Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a parallel energy-efficient coverage optimization mechanism to optimize the positions of mobile sensor nodes based on maximum entropy clustering in large-scale wireless sensor networks. According to the models of coverage and energy, stationary nodes are partitioned into clusters by maximum entropy clustering. After identifying the boundary node of each cluster, the sensing area is divided for parallel optimization. A numerical algorithm is adopted to calculate the coverage metric of each cluster, while the lowest cost paths of the inner cluster are used to define the energy metric in which Dijkstra’s algorithm is utilized. Then cluster heads are assigned to perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric where a weight coefficient between the two metrics is employed to achieve a tradeoff between coverage area and energy efficiency. Simulations of the optimization mechanism and a target tracking application verify that coverage performance can be guaranteed by choosing a proper weight coefficient for each cluster and energy efficiency is enhanced by parallel energy-efficient optimization.