Trusted Collaboration in Connected Intelligent Systems

#Trust #Collaboration #Task-Specific #Task#Completion #Connected#Intelligent#Systems

To support a wide range of vertical applications with limited resources, future connected systems rely on highly efficient distributed collaboration. However, with the growing complexity of tasks and resources as well as the dynamic evolution of network topologies, efficiently selecting appropriate capabilities and resources within connected systems to ensure effective task completion is becoming a significant challenge. To address this, we propose using trust as the unified evaluation framework to enable task-oriented resource and capability selection, and present detailed strategies on how trust evaluation can facilitate effective task completion in various types of connected systems. First, a rapid trust evaluation mechanism is introduced for highly dynamic Internet of Vehicles systems to enable timely and efficient collaborator selection. Additionally, task-specific accurate trust evaluation is explored through the use of Generative AI and machine learning techniques to facilitate effective task completion.  Moreover, a trusted multi-task and multi-collaborator matching framework is developed using hypergraphs to uncover device dependencies under specific tasks. Furthermore, a spatio-temporal trust evaluation method is proposed for multi-hop collaboration, leveraging Large Language Model (LLM)-enabled agents to facilitate privacy-preserving collaborator selection. Finally, several open challenges are discussed to highlight future research directions.