1. Weikuan Yu, Hui Cao, and Vineet Mittal The Ohio State University
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 Mobile Object Static Object Physical Location
Symbolic
Location
Location
Service
Decision/Data
Fusion
Centralized
Distributed
Beamforming
GPS
Sensing
Centralized
Distributed
Node
Location
Data
Location
Infrastructure
based
Non-infrastructure
based
Infrastructure
based
Non-infrastructure
based

7. Challenges Scalability Fault-tolerance Sensor networks
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 Sensor coordination
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|Z1, Z2, …, Zj)) = a * U (p(X|Z1, Z2, …, Zj-1, Zj)) – (1-a) U(Zj)
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”?