Lightweight Indoor Localization System

Abstract

Indoor localization is an important topic for context aware applications. In particular, many applications for wireless devices can benefit from knowing the location of a user. Despite the huge effort from the research community to solve the localization problem, there is no widely accepted solution for localization in an indoor environment. In this paper we focus on constrained devices and propose an extremely lightweight indoor localization system that can be scaled to different devices, from smart phones to smart glasses and other devices. We devise a simple yet effective WiFi-based system with low computational complexity, which does not need any additional special infrastructure nor map or an internet connection. Our system relies on IEEE 802.11 Received Signal Strength Indicator (RSSI) values and a dead reckoning module to collect walking trajectories which are further clustered and compressed to build a sensor map. The key novelty of our work is a merging algorithm that can fuse multiple sensor maps. We evaluate our system in a real world scenario and we show that using the map produced by our merging algorithm we achieve room-level accuracy. Our system is also comparable to state of the art systems, despite the lightweight approach.