NSGA-II based optimal Wi-Fi access point placement for indoor positioning: A BIM-based RSS prediction

Abstract

One of the most basic systems required in smart cities are indoor positioning systems that allow positioning of people and objects inside buildings to provide them with suitable location-based services. Due to the existence of Wi-Fi technology infrastructure in most buildings and the fact that most mobile devices are equipped with this technology, indoor positioning systems based on Wi-Fi technology and fingerprinting method are quite popular. Therefore, the optimal placement of Wi-Fi Access Points (APs) is important to maximize the accuracy of indoor positioning. This paper presents a method to generate virtual fingerprints inside buildings by predicting Wi-Fi RSS values using integration of BIM and signal propagation models. The proposed method allows the optimization of the spatial distribution and number of Wi-Fi APs. The experiments reveal improvement in signal propagation modelling to measure RSS, efficient RSS fingerprinting-based IPS in the offline phase, decrease in the number of measurement efforts, and optimization of the spatial distribution of Wi-Fi APs, that finally was resulted in high overall accuracy of the indoor positioning.