Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System
Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System
复制标题
无人机移动边缘计算系统的完成时间和能源优化
DOI：
10.1109/jiot.2020.2993260
复制
发表时间：
2020-08-01
影响因子：
10.6
通讯作者：
Niyato, Dusit
中科院分区：
文献类型：
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作者：
Zhan, Cheng;Hu, Han;Niyato, Dusit
无人机 (UAV) 的完成时间和能耗是无人机应用中的两个重要设计因素。在本文中，我们考虑一种支持无人机的移动边缘计算 (MEC) 系统，用于在有限或没有通用云/边缘基础设施的情况下进行物联网 (IoT) 计算卸载。我们研究了计算卸载和资源分配的联合设计，以及无人机轨迹，以在物联网设备的任务和能源预算约束下最小化无人机的能耗和完成时间。首先考虑没有预定完成时间的无人机能量最小化问题，利用路径离散化技术得到离散化非凸等效问题。通过将离散问题解耦为两个子问题并使用基于逐次凸逼近（SCA）的算法迭代解决这两个子问题，提出了一种针对离散问题的高效交替优化算法。随后，我们重点关注完成时间最小化问题，该问题是非凸且难以解决的。通过使用相同的路径离散逼近模型重新表述问题，提出了类似的交替优化算法。此外，我们研究了平衡无人机能量和完成时间之间的权衡的帕累托最优解决方案。提供的仿真结果是为了证实本文，并表明所提出的设计优于基准方案。我们的结果揭示了 MEC 系统无人机的完成时间和能耗之间的权衡，并且所提出的解决方案可以提供接近下限的性能。
Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a UAV-enabled mobile-edge computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation, as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices’ task and energy budget constraints. We first consider the UAV energy minimization problem without predetermined completion time, a discretized nonconvex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is nonconvex and challenging to solve. By using the same path discretization approximation model to reformulate problem, a similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the tradeoff between the UAV energy and completion time. The simulation results are provided to corroborate this article and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.