The Problem of Location Determination and Tracking in

Networked Systems

Weikuan Yu, Hui Cao, and Vineet MittalThe Ohio State University

Outline

The Problem Statement Applications Challenges Approaches

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

The Problem Statement of Tracking

Devise a scheme that tracks the location of an object Single object Multiple objects

Applications

Emergency services Efficient distribution of data Security Pursuer-evader

Location and Tracking

Source localization Location service

Infrastructure based

Non-infrastructure based

Network Object

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

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

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

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

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

Information-Driven Sensor Coordination

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

Detection and tracking

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”?