Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (9): 2691-2697.DOI: 10.11772/j.issn.1001-9081.2020010120

• Network and communications • Previous Articles     Next Articles

Clustering algorithm of energy harvesting wireless sensor network based on fuzzy control

HU Runyan, LI Cuiran   

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China
  • Received:2020-02-13 Revised:2020-05-11 Online:2020-09-10 Published:2020-05-11
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61661025), Gansu Institutions of Higher Education Innovation Ability Improvement Program (2019B-052).


胡润彦, 李翠然   

  1. 兰州交通大学 电子与信息工程学院, 兰州 730070
  • 通讯作者: 李翠然
  • 作者简介:胡润彦(1996-),男,重庆人,硕士研究生,主要研究方向:无线传感器网络;李翠然(1975-),女,山西黎城人,教授,博士,主要研究方向:铁路无线通信、无线传感器网络、协同通信。
  • 基金资助:

Abstract: The existing energy harvesting Wireless Sensor Network (WSN) clustering algorithms rarely consider the optimal number of clusters of the network, which leads to excessive network energy consumption and uneven energy consumption across the entire network. To solve this problem, a fuzzy control based energy harvesting WSN clustering algorithm was proposed, namely Energy Harvesting-Fuzzy Logic Clustering (EH-FLC). First, a solar energy replenishment model was introduced into the network energy consumption model, and the function relationship between total energy consumption of the network and the number of network clusters was obtained for each round. The function was derived to obtain the optimal number of clusters of the network. Then, the two-level fuzzy decision system was utilized to assess whether the nodes of the network can become cluster head nodes. The residual energy of the node and the number of adjacent nodes were input into the first level (capability level) as the judgment indexes to filter all the nodes in order to obtain the candidate cluster head nodes. And the centrality parameter and proximity parameter were input into the second level (collaboration level) as the judgment indexes to filter the candidate nodes in order to obtain the cluster head nodes. Finally, the performance indexes of the proposed algorithm such as network life cycle, network energy consumption and network throughput were analyzed through Matlab simulation. Compared with the algorithms of Low Energy Adaptive Clustering Hierarchy (LEACH), Wireless sensor networks non-Uniform Clustering Hierarchy (WUCH) and Cluster head selection using Two-Level Fuzzy Logic (CTLFL), the proposed algorithm has the network working life improved by about 1.4 times, 0.4 times and 0.6 times respectively, and the network throughput increased by about 20 times, 1.5 times and 1.28 times respectively. Simulation results show that the proposed algorithm has better performance in network life cycle and network throughput.

Key words: Wireless Sensor Network (WSN), energy harvesting, the optimal clustering number, clustering algorithm, fuzzy control

摘要: 现有自供能无线传感器网络(WSN)分簇算法较少考虑网络最优分簇数,导致网络能量消耗过快,全网能耗不均衡。针对这个问题,提出了基于模糊控制的自供能WSN分簇算法(EH-FLC)。首先,在网络能量消耗模型中引入太阳能补给模型,得出每一轮次网络能量总消耗与网络分簇数目的函数关系,并对其求导从而得到网络的最佳分簇数。然后,利用双层模糊决策系统来评定网络中的节点能否成为簇头节点。先将节点剩余能量、相邻节点数作为判定指标输入第一层(能力层)对所有节点进行筛选,得到备选簇头节点;再将中心度参数、邻近度参数作为判定指标输入第二层(协作层)对备选簇头节点进行筛选,得到网络簇头节点。最后,通过Matlab仿真分析了该算法的网络生存周期、网络能量消耗和网络吞吐量等性能指标,与低功耗自适应集簇分层型协议(LEACH)、改进的非均匀分簇路由算法(WUCH)和利用双层模糊控制的簇头选择算法(CTLFL)相比,该算法在网络工作寿命上分别提高了约1.4倍、0.4倍和0.6倍,网络吞吐量上分别提高了约20倍、1.5倍和1.28倍。仿真结果表明所提算法在网络生存周期和网络吞吐量方面的性能较优。

关键词: 无线传感器网络, 自供能, 最优分簇数, 分簇算法, 模糊控制

CLC Number: