Enhanced Indoor Locationing in a Congested Wi-Fi Environment

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 WiFi 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.