Empirical-based analysis of a cooperative location-sensing system

We have designed a novel positioning system, the Cooperative Location-sensing system (CLS) that employs the peer-to-peer paradigm and a probabilistic framework to estimate the position of wireless-enabled devices in an iterative manner without the need for an extensive infrastructure or time-strenuous training. CLS can incorporate signal-strength maps of the environment to improve the position estimates. Such maps have been built using measurements that were acquired from Access Points (APs) and peers during a training phase. This paper makes three important contributions. First, it uses a particle-filters-based framework to model theoretically CLS. Second, it proposes new algorithms that incorporate real-life signal strength measurements from (APs) and peers to estimate position and distance. Third, it evaluates the performance of CLS via real-life measurements and extensive simulation, and compares it with other positioning systems. We have implemented and evaluated the CLS prototype along with its variants using IEEE802.11 and Blue-tooth, and compared its performance with other positioning systems.

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