Artificial Intelligence Inspired Task Offloading and Resource Orchestration in Intelligent Transportation Systems

Internet of Vehicles (IoV) applications require the support of communication, caching, and computation (3C) resources to offload the computation-intensive tasks and for uplifting the traffic conditions in the development of sustainable smart cities. Intelligent Transportation Systems (ITS) lack the integrated ecosystems of addressing the low-latency task handovers, resource management issues, and centralized incentivization strategies. Digital Twin (DT) aids in capturing the real-time varying resource needs of the vehicles and the communication infrastructure that will regulate the task offloading process and facilitates in incentivizing the vehicular instances. In this manuscript, we establish a digital twin counterpart ( \(DT_{PIoV}\) $D{T}_{\mathit{PIoV}}$ ) of the physical IoV (PIoV) to meet the QoS requirements during dynamic offloading and the time-varying resource supply–demand of computationally intensive applications. We formulate a response delay minimization function which is solved by the proposed DT-driven context-aware dynamic offloading method (CADOM). Furthermore, we use M/M/1/N/FCFS queueing method that combats the drawbacks of handling the simultaneous deadline-based tasks in a volatile environment of PIoV. In addition, we also maximize the utilities of vehicle and RSU service satisfaction by employing a reward-based mechanism for on-demand allocation of resources based on the Stackelberg game, where the DT of vehicle is deemed as a leader and service provider RSUs as a follower. The simulation results establish that the proposed system outpaces the conventional traffic management system by emphasizing the role of \(DT_{PIoV}\) $D{T}_{\mathit{PIoV}}$ in jointly optimizing the overall response latency for different task sizes and also ensure a better utility satisfaction by catering on-demand resource allocation.