Trustworthy computation offloading in digital twin edge networks: A hierarchical game-based approach

The rapid adoption of artificial intelligence (AI) across Internet of things (IoT) applications has intensified the demand for substantial computational resources. Due to the inherent resource limitations of IoT mobile devices, they are often unable to run most resource-intensive applications locally. Mobile edge computing (MEC) emerges as a promising solution, enabling task offloading to powerful edge servers. Although the technical aspects of task offloading are well-investigated in literature, the critical dimensions of security and trust concerns often remain under-explored. To address this gap, we introduce a blockchain-based trust management mechanism for digital Twin (DT) empowered MEC, facilitating informed offloading decision-making in task delegation and processing. The proposed trust management mechanism uses DT to enhance dynamic real-time task offloading. DT provides insights into the physical MEC environment and shares AI agent training data deployed on IoT devices, optimizing workload distribution. Our proposed trust management mechanism is based on an incentive cooperation model based on a hierarchical game model. Initially, we model cooperation among reliable servers as a hedonic coalition formation game, capturing edge servers’ selfish behavior and aiming to maximize utilities. Subsequently, we model interactions between service-seeking devices and the coalition as a two-stage Stackelberg game, encouraging devices to delegate tasks to the most reliable coalition. The simulation results validate the effectiveness of our trustworthy approaches, demonstrating significant improvements in energy efficiency compared to existing related works. Our study shows up to a 60% increase in energy consumption fairness, while ensuring trust among nodes.