A low complexity clustering optimization algorithm for underwater sensor networks
Hao Zhang, Shilian Wang, Haixin Sun · 2016
Aiming at issues of load imbalance and low energy efficiency in the existing underwater sensor network clustering algorithm, a novel global optimal clustering algorithm with the low complexity and parallel processing is proposed. The algorithm is based on the basic idea of particle swarm optimization algorithm (PSO). After the binary initial code of the sensor nodes, the particle code is adjusted by mutation to satisfy the ideal number of cluster heads. In the iterative process, new particles are generated by random recombination of the surviving nodes. In order to screen out the optimal particle, three optimization objectives are considered in the particle fitness function, which are cluster head energy, cluster head load and cluster range. Simulation results show that the proposed algorithm can effectively improve the load balance and prolong the network lifetime.