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Enhanced Indoor Locationing in a Congested Wi-Fi Environment (2009)
BibTeX
@MISC{Lin09enhancedindoor,
author = {Hsiuping Lin and Ying Zhang and Martin Griss and Ilya L},
title = {Enhanced Indoor Locationing in a Congested Wi-Fi Environment},
year = {2009}
}
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Abstract
Many context-aware mobile applications require a reasonably accurate and stable estimate of a user’s location. While the Global Positioning System (GPS) works quite well world-wide outside of buildings and urban canyons, locating an indoor user in a real-world environment is much more problematic. Several different approaches and technologies have been explored, some involving specialized sensors and appliances, and others using increasingly ubiquitous Wi-Fi and Bluetooth radios. In this project, we want to leverage existing Wi-Fi access points (AP) and seek efficient approaches to gain usefully high room-level accuracy of the indoor location prediction of a mobile user. The Redpin algorithm, in particular, matches the Wi-Fi signal received 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 where many APs exist, the standard Redpin algorithm can become confused because of the unstable radio signals received from too many APs. In this paper, we propose several enhanced indoor-locationing algorithms for the congested Wi-Fi environment. Different statistical learning algorithms are compared and empirical results show that: using more neighbors gives better results than using the 1-best neighbor; weighting APs with the correlation between the AP visibility and the location is better than the equally weighted AP combination, and automatic filtering noisy APs increases the overall detection accuracy. Our experiments in a university building show that our enhanced indoor locationing algorithms 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.
Keyphrases
congested wi-fi environment training data enhanced indoor locationing weighted ap combination automatic filtering noisy aps wi-fi access point specialized sensor real-world environment indoor location prediction standard redpin algorithm different statistical learning algorithm bluetooth radio enhanced indoor physical size current location university building show seek efficient approach 1-best neighbor high room-level accuracy many aps unstable radio signal the-state-of-the-art redpin algorithm ubiquitous wi-fi indoor user empirical result wi-fi signal ap visibility indoor locationing stable estimate mobile user several different approach user location redpin algorithm many context-aware mobile application many aps exist indoor-locationing algorithm global positioning system overall detection accuracy urban canyon
