Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

Automated summary

Technological advancements in urban informatics and vehicular intelligence have made smart vehicles ubiquitous edge computing platforms for various applications. However, the different capacities of smart vehicles, diverse application requirements, and unpredictable vehicular topology pose challenges for efficient edge computing services. To address these challenges, we propose incorporating digital twin technology and artificial intelligence into a vehicular edge computing network, enabling centralized service matching and distributed task offloading and resource allocation using multiagent deep reinforcement learning. We also introduce a coordination graph-driven task offloading scheme that integrates service matching and intelligent offloading scheduling in both digital twin and physical networks to minimize costs. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.