Modified Fuzzy C-means Clustering Algorithm for Mobile Wireless Sensor Networks Based on Motion Similarity

As the basis of the Internet of Things, mobile wireless sensor networks have been widely used in many fields such as military and civil communications. Establishing a clustering structure for mobile wireless sensor networks, implementing hierarchical network control and management, can overcome the problem of network topology instability caused by node movement. In order to improve the stability of the dynamic topology network of large-scale mobile wireless sensor clusters, this paper proposes M-FCM (Modified Fuzzy C-means) clustering algorithm based on motion similarity, and optimizes the calculation formula of the objective function of the FCM algorithm. The Euclidean distance and the nodal radial velocity difference are combined as the characteristic parameters of the evaluation function. Simulation result shows that mobile wireless sensor nodes with similar motion states are more likely to cluster together. The modified FCM clustering algorithm can improve the communication link holding time of nodes in the cluster, which is 10.43% and 7.32% higher than that of K-means algorithm and FCM algorithm, respectively, which makes the communication network obtain higher stability and reliability and prolongs the network life cycle.