Energy aware clustering and routing using cauchy operator based gorilla troops optimization for WBAN

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-05-01 DOI:10.1016/j.compeleceng.2025.110408

SankaraSrinivasaRao Illapu , Padmaja Malarowthu , Aswini Mula , J.N.V.R. Swarup Kumar , Ramjee Maddula

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引用次数: 0

Abstract

Wireless Body Area Network (WBAN) offers high quality services to its users via different applications of healthcare, fitness and sports, however it faces issue in energy efficiency due to limited battery capacity. To address this issue, the Energy Aware Clustering and Routing (EACR) using Cauchy Operator based Gorilla Troops Optimization (COGTO) is proposed to increase life expectancy and to ensure reliable data delivery. COGTO improves the searching efficiency by reducing the step size and avoids local optima risk by enhancing the exploitation search via the integration of Cauchy Inverse Cumulative Distribution (CICD) operator. First, optimum Relay Nodes (RNs) are identified by optimizing COGTO based on the residual energy, interspace between sensors, interspace between RN & Base Station (BS), node degree and node centrality. Next, the energy and distance are used to optimize the multi path routing using COGTO. Additionally, the Time Division Multiple Access (TDMA) helps ensure node scheduling in the transmission phase. The existing methods of DECR, NEEMA, EEART and ESTEEM are used for comparison with the EACR-COGTO. The energy expenditure percentage of EACR-COGTO at 800 rounds is 3 % which is lesser in relation to the DECR whose percentage is 81 %.

查看原文本刊更多论文

基于柯西算子的WBAN猩猩群优化的能量感知聚类和路由

无线体域网络（Wireless Body Area Network， WBAN）通过医疗保健、健身和运动等不同的应用为用户提供高质量的服务，但由于电池容量有限，它面临着能效问题。为了解决这一问题，提出了基于柯西算子的大猩猩部队优化（COGTO）的能量感知聚类和路由（EACR），以提高预期寿命并确保可靠的数据传输。COGTO通过减小步长来提高搜索效率，并通过集成柯西逆累积分布算子来增强挖掘搜索，避免了局部最优风险。首先，通过基于剩余能量、传感器间间距、中继节点间间距和中继节点间间距的COGTO优化，确定最优中继节点；基站（BS），节点度和节点中心性。然后，利用能量和距离对多路径路由进行COGTO优化。另外，TDMA （Time Division Multiple Access）可以保证传输阶段的节点调度。采用现有的DECR、NEEMA、EEART和ESTEEM方法与EACR-COGTO进行比较。800发时，EACR-COGTO的能量消耗百分比为3%，比DECR的81%要低。

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

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.