系统仿真学报 ›› 2018, Vol. 30 ›› Issue (8): 2908-2917.doi: 10.16182/j.issn1004731x.joss.201808012

• 仿真建模理论与方法 • 上一篇    下一篇

无线传感网络中基于虚拟力的节点动态覆盖算法

周非, 高建军, 范馨月, 安康宁   

  1. 光通信与网络重点实验室,重庆邮电大学,重庆 400065
  • 收稿日期:2016-11-11 出版日期:2018-08-10 发布日期:2019-01-08
  • 作者简介:周非(1977-),男,湖北浠水,博士,教授,研究方向为无线定位,信号处理,网络安全,图像处理等;高建军(1990-),男,山西朔州,硕士生,研究方向为无线传感网络。
  • 基金资助:
    国家自然科学基金(61471077)

Dynamic Covering Algorithm of Node Based on Virtual Force in Wireless Sensor Networks

Zhou Fei, Gao Jianjun, Fan Xinyue, An Kangning   

  1. Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunication, Chongqing, 400065, China
  • Received:2016-11-11 Online:2018-08-10 Published:2019-01-08

摘要: 网络覆盖率和节点功耗是WSNs(Wireless Sensor Networks)中主要考虑的2个性能指标,尽管现有的许多覆盖方法对这2个指标做了相应的提升,但它们大多只针对一个性能进行改进,而对另一个性能的优劣未作详细的讨论。针对这种不足,提出了一种基于VFA(Virtual Force Algorithm)的改进算法,将WSN进行网格划分,节点对网格的作用力和其它作用力进行自适应选择;在所选合力作用下,传感器节点进行重新部署,进一步优化WSN的动态覆盖,使WSN达到较优的覆盖状态;同时,通过对合力门限值的修正,使动态节点的能耗尽可能较少。仿真结果表明,该算法不但能实现较大的网络覆盖和较少的节点功耗,而且还有收敛速度快,计算量小,冗余度低等优点。

关键词: 无线传感网络, 虚拟力, 二进制传感模型, 网络覆盖

Abstract: Network coverage and node power consumption are two main performance indicators in wireless sensor networks (WSNs). Although many of the existing coverage methods have improved the two indicators, most of them have only improved for one performance, and the merits of another performance have not been discussed in detail. For this insufficient, an improved algorithm based on VFA is proposed. The WSN is divided into unit grids. The nodes select the grid forces and other forces adaptively. Under the combined forces, the nodes are re-deployed and the dynamic coverage of WSN is further optimized, so that the WSN achieves a better coverage state. By modifying the force threshold, the energy consumption of the dynamic nodes is as small as possible. The simulation results show that the algorithm can not only achieve large network coverage and less node power consumption, but also has the advantages of fast convergence speed and low computational complexity.

Key words: wireless sensor network, virtual force, binary sensing model, network cover

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