Space-air-ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas. In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites. However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER). Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation. The first subproblem is solved by the Lagrange dual decomposition. For the second subproblem, we propose a queue-aware actor-critic-based task offloading algorithm to cope with dimensionality curse. A greedy-based low-complexity algorithm is developed to solve the third subproblem. Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence.

中文翻译：

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基于学习的队列感知任务卸载和资源分配空间__ir__圆形集成电力物联网


天地一体化电力物联网（SAG-PIoT）可以为部署在偏远地区的PIoT设备提供无处不在的通信和计算服务。在 SAG-PIoT 中，任务可以由 PIoT 设备在本地处理，通过无人机 (UAV) 卸载到边缘服务器，或通过卫星卸载到云服务器。然而，任务卸载和计算资源分配的联合优化面临着信息不完全、维数灾难、排队延迟的长期约束与短期决策之间的耦合等挑战。在本文中，我们提出了一种基于学习的队列感知任务卸载和资源分配算法（QUARTER）。具体来说，联合优化问题被分解为三个确定性子问题：1）设备端任务拆分和资源分配； 2）任务卸载； 3）服务器端资源分配。第一个子问题通过拉格朗日对偶分解来解决。对于第二个子问题，我们提出了一种基于队列感知的 actor-critic 的任务卸载算法来应对维度灾难。开发了一种基于贪婪的低复杂度算法来解决第三个子问题。仿真结果表明，与现有算法相比，QUARTER在能耗、排队时延、收敛性等方面具有优越的性能。