Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer

Wireless sensor network (WSN), plays an increasingly important role in information collection. In this paper, firstly, in order to adapt to the actual conditions, the communication process of the nodes energy is limited, and a three-stage energy heterogeneous network model is designed. Secondly, for the convergence node frequent task forwarding and complex cluster first-round energy consumption, by combining the optimal number of cluster heads with the gray wolf optimization algorithm, a new fitness function is designed that integrates the remaining energy of the nodes and the distance from the nodes to the base station. In addition, an improved iterative factor is introduced to enhance the ability of local search in cluster head selection, so as to improve the accuracy of cluster head search. Finally, the simulation results show that the proposed method extends the lifetime of the network 50%, reduces the process of energy consumption, and improves the throughput of network data 30%.