Alangari Someah, Obayya Marwa, Gaddah Abdulbaset, Yafoz Ayman, Alsini Raed, Alghushairy Omar, Ashour Ahmed, Motwakel Abdelwahed
Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia.
Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2022 Nov 4;22(21):8508. 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.
无线传感器网络（WSNs）是近年来发展起来的，用于支持多种应用，包括环境监测、交通控制、智能战场、家庭自动化等。无线传感器网络包括众多传感器，这些传感器可以分散在特定节点周围以实现计算过程。在无线传感器网络中，路由成为一项非常重要的任务，需要谨慎管理。路由算法的主要目的是在传感器节点（SNs）和基站（BS）之间发送数据以完成通信。一个好的路由协议应该能够适应并扩展到网络拓扑结构的变化。因此，一个可扩展的协议必须在工作量增加或网络规模扩大时运行良好。路由中的许多复杂性涉及安全性、能量消耗、可扩展性、连通性、节点部署和覆盖范围。本文介绍了一种基于天鹰座优化的小波变异路由（WMAO-EAR）协议用于无线通信。所提出的WMAO-EAR技术旨在在无线传感器网络中实现能量感知路由过程。为此，WMAO-EAR技术首先推导了用于将小波变异与天鹰座优化（AO）算法集成的WMAO算法。使用不同的约束条件，如延迟、能量、距离和安全性，推导了一个适应度函数。通过设置变异概率P，在开发和探索阶段过程之后的每个个体都有使用小波变异过程进行变异的概率。为了证明WMAO-EAR技术的增强性能，进行了全面的仿真分析。实验结果表明WMAO-EAR方法优于其他近期方法。
