Efficient weighted multi-source trust aggregation scheme for edge computing offloading

Edge computing offloading involves moving computational tasks from traditional cloud computing data centers to edge servers or terminal devices closer to data sources, or idle resource devices. This aims to reduce task response times and enhance service efficiency. However, the dynamic and unreliable nature of edge computing environments can lead to malicious attacks on resource devices, potentially compromising system trustworthiness. Current research primarily focuses on performance optimization and latency reduction, often overlooking service trustworthiness. Therefore, this paper addresses resource device trustworthiness in edge computing, proposing an efficient multi-source trust aggregation scheme to ensure the selection of trustworthy service providers for edge computing offloading. It establishes trust relationships, combines direct and indirect trust, and introduces time decay and energy factors for trust evaluation. Furthermore, the paper integrates trust measurement with collaborative filtering and presents an adaptive aggregation algorithm for calculating and updating global trustworthiness. Through simulation experiments, it validates the effectiveness of the proposed scheme in the selection of service providers for edge computing offloading. The results demonstrate that this approach effectively filters unreliable recommendations, reduces interference from malicious nodes, and enhances the trustworthiness and stability of edge computing offloading services.