PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Proximity classification for mobile devices using wi-fi environment similarity
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Redpin - adaptive, zero-configuration indoor localization through user collaboration
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Overseer: A Mobile Context-Aware Collaboration and Task Management System for Disaster Response
C5 '10 Proceedings of the 2010 Eighth International Conference on Creating, Connecting and Collaborating through Computing
Wi-Fi fingerprinting through active learning using smartphones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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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.