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Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks

机译:MEC启用的IOT网络中的移动性感知卸载和资源分配

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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.
机译:移动边缘计算(MEC) - 密集的东西(物联网)网络被认为是有希望的范例来支持大规模的能量受限和计算限制物联网设备。 Movility的IoT在5G时代和即将到来的6G时代,如自主驾驶和车辆通信等,发现了巨大的新服务。然而,在现有工作中的足够水平中尚未研究IoT设备的移动性。在本文中,通过移动性考虑研究了卸载决策和资源分配问题。通过联合优化CPU周期频率,发射功率和中期用户关联向量,最小化所有移动物联网设备(中间)的长期平均总和服务成本。基于Lyapunov优化和半定编程(SDP)提出了在线移动性感知卸载和资源分配(Omora)算法。仿真结果表明,我们所提出的方案可以平衡系统服务成本和延迟性能,并且在系统服务成本方面优于其他卸载基准方法。

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