Energy-minimized Partial Computation Offloading in Cloud-assisted Vehicular Edge Computing Systems

Nowadays, the rapid advancement of Connected and Automated Vehicles (CAVs) has led to their integration with various capabilities, encompassing environmental sensing, decision-making, and multi-level assisted driving. However, the integration of computationally intensive applications like navigation and autonomous driving challenges CAVs due to their limited computational resources, necessitating the timely completion of computations. Vehicular Edge Computing (VEC) offers a solution by enabling CAVs to partially offload computation-intensive tasks to Roadside Units (RSUs) embedded with Roadside Edge Servers (RESs). Nonetheless, RSUs have finite computational resources. Therefore, a Cloud-assisted Vehicular Edge Computing (CVEC) architecture is introduced to address this problem. In this paper, we first formulate a typical CVEC system and then formulate a constrained optimization problem based on the aforementioned system, which considers both communication latency and energy consumption. Finally, a novel optimization algorithm called Whale optimization embedded with Simulated-Annealing and Genetic-learning (WSAG) is proposed to solve the above optimization problem. WSAG simultaneously determines the resource allocation and optimizes the energy consumption of the system. Experiment results prove that WSAG significantly achieves lower energy consumption with faster convergence speed than state-of-the-art peers.