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.
@article{arxiv.2211.15287,
title = {YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quality Monitoring},
author = {Yagmur Yigit and Khayal Huseynov and Hamed Ahmadi and Berk Canberk},
journal= {arXiv preprint arXiv:2211.15287},
year = {2023}
}
Comments
This paper has been accepted at the 4th Workshop on Future of Wireless Access and Sensing for Industrial IoT (FUTUREIIOT) in IEEE Global Communications Conference (IEEE GLOBECOM) 2022