Composite and Staged Trust Evaluation for Multi-Hop Collaborator Selection
Botao Zhu, Xianbin Wang
TL;DR
The paper tackles trustworthy multi-hop collaborator selection in distributed edge computing by separating stable historical trust from dynamic resource trust. It introduces Composite and Staged Trust Evaluation (CSTE), which first derives historical trust via a graph neural network on a device interaction graph and then assesses task-specific resource trust based on idle, storage, and energy constraints; these results are integrated to identify trusted collaborators and guide an A* path planning step that maximizes the average trust along the path. Key contributions include a device interaction graph with directed trust edges, a GNN-based trust propagation/aggregation framework, a task-specific resource trust model, and a trusted-topology guided multi-hop path planner with parallel A* searches. Experimental results show that CSTE consistently yields higher average trust values than baseline methods across varying packet loss and task forwarding success rates, demonstrating improved reliability for distributed task offloading in edge-enabled networks.
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.