On Studying Partial Coverage and Spatial Clustering Based on Jensen-Shannon Divergence in Sensor Networks

The idea of partial coverage is provided in this paper, which means that the distance among data trends gathered by neighbor sensors is so small that, in some period, we can cluster those sensors, and replace the cluster with certain sensor in this cluster to form the virtual sensor network topology. But adopting this approach, we need to solve two problems: 1) how to characterize the distance among data trends (rather than raw data) of different sensors; 2) based on the distance, how to form the cluster and use the virtual network to represent the whole sensor network within certain error range. For the first problem, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Finally, the performance of our approach is evaluated through simulation.