Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

中文翻译：

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能量收集边缘计算系统中的在线认知数据传感和处理优化

移动边缘计算 (MEC) 最近已成为减轻物联网 (IoT) 网络中密集计算负担的流行技术。然而，有限的设备电池容量和严格的频谱资源极大地限制了支持 MEC 的物联网网络的数据处理性能。为了解决这两个性能限制，我们在本文中考虑了一个支持 MEC 的物联网系统，其中一个无线设备 (WD) 通过能量收集 (EH) 补充其电池，并有机会访问覆盖的主要通信链路的许可频谱以卸载将其传感数据发送到 MEC 服务器 (MS) 进行边缘处理。在时变衰落信道、随机能量到达和主链路随机开关状态下，我们的目标是设计一种在线算法，在不知道未来系统信息的情况下联合控制认知数据感知速率和处理方法（即本地和边缘处理）。具体而言，我们的目标是最大化 WD 的长期平均感知速率，这取决于主链路的服务质量 (QoS) 要求、MS 的平均功率约束以及 MS 和 WD 的数据队列稳定性。我们将问题表述为多阶段随机优化，并提出了一种名为 PLySE 的在线算法，该算法应用扰动 Lyapunov 优化技术将原始问题分解为每槽确定性优化问题。对于每个时隙问题，我们推导出数据传感和处理控制的封闭形式最优解，以促进低复杂度的实时实现。有趣的是，我们的分析发现，最优解决方案表现出与当前能量状态、二级排队积压和主要链接活动相关的基于阈值的结构。仿真结果与我们的分析相结合，表明所提出的 PLySE 比代表性基准方法的数据传感率提高了 46.7% 以上。