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

Abstract

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.

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

“…The improved versions of AO can handle a large range of difficult real-world optimization problems better than the standard AO. The strategies used in AO are hybridization with NIOAs [22,23], oppositional-based learning [24], chaotic sequence [25], Levy flight-based strategy [26], Gauss map and crisscross operator [27], Niche Thought with Dispersed Chaotic Swarm [28], random learning mechanism and Nelder-Mead Simplex Search [29], wavelet mutation [30], Weighted Adaptive Searching Technique [31], Binay AO [32], and multi-objective AO [33].…”

A Comprehensive Survey on Aquila Optimizer

“…In order to improve wireless communication, Alangari et al [ 80 ] presented a wavelet mutation AO-based routing algorithm. One goal of the proposed method was to enable energy-aware routing in WSNs.…”

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering

“…These algorithms offer the features of self-organization, self-adaptation, and self-learning, and they have been widely applied in various domains, such as biology [17,18], feature selection [19], optimization computing [20], image classification [21], and artificial intelligence [22,23]. map and crisscross operator [51], random learning mechanism and Nelder-Mead simplex search [52], wavelet mutation [53], weighted adaptive searching technique [54], binary AO [55], etc. A fine literature examination of the AO algorithm and its application is offered in reference [56].…”