Dual Prediction-based Reporting for Object Tracking Sensor Networks

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 T seconds Base Station OTSN

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 T seconds Base Station OTSN

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

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- • SM Base Station Monitored region

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

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

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

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

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

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

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

17. Performance Evaluation • Comparison • Naïve scheme • PREMON scheme • Prediction-based reporting mechanism Prediction Model Base Station

18. Performance Evaluation • Simulator: CSIM

19. Performance Evaluation • Workload—Total Energy Consumption

20. Performance Evaluation • Workload—Prediction Accuracy

21. Performance Evaluation • Moving Duration—Total Energy Consumption

22. Performance Evaluation • Moving Duration—Prediction Accuracy

23. Performance Evaluation • Moving speed—Total Energy Consumption

24. Performance Evaluation • Moving speed—Prediction Accuracy

25. Performance Evaluation • Reporting period—Total Energy Consumption

26. Performance Evaluation • Reporting period—Prediction Accuracy

27. Performance Evaluation • Location Model—Total Energy Consumption

28. Performance Evaluation • Location Model—Prediction Accuracy

29. 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.

30. 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.