WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2024-10-09 DOI:10.1016/j.comnet.2024.110845

Navneet Kumar , Karan Singh , Jaime Lloret

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引用次数: 0

Abstract

Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.

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WAOA：用于无线传感器网络高能效路由选择的鲸蚂蚁混合优化算法

无线传感器网络（WSN）对于从远程环境中收集数据至关重要。然而，由于传感器节点的能源资源有限，高能效路由成为 WSN 成功运行的关键问题。为了解决这些问题，集群和路由选择是 WSN 的基本任务；集群旨在将传感器节点组织成群或簇，以最大限度地减少能源消耗，延长网络的使用寿命。另一方面，路由涉及确定从源节点向目的节点传输数据的最佳路径。尽管如此，目前的高能效路由问题是一个 NP 难题，需要在能量和整体网络性能之间进行权衡。本文针对 WSN 中的高能效路由问题提出了一种鲸蚂蚁混合优化算法（WAOA）。所提出的 WAOA 利用鲸鱼优化算法（WOA）在预定义的搜索空间内寻找合适的簇头，而蚁群优化算法（ACO）则在其预定义的空间内搜索从源簇传感器到簇头的最佳路径。采用线性规划结构来提出簇头选择和最佳路径搜索的优化问题。性能分析表明，就网络寿命而言，所提出的 WAOA 比 MOORP、MMABC 和 AZEBR 分别好 5.78 %、16.11 % 和 18.52 %。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.