基于分布式模糊逻辑推理的WSN分簇算法

WSN clustering algorithm based on distributed fuzzy logic inference

针对无线传感器网络(WSN)中均衡分簇问题,提出一种基于模糊逻辑推理的WSN分布式分簇算法(DFLC)。利用分布式模糊逻辑控制器选择根节点,以能量大小、中心性、距基站的距离、跳数和节点密度5个参数作为分布式模糊逻辑控制算法的输入。为网络中的中间节点分配模糊逻辑推理引擎,根据自身和相邻节点的信息进行判断,选择发送质量最高子节点的回复消息给根节点,减少了消息传输数量。仿真实验表明,在产生消息数量、能源消耗、存活节点数、容错性、负载平衡等方面,DFLC算法都优于LEACH,ACAWT,Gupta和CHEF算法。

To solve the clustering balancing problem in wireless sensor network （WSN）, a WSN distributed clustering algo- rithm based on fuzzy logic inference is proposed. The distributed fuzzy-logic controller is used to select the root node. The energy magnitude, centrality, distance from the base station, hop count and node density are taken as the inputs of the distributed fuzzy logic control algorithm. All the intermediate nodes in the network are assigned with fuzzy logic inference engines. The reply message of child node with high quality is selected according to the information of the node itself and its adjacent nodes, and transmitted to the root node to reduce the message transmission quantity. The simulation results show that the proposed algorithm is better than LEACH, ACAWT, Gupta and CHEF algorithms in the aspects of message generating quantity, energy consumption, survived node quanlity, fault tolerance and load balancinz.