Graph-Neural-Network-Based WiFi Indoor Localization System With Access Point Selection

With the popularity of mobile devices and the increasing demand for indoor localization services, the localization of indoor mobile users is becoming more and more popular. However, many existing methods of building the radio map require collecting the received signal strength (RSS) of a large number of access points (APs), which causes high-hardware costs and large storage. Additionally, the instability of RSS in the actual environment will have a detrimental influence on indoor localization, and the large multistory buildings will also create new challenges. In this article, we propose a localization model with the combination of the AP selection network and the graph-based location mapping network. This model selects the optimal APs through the AP selection network and reduces the number of required APs. Then, the connection mode of the selected APs is used to construct a graph describing the location of reference points and users. Besides, the graph neural networks are used to extract graph-level representation, effectively capturing the misaligned features. Moreover, evaluated on the UJIIndoorLoc and UTSIndoorLoc data sets, the proposed method could not only reduce the number of required APs while ensuring localization performance but also outperform several state-of-the-art methods.