The integration of Internet of Things (IoT) technologies into the vehicular industry has initiated a new era of connected and autonomous vehicles, revolutionizing transportation systems. However, this transformation introduces significant challenges, especially in 5 G networks, such as achieving Ultra-Reliable Low-Latency Communications (URLLC) and maintaining energy efficiency within the high mobility of vehicular environments. These are essential for supporting sustainable and environmentally friendly computing practices. To address these challenges, this paper introduces a URLLC-aware and energy-efficient data offloading strategy, utilizing the Asynchronous Advantage Actor-Critic (A3C) algorithm to navigate the complex dynamics of vehicular Mobile Edge Computing (MEC) environments. Our proposed method balances latency and energy consumption trade-offs while ensuring robust communication reliability. Technical evaluations reveal that our approach significantly outperforms other algorithms, achieving up to 8.2 % energy savings and a reduction of over 29 % in latency.

中文翻译：

---

高移动性车载移动边缘计算环境中的 URLLC 感知和节能数据卸载策略


物联网 (IoT) 技术与汽车行业的融合开启了互联和自动驾驶汽车的新时代，彻底改变了交通系统。然而，这种转变带来了重大挑战，特别是在 5G 网络中，例如实现超可靠低延迟通信 (URLLC) 以及在车辆环境的高移动性中保持能源效率。这些对于支持可持续和环保的计算实践至关重要。为了应对这些挑战，本文引入了一种 URLLC 感知且节能的数据卸载策略，利用异步优势 Actor-Critic (A3C) 算法来导航车辆移动边缘计算 (MEC) 环境的复杂动态。我们提出的方法平衡了延迟和能耗的权衡，同时确保了鲁棒的通信可靠性。技术评估表明，我们的方法明显优于其他算法，节能高达 8.2%，延迟减少超过 29%。