Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings

Abstract

In mobile-centric indoor positioning, having a small databases to transfer from the network side to the mobile is of utmost importance. For scalable and low-complexity solutions, various clustering algorithms have been suggested in the literature, either in coordinates or 3D dimension or in the Access Points or Received Signal Strength (RSS) dimension. Typically, the two dimensions were investigated separately. This paper offers a comparative analysis between different clustering methods, together with a novel metric, called the Penalized Logarithmic Gaussian Distance metric which can boost the performance of the clustering. The results are compared based on real-field measurement data in two different multi-floor buildings and they are given in terms of estimation errors, floor detection probabilities and complexity. It is shown that the proposed metric enhances the performance of both 3D and RSS clustering and that the RSS clustering has lower complexity but worse performance than the 3D clustering. We are also providing in open-access the measurement data together with the Python-based implementation of the algorithms to serve as future benchmarks for indoor positioning studies.