SDERP: Stable-aware differential evolution-based routing protocol for mobile wireless networks

作     者：Mekkaoui, Abdelkader Smail, Omar Rebbah, Mohammed 

作者机构：Univ Mustapha Stambouli Fac Exact Sci Comp Sci Dept Mascara Algeria 

出 版 物：《INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS》 (Int J Commun Syst)

年 卷 期：2025年第38卷第4期

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：differential evolution algorithm energy constraint MANET multipath routing protocol stability constraint 

摘      要：A mobile ad hoc network (MANET) is a network with non-wired communications and non-centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real-time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable-aware differential evolution-based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV-EE, FT-AORP, EE-LB-AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end-to-end delay reduced by up to 16%. In this paper, we propose a new routing solution named stable-aware differential evolution-based routing protocol (SDERP) for mobile ad hoc networks (MANETs). Our solution used a bio-inspired method derived from the family of evolutionary algorithms known as differential evolution. A fitness function is integrated to select the best paths. This fitness function is based on energy constraint and link stability. image