Machine Learning for Location Prediction Using RSSI On Wi-Fi 2.4 GHz Frequency Band

For decades, the determination of an object's location has been implemented utilizing different technologies. Despite the Global Positioning System (GPS ) providing scalable, efficient, and cost-effective location services, the satellite emitted signals cannot be exploited indoors to determine the location effectively. In contrast to GPS, which is a cost-effective localization technology for outdoor locations, several technologies have been studied for indoor localization. These include Wireless Fidelity (Wi-Fi), Bluetooth Low Energy (BLE), and Received Signal Strength Indicator (RSSI) etc. This paper presents an enhanced method of using RSSI as a mean to determine an object's location by applying some Machine Learning (ML) concepts. The binary classification is defined by considering the adjacency of the coordinates denoting object's locations. The proposed features were tested empirically via multiple classifiers that achieved a maximum of 96% accuracy.