Energy aware cluster-based routing in WSN using hybrid pelican-blue monkey optimization algorithm

As the nodes in wireless sensor networks (WSNs) are powered by batteries, energy efficiency is a major concern. Since communication consumes the most energy, efficient routing becomes an effective solution. A typical routing strategy is to use hierarchical clustering algorithms. In hierarchical routing schemes, a clustered arrangement of sensor nodes (SNs) is used to facilitate data merging and aggregation. The Cluster heads are in charge of obtaining data from the cluster’s SNs and gathering and delivering the data they receive at BS. These data are combined and pooled at the Cluster head (CH) level, resulting in considerable energy savings. However, there are certain reliability issues in cluster head selection (CHS) and optimal routing. To model a CHS and optimal routing in WSN, this work carries out the CHS using improved kernel Fuzzy C means clustering for selecting the CH by considering energy and distance. Then, optimal routing is done by deploying a novel optimization scheme named Customized Pelican with Blue Monkey Optimization (CP-BMO), which offers the optimal routes by considering trust and risk constraints. Also, the CP-BMO model holds better energy consumption which is 35.65%, 29.42%, 38.79%, 25.31%, 39.85%, 39.16%, 9.55%, 29.39%, 39.32% and 39.32% superior to ABO, RHSO, PELICAN, BMO, F-FLY, AEFA, ML-AEFA, ETERS, GA and QOBOA respectively. At last, the simulation outcomes confirm the developed approach’s effectiveness on PDR, network lifetime, etc.