Energy Optimization of Wireless Sensor Networks Based on Hybrid Intelligent Algorithms

In order to reduce energy consumption during the operation of wireless sensor networks and achieve energy balance among all nodes, a wireless sensor network energy optimization method (GFE) based on the greater cane rat algorithm (GCRA) and the four vector intelligent metaheuristic for data optimization (FVIM) is proposed. This method first uses a low-power adaptive clustering layered protocol (LEACH) to bring the wireless sensor network into a stable phase. Afterwards, a mixed strategy of greater cane rat algorithm and four vector intelligent metaheuristic for data optimization are used to select cluster head nodes. After determining the cluster head node, the maximum number of inter cluster nodes connected to it is limited. Finally, based on the signal strength received from the cluster head nodes, non cluster head nodes select the clusters they want to join. Simulation experiments show that compared with several routing protocols such as LEACH, LEACH-C, and LEACH-D, GFE can significantly reduce energy consumption, lower node death rates, better balance WSN energy consumption, and extend its lifecycle.