A novel Wi-Fi AP localization method using Monte Carlo path-loss model fitting simulation

Wi-Fi-based localization is one of the most promising technologies for indoor location-based services. However, it is still a difficult task to construct a positioning database to provide high accuracy and vast coverage, especially in probabilistic-based algorithms such as fingerprint-based localization. On the other hand, positioning methods that use the location database of the positioning infrastructure, such as the weighted centroid method, can support terminal-based localization due to lower computational complexity and small database size. Terminal-based positioning has the advantages not only of protecting personal location information, but also of maintaining the network topology among the terminals. The only weak point for positioning database-based methods is that the accurate location of the infrastructure should be known in advance. In this paper, we propose a novel algorithm to estimate the location of infrastructure, especially for Wi-Fi access points. We developed and employed a smartphone application to collect Wi-Fi signals by just walking around the test area. Then, the locations of Wi-Fi access points are estimated efficiently and accurately by selection of the maximum likelihood position that has the most similar path-loss model corresponding to the signal acquisition points. The estimated locations are processed as infrastructure database to support terminal-based positioning. The simulation and experiment result validate the feasibility of the proposed algorithm. The estimated location of access points is within 10 m accuracy in most cases and also the terminal-based positioning results achieve competitive performance comparing with other positioning methods.