Adaptive Swarm Intelligent Offloading Based on Digital Twin-assisted Prediction in VEC
Document Type
Article
Publication Title
IEEE Transactions on Mobile Computing
Abstract
Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, Adaptive Swarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRediction In VEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveD genetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.
DOI
10.1109/TMC.2023.3344645
Publication Date
12-19-2023
Keywords
Digital Twin, Task analysis, Heuristic algorithms, Vehicle dynamics, Adaptation models, Space vehicles, Real-time systems, Mobile computing
Recommended Citation
L. Zhao et al., "Adaptive Swarm Intelligent Offloading Based on Digital Twin-assisted Prediction in VEC," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2023.3344645.
Comments
IR conditions: non-described