K-Centers Mean-shift Reverse Mean-shift clustering algorithm over heterogeneous wireless sensor networks

A clustering algorithm K-centers mean-shift reverse mean-shift for heterogeneous wireless sensor networks is presented in this paper, addressing the empty cluster problem as a key issue. Many clustering algorithms for sensor networks have empty cluster problems due to random deployment, which causes resource and cost inefficiencies. Our algorithm calculates the mean-shift of sensor nodes and the reverse mean-shift of cluster heads to iteratively move cluster heads closer to the sensor nodes´ density and away from cluster heads´ density. This helps cluster heads better accommodate the distribution of sensors. Our proposed K-Centers Mean-shift Reverse Mean-shift algorithm decreases the number of empty clusters dramatically, and it also balances the sizes of clusters more evenly.