WiFi positioning systems require radio maps in the form of either RF fingerprints or positions of WiFi access points (APs). In particular, knowledge of the AP positions is essential to enable a locating mechanism as well as to understand the nature of underlying WiFi networks, such as density, connectivity, interference characteristics, and so on. In this paper, we propose an approach called Serendipity, which locates WiFi APs in an unsupervised manner using radio scans collected by ordinary smartphone users. From the radio scans, we extract dissimilarities between all pairs of WiFi APs and estimate relative positions of APs by analyzing the dissimilarities based on a multidimensional scaling technique. We then find the absolute positions with additional radio scans whose positions are known. The discovered positions of WiFi APs are used for the positioning of smartphones or the management of the WiFi networks. To validate the proposed approach, we conducted experiments on several indoor locations.

Discovering the location of the mobile nodes carried by people is important issue for many sensor applications. Several localization techniques have been proposed, but human mobility patterns and collaboration between mobile nodes have been seldom considered. In this paper, we propose a mobile node localization system based on collaboration and route information that characterizes human mobility. To validate the feasibility of our approach, the proposed system is implemented and experiments are conducted on real routes and to evaluate various scenarios, simulation experiment was also conducted.

One interesting issue in recent sensor network research is the application of sensor nodes to mobile objects, such as people or animals, to gather non-stationary information. Apparently, locating mobile nodes is essential in many of the mobile sensor applications. Although several localization techniques have been proposed to locate mobile nodes, the mechanisms are usually based on many impractical assumptions, and mobility pattern of the mobile object is seldom considered. In this paper, we propose a practical and yet simple mobile node localization system using nearby anchor information which contains absolute time and positions. A history of anchor information is used to characterize the mobility of mobile devices. The unknown node then calculates its position with the archived anchor using a regression model. The simulation results show that the proposed algorithm outperforms previous methods under realistic conditions. We have also implemented the algorithm on real hardware, and diverse experiments were conducted to validate the feasibility of the proposed approach.