Distributed Localization for Wireless Distributed Networks in Indoor Environments

Abstract

Positioning systems enable location-awareness for mobile devices, computers, and even tactical radios. From the collected location information, location-based services can be realized. One type of positioning system is based on location fingerprints. Unlike the conventional positioning techniques of time of or time delay of arrival (TOA/TDOA) or even angle of arrival (AOA), fingerprinting associates unique characteristics such as received signal strength (RSS) that differentiates a location from another location. The location-dependent characteristics then can be used to infer a user's location. Furthermore, fingerprinting requires no specialized hardware because of its reliance on an existing communications infrastructure. In estimating a user's position, fingerprint-based positioning systems are centrally calculated on a mobile computer using either a Euclidean distance algorithm, Bayesian statistics, or neural networks. With large service areas and, subsequently, large radio maps, one mobile computer may not have the adequate resources to locally compute a user's position. Wireless distributed computing provides a means for the mobile computer to meet the location-based service requirements and increase its network lifetime. This thesis develops distributed localization algorithms to be used in an indoor fingerprint-based positioning system. Fingerprint calculations are not computed on a single device, but rather on a wireless distributed computing network on Virginia Tech's Cognitive Radio Network Testbed (CORNET).