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Dual Prediction-Based Reporting for Efficient Energy Management in Object Tracking Sensor Networks

This paper presents a novel dual prediction-based reporting mechanism aimed at optimizing energy consumption in Object Tracking Sensor Networks (OTSN). With the growing demand for effective monitoring and reporting of mobile objects, energy conservation emerges as a critical challenge. The study evaluates different prediction models, including geometric and symbolic location models, and their impact on reporting efficiency. By employing dual prediction strategies, the proposed approach minimizes energy usage while maintaining accuracy in object tracking and reporting across various operational conditions.

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Dual Prediction-Based Reporting for Efficient Energy Management in Object Tracking Sensor Networks

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  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.

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