An Indoor Positioning System Based on Wi-Fi for Energy Management in Smart Buildings

To offer indoor services to occupants in the context of smart buildings, it is necessary to consider information concerning to the identity and location of the occupants. This paper proposes an indoor positioning system (IPS) based on Wi-Fi fingerprint and K-nearest neighbors (KNN) method. The positioning of a mobile device (MD) using Wi-Fi technology involves online and offline phases. In this paper, the offline phase includes data collection in WiFi-based Nonintrusive SMS (WinSMS) context, while the online phase involves updating the structure of the collected radio map and online positioning. In online positioning, the proposed Weighted Differential Coordinate Probabilistic-KNN (WDCP-KNN) method based on probabilistic weighting of generalized Reference Points (RPs) and differential coordinates is used. Experiments in a complex indoor environment with real values indicate that the proposed method reduces the positioning error compared to other methods and is also comparable in terms of computational complexity.