An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92$j$), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.