WASP: An Enhanced Indoor Locationing Algorithm for a Congested Wi-Fi Environment
Accurate and reliable location information is important to many context-aware mobile applications. While the Global Positioning System (GPS) works quite well outside, it is quite problematic for indoor locationing. In this paper, we introduce WASP, an enhanced indoor locationing algorithm. WASP is based on the Redpin algorithm which matches the received Wi-Fi signal with the signals in the training data and uses the position of the closest training data as the user’s current location. However, in a congested Wi-Fi environment the Redpin algorithm gets confused because of the unstable radio signals received from too many APs. WASP addresses this issue by voting the right location from more neighboring training examples, weighting Access Points (AP) based on their correlation with a certain location, and automatic filtering of noisy APs. WASP significantly outperform the-state-of-the-art Redpin algorithm. In addition, this paper also reports our findings on how the size of the training data, the physical size of the room and the number of APs affect the accuracy of indoor locationing.
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