LAUSR

Acquiring the locations of WiFi access points (APs) not only plays a vital role in various WiFi related applications, such as localization, security and AP deployment, but also inspires the emergence of novel applications. Thus, many efforts have been invested in studying AP localization. Most existing studies adopt the well-known lognormal distance path loss (LDPL) model, which only accounts for large-scale fading but ignores small-scale fading induced by multipath propagation. In this paper, we tackle the problem of AP localization based on the Rayleigh lognormal model which characterizes the influence of both large-scale fading and small-scale fading. In addition, particle filtering is used to sequentially narrow the scope of possible locations of the target AP. In order to label the locations of received signal strength (RSS) measurements in real time, manual configuration or certain indoor and outdoor localization techniques, including GPS, pedestrian dead reckoning (PDR) and WiFi fingerprinting, can be leveraged. Moreover, due to the bias caused by unavailable or inaccurate state space, a particle area dynamic adjustment strategy (PADAS) is designed to improve the AP localization accuracy. Extensive experiments were carried out in typical indoor and outdoor scenarios. It is shown that, if accurate location labels are available, the proposed method is able to achieve an average localization accuracy of 2.48 m indoors and 4.21 m outdoors. In comparison with the LDPL based solutions, the proposed method improves by 23.82% – 70.38% indoors and 14.13% – 35.94% outdoors; more importantly, the proposed method enhanced by using PDR, PADAS, GPS and WiFi fingerprinting can achieve localization accuracy comparable to that with accurate location labels. In addition, an Android application (APP) was developed to demonstrate the feasibility of the proposed algorithm on smartphones.