Intelligent Trust Evaluation Method for Underwater Sensor Networks Based on Fuzzy Clustering and Dynamic Weight Allocation

Underwater sensor networks (USN) play a crucial role in marine environmental monitoring and other fields while facing significant security challenges. Trust models can effectively defend against insider attacks and ensure network reliability. However, most existing trust evaluation methods rely on the linear weighting of trust features and the decision-making method based on threshold comparison. In dynamic underwater environments, factors such as water flow and temperature are constantly changing in time and space, leading to differences in the variations of node trust features and overall fluctuations of trust values. This makes it challenging to effectively determine the optimal weights and the reasonable threshold, thereby affecting the accuracy of evaluation and the reliability of decision-making. To solve this issue, this paper proposed an intelligent trust evaluation method based on fuzzy clustering and dynamic weight allocation. First, a hierarchical dynamic topology model of the USN was developed to enhance universality. On this basis, communication, energy, and data features were comprehensively calculated to fully reflect node states. Then, the unsupervised machine learning algorithm, namely fuzzy C-means clustering, was employed to enable adaptive node trust decision-making. Meanwhile, a subjective and objective combination strategy was adopted to dynamically allocate weights to features according to network and environmental conditions. Consequently, the intelligent evaluation of node trust was achieved. Simulation results demonstrate that the proposed method can effectively evaluate the trust of nodes in underwater environments, improve the reliability of trust decision-making, and enhance the security of the network.