With the swift progress of Internet of Things (IoT) technologies, the number of IoT devices has grown exponentially, leading to an increasing demand for computational power and system stability. Mobile Edge Computing (MEC) is a powerful solution that allows IoT devices to offload data to the edge for computing. In situations involving disasters or complex terrains, establishing ground-based stations may be challenging in providing computational services. Edge computing frameworks built with unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) can provide airborne computational services for IoT devices situated in environments with disasters or complex terrains. In this paper, we design a three-tier framework consisting of ground users (GUs), UAVs, and HAP, offering MEC services for GUs. Considering the randomness and dynamism of task arrivals and the wireless communication quality of devices, we propose an algorithm supporting Non-Orthogonal Multiple Access (NOMA) communication in aerial access networks. The objective of the algorithm is to reduce the overall energy consumption of the system while ensuring system stability. Employing stochastic optimization techniques, we convert the task offloading and resource allocation problem into several parallel solvable sub-problems. We also provide a theoretical analysis of the algorithm. Through a series of comparative experiments, we demonstrate the feasibility and effectiveness of our proposed Dynamic Energy-Efficient Computation Offloading (DEECO) algorithm.

中文翻译：

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支持 NOMA 的空地集成边缘计算中的动态节能计算卸载


随着物联网（IoT）技术的快速发展，物联网设备的数量呈指数级增长，导致对计算能力和系统稳定性的需求不断增加。移动边缘计算 (MEC) 是一种强大的解决方案，允许物联网设备将数据卸载到边缘进行计算。在涉及灾害或复杂地形的情况下，建立地面站在提供计算服务方面可能具有挑战性。由无人机（UAV）和高空平台（HAP）构建的边缘计算框架可以为位于灾难或复杂地形环境中的物联网设备提供机载计算服务。在本文中，我们设计了一个由地面用户（GU）、无人机和HAP组成的三层框架，为GU提供MEC服务。考虑到任务到达的随机性和动态性以及设备的无线通信质量，我们提出了一种支持空中接入网络中非正交多址（NOMA）通信的算法。该算法的目标是在保证系统稳定性的同时降低系统整体能耗。采用随机优化技术，我们将任务卸载和资源分配问题转换为几个并行可解决的子问题。我们还提供了该算法的理论分析。通过一系列的对比实验，我们证明了我们提出的动态节能计算卸载（DEECO）算法的可行性和有效性。