Wireless fidelity (Wi-Fi) fingerprinting system using hybrid classifier approach - Del Mundo, Lersan
IEEE 802.11 Wireless Fidelity (Wi-Fi) fingerprinting is an indoor positioning technique that estimates the location of a mobile device by matching an observed set of signal strength measurements (typically obtained through the Received Signal Strength Indication metric, or RSSI) received by a mobile node from multiple wireless access points, with a known or historical set of similar signal strength values in a location fingerprint database. Among several techniques proposed for indoor positioning using Wi-Fi based networks, those that rely on fingerprinting have been demonstrated to outperform those based on lateration, angulation, and cell of origin in terms of accuracy. We build a hybrid classifier architecture Wi-Fi fingerprinting techniques, based on the K-Nearest Neighbor (k-NN), Naive Bayes Classifier (NBC) and Support Vector Machines (SVM) algorithms. Our experiments show that hybrid-based approach outperformed all three K-NN, NBC, and SVM-based fingerprinting, achieving accuracies of one fingerprint vell in our testbed.