The Performance of Simulated Annealing Algorithms for Wi-Fi Localization Using Google Indoor Map

Abstract

Wi-Fi localization is currently the most promising approach to build indoor localization systems. Especially after the recent release of Google Indoor Map for the Android system smart phones, many companies such like Skyhook and TCS etc. have flourished into the business of developing accurate Wi-Fi based indoor localization techniques due to their numerous applications. In this paper, we proposed an accurate Wi-Fi based indoor localization algorithm with the help of Google Indoor Map. The initial position of the mobile station (MS) is estimated according to the received signal strength (RSS) from the calibrated Wi-Fi access points (APs). Then, the position of the MS is allocated by using the simulated annealing (SA) algorithm to search for a better solution. During the searching process, the SA takes the indoor map structure into consideration and updates the cost function weights accordingly. This procedure makes sure the final estimation reaches to a better convergence. Extensive experimental results confirm that our solution is able to provide a much better result compared with other existing indoor localization techniques.