Hotspot Ranking Based Indoor Mapping and Mobility Analysis Using Crowdsourced Wi-Fi Signal

In this paper, we propose a new hotspot ranking-based indoor mapping and mobility analysis approach based on the sporadically collected crowdsourced Wi-Fi received signal strength (RSS) data. This approach aims to construct the indoor mapping, as well as achieve the mobility analysis of the users following their daily motion patterns in target environment. First, we perform the wavelet analysis with respect to each RSS sequence to mitigate the noise interference to some extent. Second, we develop a new multidimensional scaling approach to map each RSS data into a linear one in the 2-D signal space, which is followed by the density clustering approach to merge the linear ones into different clusters based on the spatial correlation property. Finally, we construct the indoor mapping from the signal into physical spaces by the concept of hotspot ranking order, as well as the transfer relations among different RSS clusters and different physical sub-areas. The experimental results demonstrate that the proposed approach can achieve the superior performance in terms of indoor mapping and mobility analysis in an unknown indoor environment.