediction Model—  Heuristics INSTANT Current node assumes that moving objects will stay in the current speed and direction for the next (T-X) seconds.  Heuristics AVERAGE By recording some history, the current node derives the object’s speed and direction for the next (T-X) seconds from the average of the object movement history.  Heuristics EXP_AVG Assigns different weights to the different stages of history.

13. Dual Prediction based Reporting Reporting energy conservation OTSN Base Station T frequency RF Radio SensorMCU Sensor Node

14. c b Dual Prediction based Reporting f d a Base Station Instance Prediction Model e Instance Prediction Model OTSN

15. Related Work -PES- Wake up Mechanisms  Heuristic DESTINATION Only informs the destination node. Higher probability of losing the object.  Heuristic ROUTE In addition to the destination node, it also include the node on the route from current node to destination node.  Heuristic ALL_NBR In addition to route and destination node, the current node also informs the neighboring nodes surrounding the route, current node, and destination node.

16. Related Work -PES- Wake up Mechanisms

17. Dual Prediction based Reporting Location Models  Indirectly affect the accuracy of the prediction models.  Two categories Geometric location model Symbolic location model

18. Dual Prediction based Reporting Location Models  Sensor Cell(SS)  Triangle(ST)  Grid(SG)  Coordinate(SG)

19. Performance Evaluation Comparison  Naïve scheme  PREMON scheme Prediction-based reporting mechanism Base Station Prediction Model

20. Performance Evaluation Simulator: CSIM

21. Performance Evaluation Workload—Total Energy Consumption

22. Performance Evaluation Workload—Prediction Accuracy

23. Performance Evaluation Moving Duration— Total Energy Consumption

24. Performance Evaluation Moving Duration— Prediction Accuracy

25. Performance Evaluation Moving speed— Total Energy Consumption

26. Performance Evaluation Moving speed— Prediction Accuracy

27. Performance Evaluation Reporting period— Total Energy Consumption

28. Performance Evaluation Reporting period— Prediction Accuracy

29. Performance Evaluation Location Model— Total Energy Consumption

30. Performance Evaluation Location Model— Prediction Accuracy

31. Conclusion OTSN energy consumption  Monitoring and Reporting Dual Prediction Reporting (DPR)  Prediction Model  Location Model DPR is able to minimize the energy usage of OTSNs efficiently under various condition.

32. Conclusion Mobile objects have less impact on the low granular location models than the high granular one. The longer reporting period is adverse to the prediction-based schemes with high granular location models, but improves the prediction accuracy for the location models with low gutturality by eliminating the granularity effect.