Energy Efficient Object Tracking in Sensor Networks by Mining Temporal Moving Patterns

1. Energy Efficient Object Tracking in Sensor Networks by Mining Temporal Moving Patterns

2. Outline • Introduction • Data Mining Algorithm • Proposed Prediction Strategies • Experimental Evaluation

3. Introduction • Sensor network • Environmental data collection • Object tracking • ……etc… • The intrinsic limitation • Power constraints ( focus on energy saving ) • Synchronization • Deployment • Data routing

4. Introduction • Several researchers tried to save the energy through the software approach like scheduling of sensors • Non-prediction based tracking • periodically turn the sensor nodes off and only activate the sensor nodes when it is time to monitor their sensing regions • Prediction-based tracking • use the information of a moving object like velocity or moving direction to predict the next location the object might visit

5. Data Mining Algorithm: TMP-Mine

6. Proposed Prediction Strategies • PTMP is a non-velocity based prediction strategy that exploits the TMRs to predict the location of the missing object • PES+PTMP , using both information of detected velocity and the TMRs

11. Experimental Evaluation • TECindicates the total energy consumed by sensor nodes in the OTSN during data mining and object tracking phases • Missing ratedenotes the number of erroneous predictions in a specified time period in ratio of the total number of movement of objects

12. Selection of Ranking Method

13. Performance of Variations of PTMP

14. Comparisons of Different Prediction Methods

15. Effects of Varying the Object Velocity

16. Effects of Varying the Object Velocity