Multi-source Trust Model Based on Blockchain and IoT Edge Task Collaboration
Sikai He, Jian Chen, Peiyun Zhang, Zhangjie Fu · 2024
Edge devices are positioned on the periphery of cloud environments, serving as agile, secure, and dependable service providers. Nevertheless, IoT edge devices grapple with distinct physical and communication vulnerabilities, such as message forgery and tampering. Thus, trust computing is indispensable for IoT edge devices. Existing trust computing methods confront certain challenges, such as overlooking the impact of fluctuating device resources and subjectivity in human-based weighting aggregation. This work designs a multi-source trust model rooted in IoT edge task collaboration. Edge devices undergo evaluation based on direct trust, indirect trust, and capability trust. This evaluation yields trust values for the edge devices, allowing for the identification of those with dynamic identities with malicious behavior. Subsequently, these malicious entities are excluded from the pool of edge devices. The proposed model employs an IoT consortium blockchain. This blockchain maintains records of interactions within edge task collaboration and documents feedback on trust concerning edge devices. The stored data is accessible to the public and can be verified by other devices, acting as a safeguard against tampering by malicious devices, thereby ensuring data security and privacy. Experimental results affirm that the proposed model exhibits superior computational efficiency and greater reliability when compared to existing methods.