Wi-Fi based indoor location positioning employing random forest classifier

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Abstract
Location positioning in indoor environments is a major challenge. Various algorithms have been developed over years to address the problem of indoor positioning. One of the most cost effective choice for indoor positioning is based on received signal strength indicator (RSSI) using existing Wi-Fi networks in commercial and/or public areas. This solution is infrastructure-free and offers meter-range accuracy. In this paper, machine learning approaches including k-nearest neighbor (k-NN), a rules-based classifier (JRip), and random forest have been investigated to estimate the indoor location of a user or an object using RSSI based fingerprinting method. Experimental measurements were carried out using 1500 reference points with received RSSIs of 86 installed APs in the second floor of Centre for Engineering Innovation (CEI) building at the University of Windsor. The results indicate that the random forest classifier presents the best performance as compared to k-NN and JRip classifiers with positioning accuracy higher than 91%.

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