Wavelet Mutation with Aquila Optimization-Based Routing Protocol for Energy-Aware Wireless Communication

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (21), p.8508
• Main Authors: Alangari, Someah, Obayya, Marwa, Gaddah, Abdulbaset, Yafoz, Ayman, Alsini, Raed, Alghushairy, Omar, Ashour, Ahmed, Motwakel, Abdelwahed
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Aquila optimizer; Battlefields; Data transmission; Energy consumption; Energy efficiency; Energy management; Environmental monitoring; Exploitation; Internet of Things; Mutation; Network topologies; Optimization; Routing (telecommunications); routing protocol; Sensors; Simulation methods; Traffic control; Traffic regulations; wavelet mutation; wireless communication; Wireless communications; Wireless networks; Wireless sensor networks; Wireless telecommunications equipment
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218508

Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a specific node to achieve the computing process. In WSNs, routing becomes a very significant task that should be managed prudently. The main purpose of a routing algorithm is to send data between sensor nodes (SNs) and base stations (BS) to accomplish communication. A good routing protocol should be adaptive and scalable to the variations in network topologies. Therefore, a scalable protocol has to execute well when the workload increases or the network grows larger. Many complexities in routing involve security, energy consumption, scalability, connectivity, node deployment, and coverage. This article introduces a wavelet mutation with Aquila optimization-based routing (WMAO-EAR) protocol for wireless communication. The presented WMAO-EAR technique aims to accomplish an energy-aware routing process in WSNs. To do this, the WMAO-EAR technique initially derives the WMAO algorithm for the integration of wavelet mutation with the Aquila optimization (AO) algorithm. A fitness function is derived using distinct constraints, such as delay, energy, distance, and security. By setting a mutation probability P, every individual next to the exploitation and exploration phase process has the probability of mutation using the wavelet mutation process. For demonstrating the enhanced performance of the WMAO-EAR technique, a comprehensive simulation analysis is made. The experimental outcomes establish the betterment of the WMAO-EAR method over other recent approaches.