基于PSO和最小覆盖生成树聚类WSN的移动数据采集

Q4 Business, Management and Accounting International Journal of Mobile Network Design and Innovation Pub Date : 2018-06-18 DOI:10.1504/IJMNDI.2018.10013510
K. Vijayalakshmi, J. Manickam
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引用次数: 1

摘要

在无线传感器网络(WSN)中,通常离接收器或基站最近的传感器往往比其他传感器更快地耗尽电池能量,并在接收器周围产生能量洞。为了克服这一问题,提出了一种基于粒子群算法和最小覆盖生成树(MDG-PSO-MCST)的移动数据采集方法。在这种技术中,多个传感器被排列成簇。在集群位置部署2台多天线sencar,采用空分多址(SDMA)技术高效采集数据。采用粒子群优化(PSO)技术,根据节点的连通性和节点度、节点的兼容性以及相邻两簇传感器之间的距离参数选择中间数据采集的锚点。使用MCST算法对每个senars在选定锚点之间进行访问。仿真结果表明,该方法使网络的能量消耗和时延降到最低,提高了网络效率。
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Mobile data gathering using PSO and minimum covering spanning tree clustered WSN
In wireless sensor networks (WSN), generally sensors closest to the sink or base station tend to deplete their battery energy faster than other sensors and create an energy hole around the sink. In order to overcome this problem, a mobile data gathering using PSO and minimum covering spanning tree (MDG-PSO-MCST) is proposed. In this technique, multiple sensors are arranged to form clusters. Two SenCars with multiple antennas are deployed in the cluster location and space division multiple access (SDMA) technique is applied for data gathering with energy efficiency. The anchor points are selected for intermediate data collection based on the connectivity and node degree, node compatibility and the distance between the sensors of two adjacent clusters parameters using particle swarm optimisation (PSO) technique. The visiting tour of each Sencars among the selected anchors is performed using the MCST algorithm. By simulation results, we show that the proposed technique minimises the energy consumption and delay, and enhances the network efficiency.
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来源期刊
International Journal of Mobile Network Design and Innovation
International Journal of Mobile Network Design and Innovation Business, Management and Accounting-Management Information Systems
CiteScore
0.30
自引率
0.00%
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0
期刊介绍: The IJMNDI addresses the state-of-the-art in computerisation for the deployment and operation of current and future wireless networks. Following the trend in many other engineering disciplines, intelligent and automatic computer software has become the critical factor for obtaining high performance network solutions that meet the objectives of both the network subscriber and operator. Characteristically, high performance and innovative techniques are required to address computationally intensive radio engineering planning problems while providing optimised solutions and knowledge which will enhance the deployment and operation of expensive wireless resources.
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