Modeling the Clustering of Wireless Sensor Networks Using the K-means Method

Currently, the development of information and communication technologies has led to the emergence of the Internet of Things. The technological basis for the implementation of the concept of the Internet of Things is wireless sensor networks. Clustering, a machine learning technique, is an effective way to extend the lifecycle and reduce power consumption of wireless sensor networks (WSNs). The article presents modeling and clustering of flying wireless sensor networks that form when a group of drones moves. The introduction of moving sensors leads to dynamic clustering of the entire system at successive times. complicating the determination of the number of individual clusters and their integration. The clustering mechanism is performed using the K-means algorithm with respect to the location of wireless stationary and flying sensors, described by coordinates x, y, and z in three-dimensional space.