Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in the outdoors cannot work well in indoors. The Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However, in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use the affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also, we assign lower weights to alter APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs

A Low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems

Location fingerprinting is a technique employed when Global Positioning System (GPS) positioning breaks down within indoor environments. Since Location Service Providers (LSPs) would implicitly have access to such information, preserving user privacy has become a challenging issue in location estimation systems. This paper proposes a low-complexity k-anonymity approach for preserving the privacy of user location and trajectory, in which real location/trajectory data is hidden within k fake locations/trajectories held by the LSP, without degrading overall localization accuracy. To this end, three novel location privacy preserving methods and a trajectory privacy preserving algorithm are outlined. The fake trajectories are generated so as to exhibit characteristics of the user’s real trajectory. In the proposed method, no initial knowledge of the environment or location of the Access Points (APs) is required in order for the user to generate the fake location/trajectory. Moreover, the LSP is able to preserve privacy of the fingerprinting database from the users. The proposed approaches are evaluated in both simulation and experimental testing, with the proposed methods outperforming other well-known k-anonymity methods. The method further exhibits a lower implementation complexity and higher movement similarity (of up to 88%) between the real and fake trajectories