SoLoc: Self-organizing indoor localization for unstructured and dynamic environments

Self-organization is critical to enable novel indoor Location-Based Services (LBSs) for users and businesses in large, complex and unstructured buildings. Inspired by high densities of smartphones in public indoor spaces, in this paper we propose a self-organizing indoor localization approach that allows the use of available WiFi Access Points (APs) and iBeacons in the area to improve location accuracy and environment adaptability. Our approach is based on a semi-anchored localization that estimates the unknown location of smartphones, given known-location anchors (APs) and unknown-location anchors (iBeacons). We exploit the capabilities of Levenberg-Marquardt optimization algorithm to accurately estimate smartphone locations in realtime, in contrast to fingerprinting methods that require a tedious off-line training phase. Moreover, we use a clustering method based on the Received Signal Strength (RSS) values to obtain the initial estimated location for the optimization. We evaluate our approach using available APs and non-coordinated iBeacons in a large building to localize smartphones. The experimental results confirm that our self-organizing approach not only effortlessly estimates the position of mobile devices, but also provides a higher localization accuracy than other widely used approaches such as extant fingerprinting techniques for both scenarios, with and without iBeacons.