为了提高无线传感器网络的性能,提出了一种新的认证和访问调度方案

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
K. Baskar, P. Vijayalakshmi, K. Muthumanickam, A. Arthi
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引用次数: 0

摘要

无线传感器网络(WSN)是一种特别适用于对基础设施和资源起重要作用的场所的网络。此外,WSN中的节点本质上是自治的。无线传感器网络可以解决各种实时问题,如智能医疗、智能办公、智能能源、智能家居等。随着能源成为此类网络的稀缺资源之一,针对身份验证的攻击帮助验证传感器节点的合法性变得至关重要。这种攻击耗尽了当前连接到WSN的节点的能力,从而缩短了它们的生命周期。本文提出了一种区域节点认证技术和最优数据访问调度,以提高服务质量和网络生命周期提供数据交付。对不同WSN拓扑的仿真结果强调了我们的方法优于现有解决方案的主张,并证明了在发现具有最佳工作负载的合法传感器节点方面是有效的。该方法在提高网络生存期、效率和吞吐量的同时,还通过集成节点认证加强了WSN的安全措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel authentication and access scheduling scheme to improve the performance of WSN
Wireless sensor network (WSN) is a kind of network specifically suitable for place where infrastructure and resources are playing a vital role. Moreover, nodes in a WSN are autonomous in nature. WSNs can be able to solve various real-time problems and issues like smart healthcare, smart office, smart energy, smart home, etc. As energy becomes one of the scarce supplies for this kind of network, attacks against authentication help to validate the legitimacy of sensor nodes become foremost important. Such attacks exhaust the power of nodes that are currently connected to a WSN, thereby reducing their lifetime. In this article, a zonal node authentication technique as well as optimal data access scheduling that renders data deliverance with improved quality of service and network lifetime is proposed. The results obtained from simulation for diverse WSN topologies accentuate the claim of our method over the existing solutions and demonstrate to be efficient in discovering legitimate sensor nodes with the optimal workload. Besides improved network lifetime, efficiency, and throughput, the proposed method also reinforces the security measures of the WSN by integrating node authentication.
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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