物联网环境下基于能效集群的资源优化管理

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
J. V. Anchitaalagammai, T. Jayasankar, P. Selvaraj, Mohamed Yacin Sikkandar, M. Zakarya, M. Elhoseny, K. Shankar
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引用次数: 2

摘要

物联网(IoT)是一场重新定义现代通信和计算的技术革命。物联网通常是指通过无线网络连接并通过互联网进行通信的设备网络。在设计物联网设备时,资源管理,特别是能源管理是一个关键问题。一些研究报告称,集群和路由是物联网环境中资源优化管理的节能解决方案。从这个角度来看,本研究设计了一种新的高效节能的基于聚类的资源管理路由技术,即物联网环境下的EECBRM。提出的EECBRM模型分为三个阶段,即基于模糊逻辑的聚类、基于LWOT的路由优化和集群维护阶段。通过一系列实验对提出的EECBRM模型进行了验证,并从几个方面对结果进行了验证。将EECBRM模型与现有方法在能效、时延、数据传输次数、网络寿命等方面进行比较。仿真结果表明,与其他方法相比,EECBRM模型取得了很好的效果。由此证明了所提模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment
: Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based clustering, Lion Whale Optimization with Tumbling (LWOT)-based routing and cluster maintenance phase. The proposed EECBRM model was validated through a series of experiments and the results were verified under several aspects. EECBRM model was compared with existing methods in terms of energy efficiency, delay, number of data transmission, and network lifetime. When simulated, in comparison with other methods, EECBRM model yielded excellent results in a significant manner. Thus, the efficiency of the proposed model is established.
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来源期刊
Cmc-computers Materials & Continua
Cmc-computers Materials & Continua 工程技术-材料科学:综合
CiteScore
5.30
自引率
19.40%
发文量
345
审稿时长
1 months
期刊介绍: This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials. Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.
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