Evolutionary Game-Based Adaptive DT Association and Transfer for Wireless Computing Power Networks

Wireless Computing Power Networks (WCPN), guided by green principles, aim to provide efficient, flexible, and environmentally friendly computing services for Internet of Things (IoT) applications by seamlessly coordinating computational and networking resources across diverse nodes. The integration of Digital Twin (DT) technology is crucial for achieving these objectives. However, different DT association strategies play a crucial role in enhancing the capabilities of WCPN. In this paper, recognizing the long-term and dynamic nature of DT deployment in real-world scenarios, we utilize evolutionary game theory to model the association and transfer of DTs, aiming for continuous adaptive adjustments and optimizations in their deployment. Specifically, we propose an evolutionary game-based algorithm for DT association as a complement to the independent decision-making process in DT deployment. Moreover, in light of the inherent limitations of the evolutionary game selection mechanism and the lack of self-learning ability, we introduce a deep Q-network (DQN) based evolutionary game approach that ensures adaptive DT association and transfer by considering factors such as DT synchronization delay, model consistency, and migration costs. Numerical results demonstrate that our proposed algorithms outperform the benchmarks in terms of average user utility and convergence speed.