The Quasi-Oppositional-Based Learning Aquila Optimization Routing Protocol in Energy-Aware Wireless Communication

Wireless Sensor Networks (WSN) is the extensive intelligent data system which combines the data collection, transmission as well as processing. In WSN, the routing becomes the crucial task that should controlled judiciously and the major objective of the routing approach is to transmit the data among Sensor Nodes (SN) as well as Base Stations (BS) to achieve communication. The energy consumption, scalability, deployment of the node are the difficulties in routing protocol. In this research, Quasi-Oppositional-based Learning Aquila Optimization (QOBL-AO) based routing protocol is proposed for the wireless communication. The objective of the proposed QOBL-AO approach is to achieve the Energy-Aware Routing (EAR) procedure of routing in Wireless Communication. The fitness function is obtained by utilizing the well-defined limitation named energy, delay, security as well as distance. The proposed QOBL-AO method attains the better results and it achieves an energy consumption of 0.35, PDR of 98.77%, End-To-End Delay (ETED) of 3.25 and network time of 5349 when compared to the existing methods such as Wavelet Mutation with AO-Based Energy Aware Routing (WMAO-EAR) and Metaheuristics Cluster-based Routing Technique for Energy-Efficient WSN (MHCRT-EEWSN).