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This paper investigates the lower bounds of wireless localization accuracy using signal strength on commodity hardware. Their work relies on trace-driven analysis using an extensive indoor experimental infrastructure. First, they report the best experimental accuracy, twice the best prior reported accuracy for any localization system. They experimentally show that adding more and more resources (e.g., training points or landmarks) beyond a certain limit can degrade the localization performance for lateration-based algorithms, and that it could only be improved further by "Cleaning" the data. However, matching algorithms are more robust to poor quality RSS measurements.
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