Sparsity-Aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System
Sparsity-Aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System
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DOI：
10.1109/tcad.2022.3197543
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发表时间：
2022-11
影响因子：
2.9
通讯作者：
Wen Zhang;Mimi Xie;Caleb Scott;Chen Pan
中科院分区：
文献类型：
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作者：
Wen Zhang;Mimi Xie;Caleb Scott;Chen Pan
在物联网 (IoT) 时代，越来越多的物联网设备受益于能量收集 (EH) 技术，该技术实现了可持续的数据采集过程，包括数据传感、通信和存储，以促进社会福祉。然而，间歇性和低 EH 功率限制了数据采集过程。具体来说，由于频繁的停电以及向物联网边缘服务器传输昂贵的数据所消耗的能量，分配给数据传感的能量不足，导致关键信息的丢失。为了解决这个问题，本文提出了一种适用于 EH IoT 设备的稀疏感知时空数据传感框架，以最小化数据传感速率/能量，同时获取全面的信息并保留足够的能量。在该框架中，物联网设备对关键的稀疏时空数据进行采样，然后将稀疏数据发送到边缘服务器进行重建。为了最大限度地提高 EH 设备有限电源和间歇工作模式下的重建精度，我们首先提出基于 QR 的算法 QR-ST 来为每个 EH 设备启动感知调度。由于工作模式不稳定且间歇性，因此需要根据环境输入动态微调时间表。因此，我们进一步提出了一种基于多智能体深度强化学习的方法，称为S-Agents，用于物联网边缘服务器在每个时隙全局选择传感设备，从而保证重建数据的空间和时间特征。实验结果表明，与基线相比，所提出的框架将重建误差降低了 66.30%。
In this era of the Internet of Things (IoT), the increasing number of IoT devices benefit from energy harvesting (EH) technology which enables a sustainable data acquisition process, including data sensing, communication, and storing for promoting the well beings of the society. However, intermittent and low EH power confines the data acquisition process. Specifically, due to frequent harvesting power outages and depletion of energy for expensive data transmission to the IoT edge server, insufficient energy is allocated for data sensing resulting in the missing of key information. To address this issue, this article proposes a sparsity-aware spatiotemporal data sensing framework for EH IoT devices to minimize the data sensing rate/energy while acquiring comprehensive information and reserving sufficient energy. In this framework, the IoT devices sample critical sparse spatiotemporal data, and then the sparse data are sent to the edge server for reconstruction. To maximize the reconstruction accuracy subject to the limited power supply and intermittent work patterns of EH devices, we first propose the QR-based algorithm QR-ST to initiate a sensing scheduling for each EH device. Due to the unstable and intermittent work pattern, the schedule needs to be dynamically fine-tuned based on environmental inputs. Therefore, we further propose a multiagent deep reinforcement learning-based method named S-Agents for the IoT edge server to globally select the sensing devices at each time slot, where the spatial and temporal features of reconstructed data are guaranteed. Experimental results show that the proposed framework reduced the reconstruction error by 66.30% compared with baselines.