International Journal of Wireless Information Networks Heterogeneous Cooperative Localization for Social Networks

Location-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are ranging based, they are highly dependent on the distance interpreted from the Received Signal Strength (RSS) or Time of Arrival (TOA) from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: Centroid method, Nearest Neighbor method, Kernel method and AP density method to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points (APs) information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and Boston downtown. Moreover, we compared our results with the WiFi positioning system (WPS) made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is also carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density method and Kernel method outperform than other methods. Powered by Editorial Manager® and Preprint Manager® from Aries Systems Corporation Heterogeneous Cooperative Localization for Social Networks Ruijun Fu, Guanqun Bao, Yunxing Ye, Kaveh Pahlavan Center for Wireless Information Network Studies Department of ECE, Worcester Polytechnic Institute, Email: {rjfu,gbao,yunxingye,kaveh}@wpi.edu Abstract—Location-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are ranging based, they are highly dependent on the distance interpreted from the Received Signal Strength (RSS) or Time of Arrival (TOA) from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: Centroid scheme, Nearest Neighbor scheme, Kernel scheme and AP density scheme to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points (APs) information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and Boston downtown. Moreover, we compared our results with the WiFi positioning system (WPS) made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is also carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density scheme and Kernel scheme outperform than other schemes.