基于模糊预测的无线传感器网络信任模型

针对无线传感器网络(WSN)中的信任值更新问题，提出了一种基于模糊预测(FP)的无线传感器网络信任值更新的方法--RMFP。算法采用模糊数学理论方法，利用模糊隶属函数来全面地刻画节点的表现行为，并将其转换成节点的模糊隶属度，最后将模糊隶属度进行整合以实现节点的信任值更新。仿真实验表明，所提算法在整合节点信任值精确度方面提高了10.8%，在判断可疑节点的速度方面提高了两倍。这说明基于模糊预测的节点信任值更新算法在发现并摒除恶意节点的准确率和速度上均有显著的效果，尤其是针对前期取得高信任的恶意节点的判断具有很强的优势。

Reputation model based on fuzzy prediction in wireless sensor networks

In view of the update problem of the trust value in Wireless Sensor Network (WSN), a trust model based on Fuzzy Prediction (FP), called RMFP, was proposed. The behavior of nodes was described by using fuzzy mathematics theory method, and the fuzzy membership degree was converted by the fuzzy membership functions. Finally, the trust value was achieved by integrating the fuzzy membership degrees. The simulation results show that the accuracy of trust value is increased by 10.8%, and the judgment speed of suspected nodes is increased by two times. This shows that the effect on accuracy and speed of discovering, eliminating malicious node is more significant, especially for the judgment of the pre-made malicious nodes of high trust value.