Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (7): 1974-1980.DOI: 10.11772/j.issn.1001-9081.2018010144

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WSN hierarchical routing algorithm based on fuzzy C-means clustering and swarm intelligence

QI Pan1,2, BAO Kaiyang1, MA Xiaoyuan1,2   

  1. 1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-01-17 Revised:2018-02-21 Online:2018-07-10 Published:2018-07-12
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2016YFC0801505).


戚攀1,2, 包开阳1, 马皛源1,2   

  1. 1. 中国科学院 上海高等研究院, 上海 201210;
    2. 中国科学院大学, 北京 100049
  • 通讯作者: 包开阳
  • 作者简介:戚攀(1993-),男,湖北黄冈人,硕士研究生,主要研究方向:无线传感器网络;包开阳(1991-),男,浙江东阳人,硕士,主要研究方向:无线传感器网络;马皛源(1987-),男,上海人,助理研究员,硕士,主要研究方向:低功耗无线通信、网络编码。
  • 基金资助:

Abstract: To improve the energy efficiency and prolong the life cycle of Wireless Sensor Network (WSN), a WSN hierarchical routing algorithm based on Fuzzy C-Means (FCM) clustering and Swarm Intelligence (FCM-SI) was proposed. Firstly, FCM clustering algorithm was adopted to cluster the network, which optimized the distance between common nodes and Cluster Heads (CH). Then, Artificial Bee Colony (ABC) algorithm with three parameters was used to select the optimal CH. At last, Ant Colony Optimization (ACO) algorithm was adopted to search the multi-hop path between CH and Base Station (BS), which took energy consumption and load balancing into consideration. The simulation results show that compared with the algorithms of Improved Low Energy Adaptive Cluster Hierarchy (I-LEACH) based on uniform clustering, Low Energy Adaptive Cluster Hierarchy (LEACH) based on ABC (ABC-LEACH) and LEACH based on ACO (ANT-LEACH), the network life cycle of FCM-SI is prolonged by 65.2%, 49.6%, and 29.0% under initial network conditions with 100 nodes deployed in 100 m*100 m area. FCM-SI can effectively prolong network life and improve energy efficiency.

Key words: Wireless Sensor Network (WSN), Fuzzy C-Means (FCM) clustering, swarm intelligence, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO)

摘要: 为了提高无线传感器网络(WSN)的能量效率并延长其生命周期,提出了一种基于模糊C均值聚类(FCM)和群体智能的WSN分层路由算法(FCM-SI)。首先采用FCM聚类算法对网络进行分簇,优化普通节点与簇头(CH)间距离;然后采用三参数的人工蜂群(ABC)算法选取每个簇的最优簇头;最后采用蚁群优化(ACO)算法搜索簇头至基站(BS)的多跳路径,路径综合考虑了网络的能耗和负载均衡性能。仿真结果显示,与基于均匀分簇的改进的低功耗自适应分簇(I-LEACH)算法、基于ABC的低功耗自适应分簇(ABC-LEACH)算法和基于ACO的低功耗自适应分簇(ANT-LEACH)算法相比,FCM-SI在100 m×100 m,100个节点的初始网络条件下将网络生命周期分别提高了65.2%、49.6%和29.0%。FCM-SI能够有效地延长网络寿命,提高能量利用效率。

关键词: 无线传感器网络, 模糊C均值聚类, 群体智能, 人工蜂群, 蚁群优化

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