RSS Ranging Based Wi-Fi Localization for Unknown Path Loss Exponent

Localization of mobile phones is important to location-based mobile services, but achieving good location estimation of mobile phones is difficult especially in environment whose path loss exponent is unknown. In this paper, we present a Wi-Fi localization solution specifically designed for dense WLANs with unknown path loss exponent. In order to leverage between the computational cost and localization accuracy, our solution establishes a neighbor selection scheme based on the Voronoi diagram to identify a subset of Access Points (APs) to participate in localization. It considers the identified subset of APs and a mobile phone to be located as a mass-spring system. Provided with information of known coordinates of APs, the solution estimates the path loss exponent of the physical environment, infers inter-distances between APs and the mobile phone from Wi-Fi signals received, and implements spring relaxation algorithm to approximate the geographical location of the mobile phone, where this location estimation is fed back to refine the estimated exponent iteratively. Extensive simulation results confirm that our solution is able to provide location estimation with an attractive average accuracy of below 2 m in a typical Wi-Fi setup.