An Energy-Efficient Cluster Routing Protocol for Wireless Networks Based on Blended Optimization Technique

Since the advent of low-cost and low-power sensors, Wireless Sensor Networks (WSNs) have exploded in popularity, leading to substantial advancements in wireless communication. However, since WSN nodes rely on batteries for power, they lose all independence after a set period of time. The lifespan of the network is impacted by the need to save energy. It is possible to extend a network's useful life while simultaneously decreasing its power consumption via the process of clustering. Using clustering, a group of similar sensors may be gathered in one place to collect and transmit data to a central hub (BS). When gathering and transmitting data, Cluster Head (CH) consumes more power. Energy consumption may be reduced and WSNs' lifespan increased with accurate CH detection. There is still a lot of effort to be done in developing a routing algorithm that takes into account the main difficulties of minimizing energy consumption and optimizing network longevity. In order to address these crucial problems in WSNs, this study introduces a clustered routing protocol that minimizes energy consumption by using a Mayfly-Aquila optimization (MFAO) method. To choose the best candidate for CH, the Mayfly algorithm is used. The good possible way from CH to BS is determined using the Aquila optimization algorithm and chosen at random. Based on the simulation findings, it is clear that the suggested technique may significantly reduce energy consumption compared to the current state-of-the-art methods.