Research on Cluster Head Probability Optimization and Cluster Tree Algorithm Based on Fuzzy Logic

In order to address the problems of shortening network life cycle and attenuation of data transmission capacity caused by the constant cluster head probability in wireless sensor networks, a fuzzy logic-based cluster head probability optimization and cluster tree routing algorithm is proposed, which uses fuzzy logic to adaptively update the cluster head selection probability based on the indicators such as the residual energy of the network nodes, the distance between the nodes and the base station nodes, and the node proximity density, etc. Meanwhile, the corresponding energy factor, distance factor, and node degree are introduced to the cluster head selection probability. Energy factor, distance factor, and node degree factor are introduced to update the threshold value of cluster head selection, to complete the optimal cluster head selection, and to realize the node energy balance. On this basis, the nearest neighbor algorithm is used to organize the nodes into a multilevel tree structure, and the cluster-tree routing algorithm is constructed through a hierarchical transmission strategy in order to reduce the energy transmission loss between nodes and to achieve inter-cluster data transmission. The proposed algorithm is simulated and experimented on the MATLAB platform, and the experimental results show that the proposed algorithm enhances the data transmission capacity by 62.4% and 15.2% respectively compared to FLBCR and RT_FLCTR, which are the best performers among the compared algorithms and can achieve the purpose of prolonging the network lifecycle and reducing the energy loss while increasing the data throughput.