1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Weikuan Yu Dept. of Computer and Info. Sci. The Ohio State University

2. Presentation Outline Problem Statement General Ideas and Related Work Current System at Study  Goals aimed  Processing Steps  Algorithms  Critical Factors Node and beacon placement Traffic and energy consumption Conclusion

3. Problem Statement Wireless sensors network widespread deployed  signal sensing,  emergence detection  ground vibration Location awareness is indispensable  Immediate information transmission  Quick routing of query  Tracking of objects

4. Problem Statement Problems with GPS  Not work indoors  High power consumption, short lifetime  High cost

5. General Ideas and Related Work Localization Basics  Ranging RSSI ToA, TDoA AoA  Estimation

6. Related Work RADAR  Use RF signals to track indoor objects  Offline and online phases  High cost Cricket location support  Low cost for location awareness  Use Ultrasound singals  4 x 4 feet granularity BAT  Centralize configuration  Granularity at centimeters level Both Cricket and BAT are infrastructures-based networks

7. ADLOS (Ad-Hoc Localization System) Goals  Ad-Hoc Sensor Network (Dynamic network)  Fine granularity  Low cost  Distributed location awareness Processing Phases  Ranging  Estimation

8. Ranging Characterization Received Signal Strength  Susceptible to environmental changes, e.g., shadowing, fading and even altitude

9. Radio Characteristics  Received Signal Strength  Susceptible to environmental changes  shadowing, fading and even altitude  No consistent model for some factors  Restriction: all nodes are at ground level  r: distance, X and n are constants  WINS nodes

10. WINS node RSSI characterization

11. ToA using RF and Ultrasound

12. Ultrasound Ranging characterization

13. Signal Strength and ToA Ranging ToA is more robust and fine-grained Susceptible to environmental changes Consider the combination of ToA and RF

14. Estimation Algorithms

15.  Atomic Multilateration  Basic Formula  Weighted Combination

16. Iterative Multilateraion

17. Accuracy of Iterative Multilateration

18. Enhanced Iterative Multilateration

19. Collaborative Multilateration

21. Node and Beacon Placement Connectivity of a node Probability of having a connected node

22. Number of nodes per unit area, lamda

23. Distribution of Connectivity Results

24. Required Beacon Nodes

25. Power Chacterization

26. Power consumption at different operational modes

27. Traffic with different implementation

28. Energy with different implementation

29. Conclusion A new localization system scheme for Ad-Hoc wireless sensor networks  Distributed, low cost  Fine-grained ToA ranging is better; hybrid can be even better Distributed is advocated for estimation  Less energy  Less traffic  Although less accurate