A mutual information based online access point selection strategy for WiFi indoor localization

WiFi based Indoor Positioning System (IPS) has become the most popular and practical system to provide Location Based Service (LBS) in indoor environments due to the availability of massive existing WiFi network infrastructures in buildings. WiFi based IPSs leverage received signal strengths (RSSs) from large numbers of WiFi access points (APs) and estimate the location of clients. Proper AP selection methods are required to select a subset of APs that provide useful information for localization in order to reduce the computational load and preserve or even enhance the localization accuracy of the entire IPS. Existing approaches select and measure the discriminative ability of APs individually during the offline phase without the consideration of the dependence among them. In this paper, based on mutual information of APs, we present online mutual information (OnlineMI), a novel AP selection strategy that measures the collective discriminative ability among APs. Furthermore, the proposed AP selection process is conducted online to adapt various environmental dynamics. Weighted k nearest neighbor (WKNN) approach is further employed as the localization algorithm after the OnlineMI AP selection strategy. Extensive experiments are carried out, and performance evaluation and comparison with existing methods demonstrates the superiority of the proposed OnlineMI-WKNN approach.