Framework for Optimizing Cluster Selection using Geo-assisted Movement Prediction

Due to the availability of satellite- and radio-based location systems in most new devices, it is possible to use geographical location of a node for network management and communication protocol optimization. It is a common belief that usage of location information can bring performance benefits. However, inaccuracy and delay in obtaining such information, together with an associated overhead, can have a negative impact. In this paper we have considered a particular case of usage of location information, namely for cluster selection in mobile networks and have analyzed the impact of inaccurate movement prediction and inaccurate location estimation on its performance. The proposed algorithm is compared with two reference algorithms: when a considered node associates with either the first discovered cluster or the nearest cluster. Evaluation shows significant performance benefits in terms of average connectivity time to a cluster head and reduced overhead in case of exact future trajectory prediction. Under more realistic scenarios where location estimation or movement prediction are not perfect, performance benefits are reduced. This emphasizes the need for good movement prediction module if location-based schemes should be implemented in products.