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

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.Comment: 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) 202

DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks

Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process is required. But this brings overhead concerns that decrease the throughput. Moreover, mitigation of interference relies on the selection of transmit powers. In other words, the control scheme should select the optimum configuration relative to other possibilities based on the total interference, and this requires an extensive search. Furthermore, bidirectional communication in real-time needs to exist to control the transmit powers based on the current situation. Based on these challenges, we propose a complete solution with Digital Twin WiFi Networks (DTWN). Contrarily to other studies, with the agent programs installed on the APs in the physical layer of this architecture, we enable information-gathering without causing overhead to the wireless medium. Additionally, we employ Q-learning-based TPC in the Brain Layer to find the best configuration given the current situation. Consequently, we accomplish real-time monitoring and management thanks to the digital twin. Then, we evaluate the performance of the proposed approach through total interference and throughput metrics over the increasing number of users. Furthermore, we show that the proposed DTWN model outperforms existing schemes