YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quality Monitoring

TL;DR

This paper introduces YA-DA, a YAng-based data model, combined with digital twin technology, to enhance the performance of fine-grained air quality monitoring in IIoT systems, achieving lower latency and better synchronization.

Contribution

The paper presents a novel YAng-based data model (YA-DA) and integrates digital twin technology to improve IIoT air quality monitoring performance.

Findings

Lower round-trip time (RTT) in data collection

Higher digital twin synchronization accuracy

Reduced digital twin latency

Abstract

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.