使用深度学习的社交物联网信任预测模型

社交物联网是社交网络概念在物联网中整合后兴起的一个蓬勃发展的研究领域。提出了一种适用于社交物联网网络的改进型节点级信任模型,并通过与其他信任模型的对比仿真实验证明在恶意节点的攻击下,提出的模型拥有更好的稳定性和适用性,总体波动较小。同时,针对实际社交物联网网络中新加入网络的陌生节点可能遇到的网络延迟影响信任值评估的问题,在改进型节点级信任模型的基础上进一步使用了深度学习模型对其进行信任值预测。仿真证明,使用深度学习预测后模型的系统性能明显优于不使用深度学习的模型,成功交互率提升约1.8%。

A trust prediction model using deep learning for social Internet of Things

The social Internet of Things(SIoT) is a booming research field after the integration of the concept of social network in the Internet of Things(IoT).The devices in the network can independently establish social relationships and seek trusted other devices for task interaction through the trust management mechanism.This paper proposes an improved node-level trust model suitable for the SIoT network and proves that the model has better stability and applicability under the malicious attack of malicious nodes through the simulation experiment compared with other trust models.The overall fluctuation of the results of the proposed model is significantly smaller.In view of the problem that network delay may affect the trust value assessment of new unfamiliar nodes in the actual SIoT,this paper uses the deep learning model to predict the trust value on the basis of the improved node-level trust model.The simulation results show that the system performance of the model using deep learning is significantly better than that of the model without deep learning,and the successful interaction rate is increased by about 1.8%.