Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.