• 网络与通信 •

### 基于模糊控制的自供能无线传感器网络分簇算法

1. 兰州交通大学 电子与信息工程学院, 兰州 730070
• 收稿日期:2020-02-13 修回日期:2020-05-11 出版日期:2020-09-10 发布日期:2020-05-11
• 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:
国家自然科学基金资助项目（61661025）；甘肃省高等学校创新能力提升项目（2019B-052）。

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

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).

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.