Radio Database Compression for Accurate Energy-Efficient Localization in Fingerprinting Systems
Location fingerprinting is a positioning method that exploits the already existing infrastructures such as cellular networks or WLANs. Regarding the recent demand for energy efficient networks and the emergence of issues like green networking, we propose a clustering technique to compress the radio database in the context of cellular fingerprinting systems. The aim of the proposed technique is to reduce the computation cost and transmission load in the mobile-based implementations. The presented method may be called Block-based Weighted Clustering (BWC) technique, which is applied in a concatenated location-radio signal space, and attributes different weight factors to the location and radio components. Computer simulations and real experiments have been conducted to evaluate the performance of our proposed technique in the context of a GSM network. The obtained results confirm the efficiency of the BWC technique, and show that it improves the performance of standard k-means and hierarchical clustering methods.
wireless systems ubiquitous computing location-dependent and sensitive machine learning