Comparison of supervised learning-based indoor localization techniques for smart building applications

Abstract

Smart buildings involve modern applications of the Internet of Things (IoT). Intelligent buildings could include applications based on indoor localization, such as tracking the real-time location of humans inside the building using sensors. Mobile sensor nodes can emit electromagnetic signals in an ambient sensor network, and fixed sensors in the same network can detect the Received Signal Strength (RSS) from its mobile sensor nodes. However, many works exist for RSS-based indoor localization that use deterministic algorithms. It's complicated to suggest a generated mechanism for any indoor localization application due to the fluctuation of RSSI values. This paper has investigated supervised machine learning algorithms to obtain the accurate location of an object with the aid of Received Signal Strengths Indicator (RSSI) values measured through sensors. An available RSSI data set was trained using multiple supervised learning algorithms to predict the location and their average algorithm errors were compared.