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Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics.
Anusha et al. (Sun,) studied this question.