Intelligent Resource Allocation in IoV Using Deep Reinforcement Learning to Minimize Latency

In response to the growing demand for efficient task offloading in the context of the Internet of Vehicles (IoV), this study presents a new approach aimed at optimizing computing resources and minimizing latency. The proposed solution relies on a framework based on deep reinforcement learning to simultaneously improve computing resource utilization and reduce latency in Internet of Vehicles task offloading scenarios. The framework harnesses the power of deep reinforcement learning to facilitate intelligent decision-making and adaptive resource allocation. By taking into account both the costs associated with computing resources and the requirements of delay minimization, our approach achieves significant improvements in performance and profitability. Rigorous experimentation validates the effectiveness of our method, demonstrating substantial reductions in resource expenditure and vehicle task delays compared to local task execution.