利用聚类算法对无线传感器网络(Wireless Sensor Network,WSN)节点进行分簇,并结合Dijkstra算法可解决WSN连通性受损的问题。但选取不同的聚类算法,对WSN连通性的恢复效果也是不同的。为此,从最优移动路径、单步最大长度及算法运行时间3个角度出发,研究两种不同聚类算法(即K-means聚类算法和Fcm聚类算法)对恢复WSN连通性的影响。通过计算机仿真平台,对其仿真结果进行对比、分析,最终得出K-means算法比Fcm算法移动的距离更短,运行的时间更少,从而证明了K-means聚类算法在恢复网络连通性应用的优越性。 The clustering algorithm is used to cluster Wireless Sensor Network (WSN) nodes, and the Dijkstra algorithm can be used to solve the problem of impaired WSN connectivity. However, selecting different clustering algorithms also has different effect on the recovery of WSN connectivity. Therefore, we study the influence of two different clustering algorithms (ie, K-means clustering algorithm and Fcm clustering algorithm) on the restoration of WSN connectivity from the perspectives of the optimal path, the maximum single-step length and the algorithm run time. . Through the computer simulation platform, the simulation results are compared and analyzed. Finally, the K-means algorithm is shorter than the Fcm algorithm and runs less time, which proves that the K-means clustering algorithm in the restoration of network connectivity Application of the superiority.