Composite and Staged Trust Evaluation for Multi-Hop Collaborator Selection

Abstract

Multi-hop collaboration offers new perspectives for enhancing task execution efficiency by increasing available distributed collaborators for resource sharing. Consequently, selecting trustworthy collaborators becomes critical for realizing effective multi-hop collaboration. However, evaluating device trust requires the consideration of multiple factors, including relatively stable factors, such as historical interaction data, and dynamic factors, such as varying resources and network conditions. This differentiation makes it challenging to achieve the accurate evaluation of composite trust factors using one identical evaluation approach. To address this challenge, this paper proposes a composite and staged trust evaluation (CSTE) mechanism, where stable and dynamic factors are separately evaluated at different stages and then integrated for a final trust decision. First, a device interaction graph is constructed from stable historical interaction data to represent direct trust relationships between devices. A graph neural network framework is then used to propagate and aggregate these trust relationships to produce the historical trustworthiness of devices. In addition, a task-specific trust evaluation method is developed to assess the dynamic resources of devices based on task requirements, which generates the task-specific resource trustworthiness of devices. After these evaluations, CSTE integrates their results to identify devices within the network topology that satisfy the minimum trust thresholds of tasks. These identified devices then establish a trusted topology. Finally, within this trusted topology, an A* search algorithm is employed to construct a multi-hop collaboration path that satisfies the task requirements. Experimental results demonstrate that CSTE outperforms the comparison algorithms in identifying paths with the highest average trust values.