Goal-Driven Trusted Collaborator Selection and Task Offloading in Dynamic Collaborative Systems

Abstract

Given the limited onboard resources and operational time constraints, dynamic collaboration among moving intelligent machines, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) through task offloading has become essential for effective task completion. However, the growing offloading complexity and mismatch between task specifics and distributed resources inevitably lead to resource wastage and potential task failures. Furthermore, malicious collaborators may sneak into offloading processes, which undermines collaborative system reliability. To tackle these challenges collectively, a goal-driven trusted task offloading strategy is proposed, which efficiently matches diverse tasks to optimal distributed resources. Specifically, multidimensional goals of complex tasks are modeled as distinct task completion metrics, jointly termed Value of Service (VoS). Moreover, we define task-specific trust as a goal-achieving mechanism that enables the construction of a reliable collaborator group for a given task with diverse VoS. Based on the task-specific trust evaluation of all potential collaborators, the task offloading process is transformed into a trust-guided bipartite graph matching problem. To mitigate the matching complexity in large-scale collaborative systems, decomposed subtasks with similar goals are initially clustered into limited categories and subsequently arranged by priorities. Simulation results show the proposed strategy efficiently selects capable and reliable collaborators who complete tasks as expected in unreliable dynamic environments.