A comparison of Wireless Fidelity (Wi-Fi) fingerprinting techniques

Abstract

Among several techniques proposed for indoor positioning using IEEE 802.11 Wireless Fidelity (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 compare and evaluate three Wi-Fi fingerprinting techniques that use the K-Nearest Neighbor (k-NN), Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. Our experiments show that SVM-based fingerprinting outperformed both k-NN and NBC-based fingerprinting, achieving accuracies of 2 meters or better within our testbed.