Sparsity-aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 41; no. 11; p. 1
• Main Authors: Zhang, Wen, Xie, Mimi, Scott, Caleb, Pan, Chen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Compressed sensing; Data acquisition; Data transmission; Depletion; Distributed databases; Electronic devices; Energy; Energy harvesting; Image reconstruction; Internet of Things; IoT; Machine learning; Multiagent systems; Reconstruction; Reinforcement learning; Schedules; Scheduling; Sensors; Servers; Sparsity; Spatiotemporal data; Spatiotemporal phenomena
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2022.3197543

In this era of the Internet of Things (IoTs), 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 paper 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 multi-agent 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. Experiment results show that the proposed framework reduced the reconstruction error by 66.30% compared with baselines.