An empirical study of indoor localization algorithms with densely deployed APs

Many indoor positioning algorithms have been proposed in the last decade, most of which are based on WiFi RSS fingerprints. However, the environment has changed dramatically since the original algorithms using only a few Access Points (APs). A typical building with densely deployed APs might contain hundreds of APs. The explosive growth of the number of APs introduces new challenges to these WiFi-based localization algorithms. This paper presents an empirical study of WiFi fingerprint-based indoor localization algorithms in a real-world environment with hundreds of APs. Our study aims to answer several important research questions regarding the influence of the number of APs, time variance and device variance. The study implements four existing algorithms and also proposes a new algorithm called LCS that is designed specifically for an AP-intensive environment. We compare the localization accuracy of different algorithms with different variances in the experimental results, which shows that the proposed LCS algorithm is able to efficiently resist diverse variances in an AP-intensive setup.