支持MEC的IoT网络中的移动性感知卸载和资源分配
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
摘要：
支持移动边缘计算（MEC）的物联网（IoT）网络被认为是支持大规模能量受限和计算受限的IoT设备的有前途的范例。具有移动性的物联网在5G时代和即将到来的6G时代发现了大量新服务，如自动驾驶和车载通信。然而，物联网设备的移动性还没有在现有的作品中充分研究。本文研究了考虑移动性的卸载决策和资源分配问题。通过联合优化CPU周期频率、发射功率和MID的用户关联向量，使所有移动IoT设备（MID）的长期平均总和服务成本最小化。提出了一种基于李雅普诺夫优化和半定规划的在线移动感知卸载和资源分配算法。仿真结果表明，我们提出的方案可以平衡系统服务成本和延迟性能，并优于其他卸载基准方法的系统服务成本。
英文摘要：
Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.
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