论文标题
YA-DA：基于杨的数据模型，用于高温IIT空气质量监测
YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quality Monitoring
论文作者
论文摘要
随着工业化的发展，由于工业和日常活动都会产生大量的空气污染，因此空气污染也在稳步上升。由于减少空气污染对于公民的健康和福祉至关重要，因此空气污染监测已成为重要的话题。工业物联网（IIT）研究的重点是这个关键领域。空气污染监测已经存在几种尝试。但是，它们都没有在所需级别提高物联网数据收集的性能。受到真正的下一代（Yang）数据模型的启发，我们提出了一个基于杨的数据模型（YA-DA），以提高IIOT数据收集的性能。此外，通过利用Digital Twin（DT）技术，我们建议使用YA-DA采用启用DT的精细IIOT空气质量监测系统。结果，DT同步变得细粒度。反过来，我们提高了IIOT数据收集的性能，从而导致较低的往返时间（RTT），较高的DT同步和较低的DT潜伏期。
With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focuses on this crucial area. Several attempts already exist for air pollution monitoring. However, none of them are improving the performance of IoT data collection at the desired level. Inspired by the genuine Yet Another Next Generation (YANG) data model, we propose a YAng-based DAta model (YA-DA) to improve the performance of IIoT data collection. Moreover, by taking advantage of digital twin (DT) technology, we propose a DT-enabled fine-grained IIoT air quality monitoring system using YA-DA. As a result, DT synchronization becomes fine-grained. In turn, we improve the performance of IIoT data collection resulting in lower round-trip time (RTT), higher DT synchronization, and lower DT latency.
