Fuzzy Decision Tree with Fuzzy Particle Swarm Optimization Clustering for Locating Users in an Indoor Environment Using Wireless Signal Strength

Wireless networks play a vital role towards elevating great interest among researchers in developing a smart indoor environment using the handheld devices of the users. Tracing the user’s location in an indoor environment can enable several services for automating several activities like automating switch on/off the room lights, air conditioning, etc., which makes the environment smart. In this paper, we propose to apply fuzzy decision tree which utilizes the fuzzy memberships generated from fuzzy particle swarm optimization clustering technique for the user localization application. Here, we consider the user localization problem as a pattern classification problem, where based on the signal strengths received from mobile devices, the location of the user is predicted as in conference room, kitchen area, sports hall, and work area in an indoor environment. The dataset of wireless signal strength is taken from the physical facility at our research facility. From the results obtained, we observe that the proposed algorithm has given highly encouraging results towards user localization.