Enhanced Lifetime Maximization Using Metaheuristics-Based Unequal Clustering With Routing Protocol on Wireless Sensor Networks

Abstract

The Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors that monitor environmental or physical factors such as sound, pressure, or temperature, and pass the information directly through the network to a sink or central location. Clustering and routing are essential in WSNs to optimize their longevity, scalability, and efficiency. Clustering includes grouping Sensor Nodes (SN) into clusters, each containing a designated Cluster Head (CH) that transmits and aggregates data to the sink. This decreases the transmission overhead and conserves energy, as only the CHs interact over longer distances. On the other hand, routing is a procedure of defining the optimum path for the data transmission from SNs to the sink. Dynamic routing ensures timely and reliable data transmission while balancing the network load and reducing energy consumption. Metaheuristic algorithms play an important role in enhancing clustering and routing. WSNs can obtain better clustering and routing by leveraging metaheuristics, which results in extended operational lifetimes and improved network performance. Thus, the study presents a metaheuristic-based unequal clustering with routing protocol for lifetime maximization (MBCRP-LFTM) technique in WSN. The presented MBCRP-LFTM technique enhances the energy efficiency and lifespan of WSNs utilizing cluster-driven routing approaches. In the MBCRP-LFTM technique, an Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA) based clustering model offers an effective CH selection and unequal cluster construction. Besides, the IEMOA-based unequal clustering technique designs an objective function comprising multiple input metrics such as residual power level, CH balancing factor, and inter- and intra-cluster distance. Meanwhile, the MBCRP-LFTM technique designs a lotus effect optimization algorithm (LEA) based routing model to pick optimal routes to the sink. The LEA technique selects optimal routes using a fitness function comprising energy efficiency and link quality. An extensive series of simulations was conducted to show the efficiency of the MBCRP-LFTM method. The experimental outcomes reported that the MBCRP-LFTM model achieves improved performance compared to more recent techniques.