Estimating Indoor Zone-Level Location Using Wi-Fi RSSI Fingerprinting Based on Fuzzy Inference System

Recent advances in communication and mobile technology have becoming the Wireless Local Area Networks using Wi-Fi more ubiquitous. These networks are providing a potential infrastructure that enable the location of users wearing wireless devices indoor, where GPS (Global Position System) signal is weak or is not available. Trilateration and Fingerprinting are the two conventional and general methods used for calculating location using Wi-Fi RSSI (received signal strength indicator) data. This work presents an alternative method to people indoor localization using the Wi-Fi RSSI Fingerprinting based on Fuzzy Inference Systems estimator in a wearable wristband. Wi-Fi Fingerprinting creates a radio map of a given area based on the RSSI data from several access points (APs) and generates a set of RSSI data for a given zone location. Then the Fuzzy Inference System is trained with that set of data and upon the system is trained, live RSSI values are introduced to the Fuzzy Inference System to generate an estimation of the user zone location.