Adaptive Swarm Intelligent Offloading Based on Digital Twin-assisted Prediction in VEC

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 <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> warm Intelligent Offloading Scheme Based on Digital- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> win-Assisted P <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ediction <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> n <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VE</u> C (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 improve <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> genet <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> c simulat <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> d annealing-ba <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</u> d partic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> e 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.