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 userâ s location. Furthermore, fingerprinting requires no
specialized hardware because of its reliance on an existing communications infrastructure.
In estimating a userâ 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 userâ 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 Techâ s Cognitive Radio Network Testbed (CORNET).
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- Masters Theses [18654]