Optimal parameter setting for indoor localization via big data analysis

Indoor localization remains a hot topic and receives tremendous research efforts during the last few decades. While most previous efforts focus on the designing issue, little effort has been paid to the impact of different environmental parameters on the system performance. To this end, we present an extensive empirical study with real-world experiments to provide sufficient data for analysis. By seizing the fundamental relationship between a given scenario and environmental parameters, we show that the optimal parameter settings can be achieved for the scenario regardless of the localization algorithms.