移动 Ad hoc 网络中的数据聚合和路由选择:自适应塔斯马尼亚魔鬼优化简介

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kingston Albert Dhas Y, S. Jerine
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引用次数: 0

摘要

移动特设局域网(MANET)是指构成特设网络的移动无线节点。由于节点的移动性,网络拓扑结构可能会定期发生变化。每个节点都充当路由器,在整个网络中传递流量,并自行构建网络的基础设施。城域网路由协议需要能够存储路由信息,并根据网络拓扑的变化进行调整,以便将数据包转发到目的地。虽然移动网络是城域网路由技术的主要应用领域,但拥有固定节点且没有网络基础设施的网络也能从路由技术中受益。本文提出了一种基于自适应塔斯马尼亚魔鬼优化(SATDO)的城域网路由和数据聚合技术。该过程的第一步是聚类,根据能量、距离、延迟和增强的安全条件风险系数评估等一系列限制条件选择最佳簇头。本研究提出了 SATDO 算法来实现这一最优选择。在聚类过程之后,将通过本研究中引入的相同 SATDO 算法优化路由选择。最后,还采用了一种基于内核最小均方差的改进型数据聚合方法,以避免数据冗余。通过不同的性能指标,我们将建议的路由模型与传统算法的效率进行了对比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization
Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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