Reputation and Clustering based Fault Tolerant Algorithm for Cognitive Wireless Sensor Networks

Aiming at the problem of swirling clusters in Cognitive Radio Sensor Network (CRSN), which cannot guarantee the data reliability due to the lack of fault tolerance, this paper proposes a fault-tolerant clustering algorithm based on reputation and clustering, which organizes the nodes in the network into multiple clusters or clusters by using k-means clustering method, and reduces the overall communication distance and energy consumption. Reduce the overall communication distance and energy consumption and improve the data transmission efficiency. The algorithm optimizes the energy usage of each node, especially the energy allocation in the cluster centers, aiming to extend the overall life cycle of the network. By dynamically updating the reputation value mechanism to select reliable nodes as clustering centers, the network is able to effectively cope with node failures, energy variations, and environmental disturbances and enhance the network's security and resistance to attacks, resulting in a significant improvement in communication performance.