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.

Location Fingerprinting is a localization technique which uses a radio database as the key element for the positioning process. Performance of location fingerprinting systems has been analyzed empirically in some previous works. However, there is a lack of theoretical models that analyze and predict the fingerprints behavior under different physical situations. In this work we present an analysis to evaluate the distinctiveness of fingerprints in a local neighborhood, in the context of cellular radio fingerprinting. The model is developed assuming the presence of correlated log-normal shadowing. Based on the presented analysis, we conclude some general guidelines for design and implementation of radio databases in fingerprinting systems.

Location Fingerprinting is one of the radio positioning techniques which has been proposed in the field of Location Based Services (LBS). Considering the actual trends towards energy efficient systems and green networking, reducing the energy consumption has become a challenging issue in the context of fingerprinting systems. In this paper we present a clustering technique which aims to compress the radio database and hence to reduce the online processing load of the system. We propose a hierarchical clustering method which is applied in a concatenated location-radio signal space. Computer simulations have been conducted to evaluate the performance of the proposed technique in environments with different shadowing configurations. The results show that the proposed clustering technique outperforms the conventional griding method, and besides allows us to reduce the size of the database significantly while keeping an acceptable level of performance for the positioning system.