Secure and Robust Wi-Fi Fingerprinting Indoor Localization

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. Fingerprinting techniques can provide a low-cost and high-accuracy localization solution by utilizing in-building communication infrastructures. However, existing fingerprinting localization algorithms are not resistant to outliers, for example, the accidental environment changes, access point (AP) attacks. Another drawback is that traditional K nearest neighbor (KNN) algorithm in the literature may not select the candidate reference points (RPs) correctly. In this paper, we propose a novel environmentally robust and attack resistant probabilistic fingerprinting localization method. In the offline phase, the distribution estimation of the signal strength is performed using probabilistic histogram method. Then in the online phase, a three-step location sensing method is proposed. In the first step, a simple and efficient outlier detection method named non-iterative "RANdom SAmple Consensus" (RANSAC) is run to detect and eliminate part of APs from which the signals measured are severely distorted by unexpected environment effects. In the second step, a novel region-based RP selection method which works like a "family of probability" is proposed to improve the possibility of the correctness of selection of the nearest RPs. In the final step, the location is obtained using a weighted-mean method. In the experiment section, we demonstrate the proposed method in our lab and find that the proposed strategies are resistant to outliers and can improve the localization accuracy effectively compared with existing methods.