Dynamic Localization Control for Mobile Sensor Networks

1. Dynamic Localization Control for Mobile Sensor Networks S. Tilak, V. Kolar, N. Abu-Ghazaleh, K. Kang (Computer Science Department, SUNY Binghamton)

2. Agenda Introduction to Localization Motivation Problem Definition Protocols Results Future work Conclusion

3. Introduction to Localization AVG Normal

4. Existing Research on Localization • Focus on Static Sensor Network • Existing Approaches: • -Range/Direction based • -calculate distance from beacons and triangulate • -Received Signal Strength (e.g., RADAR) • -Time of Arrival (e.g., GPS) • -Time Difference of Arrival (e.g., Cricket, Bat) • -Calculate angle from beacons and triangulate • -Proximity based • -Centroid (Bulusu 00) • -ATIP (Mobicom 2003) • -DV-hop • -MDS (MobiHoc 2003) • -single hop vs. multi-hop to beacon

5. Motivation What about Mobile Sensor Networks ? Interesting Energy-Accuracy trade off !

6. Problem Definition

7. Goals Self-configuring Light-weight Enable Micro-monitoring Application-specific Scalable, distributed

8. Protocols SFR (Static Fixed Rate) DVM (Dynamic Velocity Monotonic) MADRD (Mobility Aware Dead Reckoning Driven)

9. SFR Localize every t seconds Very simple to implement Once Localize tag data with those coordinates till next localization Energy expenditure independent of Mobility Performance varies with Mobility Existing Projects such as Zebranet use this approach (3 minutes).

10. DVM Adaptive Protocol Sensor Adapts its localization frequency to Mobility Goal maintain error under application-specific tolerance Compute current velocity and use it to decide next localization period Once Localize tag data with those coordinates till next localization Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

11. MADRD Predictive Protocol Estimate mobility pattern and use it to predict future localization Localization triggered when actual mobility and predicted mobility differes by application-specific tolerance Tag data with predicted coordinates (differs from SFR and DVM) Changes in mobility model affect the performance Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

12. MADRD State Diagram

13. Analysis of the Proposed Protocols • Constant Velocity model • SFR and DVM error increases linearly • MADRD estimates location precisely (no error) • Contant Velocity + pause • SFR and DVM error increasely linearly and stays there • MADRD has 0 initial error and then it increases linearly • Contant Vecloty + change in direction

14. Direction change

15. Summary of Analysis • Error in non-predictive protocols increase with any mobility that moves the node away from its last localization point • Error in Predictive protocols increase only when the predictive model is inaccurate • Model estimation in incorrect • Model changes (pause, direction change)

16. Instantenous Error Study

17. Energy Expenditure Study DVM adapts 4-5 m/s 0.5-1 m/s

18. Error versus Mobility and Pause Time SFR error increases linearly with mobility, DVM, MADRD not much change

19. Accuracy versus Mobility and Pause Time

20. Conclusion Explored interesting energy accuracy trade offs for mobile sensor network with three protocols Different velocities and pause time Adaptive and Predictive protocols can outperform static protocol If mobility model is predictable MADRD performs well MADRD performed well under all situations that we simulated Possible to design light-weight, self-configuring, and scalable protocols that reduce localization energy without sacrifying accuracy

21. Future Work Implement all protocols on Motes Study protocols under more mobility models Event driven sensor network Incorporating application semantics such as data priorities

22. Questions ?

23. Thank You !!!