Multiplicatively weighted Voronoi-based sensor collaborative redeployment in software-defined wireless sensor networks

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Minghua Wang, Ran Ou, Yan Wang
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引用次数: 3

Abstract

Large-scale deployment of mobile wireless sensor networks has been widely used in some dangerous and hostile urban security surveillance scenarios. As a new network architecture, software-defined networks was introduced into wireless sensor networks to form a new software-defined wireless sensor networks to solve the problem of balanced large-scale deployment of sensor networks and simplify the complexity of network management. In this article, we first develop an original confident information coverage–based multiplicatively weighted Voronoi diagram through sensor clustering and sensor collaborative sensing. And then, we propose two sensor collaborative redeployment algorithms based on the novel confident information coverage–based multiplicatively weighted Voronoi diagram and software-defined wireless sensor networks architecture to provide high-confidence coverage and improve the coverage ratio. Finally, we demonstrate the superiority of the confident information coverage–based multiplicatively weighted Voronoi diagram and the effectiveness and efficiency of our proposed algorithms via a series of experiments.
软件定义无线传感器网络中基于乘加权voronoi的传感器协同重新部署
大规模部署的移动无线传感器网络已被广泛应用于一些危险和敌对的城市安全监控场景。作为一种新的网络架构,软件定义网络被引入无线传感器网络中,形成了一个新的软件定义无线传感器网络,以解决传感器网络均衡大规模部署的问题,简化网络管理的复杂性。在本文中,我们首先通过传感器聚类和传感器协作感知,开发了一个基于原始置信信息覆盖率的乘法加权Voronoi图。然后,我们提出了两种基于新的基于置信信息覆盖的乘法加权Voronoi图和软件定义的无线传感器网络架构的传感器协同重新部署算法,以提供高置信覆盖并提高覆盖率。最后,我们通过一系列实验证明了基于置信信息覆盖的乘法加权Voronoi图的优越性以及我们提出的算法的有效性和效率。
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
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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