BibTeX
@MISC{Agrawal12incorporatinglink,
author = {Piyush Agrawal},
title = {Incorporating link correlations in models and . . . },
year = {2012}
}
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Abstract
In wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shad-
Keyphrases
geographical routing structural monitoring experience similar environmental shadowing effect non-isotropic antenna gain pattern localization algorithm location-based application signal strength first contribution viable solution wireless sensor link pair efficient localization algorithm static node asset tracking rss-based localization algorithm r measurement wireless sensor network efficient model
