1. The Problem of Location Determination and Tracking in Networked Systems Weikuan Yu, Hui Cao, and Vineet Mittal The Ohio State University

2. Outline The Problem Statement Applications Challenges Approaches

3. The Problem Statement of Location Determination Devise a scheme that returns the location of the object Location Absolute location Relative location, e.g. beamforming, web-hosting Object Computing device, human, car, tank Information

4. The Problem Statement of Tracking Devise a scheme that tracks the location of an object Single object Multiple objects

5. Applications Emergency services Efficient distribution of data Security Pursuer-evader

6. Location and Tracking Source localization Location service Infrastructure based Non-infrastructure based Network Object

7. Challenges Scalability Locating a mobile user in a large scale network Locating a node in a mobile ad hoc network Fault-tolerance Failure of a location server Sensor networks Limited energy Limited processing power Limited communication range Sensor coordination

8. Sensor Networks Definition A spread network of small sensors Tracking moving objects Monitoring multiple objects Detecting low observable objects Sensor coordination Improved accuracy with aggregated information Reduced latency with informed selective coordination Minimize bandwidth consumption Mitigate the risk of node/link failures

9. Approaches “Everything is related to everything else but near things are more related than distant things” “Online tracking of mobile users”, by B. Awerbuch and D. Peleg Information utility “Information driven dynamic sensor collaboration for target tracking”, by F. Zhao, J. Shin, and J. Reich

10. Online Tracking of Mobile Users Construction of a tracking structure Storing location information of users at select nodes in the system Access and Update Protocols Find: using the stored information to locate the user Move: updating of stored information on relocation of the user

11. Information-Driven Sensor Coordination Making decision based on constraints regarding information, cost and resource. Metrics: Information Utility A term that quantifies the content of some data An example of tracking

12. Information-Driven Sensor Coordination

13. Information Utility Information Driven Sensor Querying and Data Routing (IDSQ) M(p(X|Z 1, Z 2, …, Z j )) = a * U (p(X|Z 1, Z 2, …, Z j-1, Z j )) – (1-a) U(Z j ) Information Utility Function: U Based on information entropy, cost to obtaining new information and Belief state of posterior distribution

14. Detection and tracking

15. Decision fusion in collaborative sensor networks Collaborative signal processing tasks such as detection, classification, localization, tracking require aggregation of sensor data. Decision fusion allows each sensor to send quantized data (decision) to a fusion center. prevent overloading the wireless network conserve energy. Question: What is “optimal decision fusion”?