TELIL: A Trilateration and Edge Learning based Indoor Localization Technique for Emergency Scenarios

When deploying localization techniques using wireless devices with only less amount of system resources, indoor WiFi localization is a difficult task. GPS (Global Positioning System) can provide a fairly accurate position of the user, but it is usually not used indoors because of the degradation of signals due to the structure of the building. Due to the unsteady nature of Received Signal Strength Indicator (RSSI), accurate results are hard to generate, in spite of the usage of various alternative WiFi localization techniques. We have attempted to improve the accuracy of localizing the user by implementing wall detection and pixel calculation to trilateration technique in smartphones. The users having WiFi enabled devices like a smartphone can locate their position and request for help in case of emergency with the implementation of WiFi localization on mobile devices. This study proves that the selection of access points used for generating the position of the user substantially affects the accuracy.