Enhancing data transmission in duct air quality monitoring using mesh network strategy for LoRa

• Published in: PeerJ. Computer science 2022-04, Vol.8, p.e939-e939, Article e939
• Main Authors: Mullick, Amit, Abd Rahman, Abdul Hadi, Dahnil, Dahlila Putri, Noraini, Nor Mohd Razif
• Format: Article
• Language: English
• Subjects: Air monitoring; Air pollution; Air quality; Analysis; Carbon dioxide; Carbon monoxide; Communication; Computer Networks and Communications; Cross-sections; Data collection; Data entry; Data transmission; Design of experiments; Duct; Health aspects; Indoor air quality; Infection control; LoRa; Mesh network; Mesh networks; Microcontrollers; Multistory buildings; Network topologies; Non-line-of-sight; Performance evaluation; Receivers & amplifiers; Robotics; Robots; Sensors; Signal strength; Smart sensors; Technology application; Transmission efficiency; Wireless; Wireless communications; Wireless networks
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.939

Duct air quality monitoring (DAQM) is a typical process for building controls, with multiple infections outbreaks reported over time linked with duct system defilement. Various research works have been published with analyses on the air quality inside ducting systems using microcontrollers and low-cost smart sensors instead of conventional meters. However, researchers face problems sending data within limited range and cross-sections inside the duct to the gateway using available wireless technologies, as the transmission is entirely a non-line-of-sight. Therefore, this study developed a new instrument for DAQM to integrate microcontrollers and sensors with a mobile robot using LoRa as the wireless communication medium. The main contribution of this paper is the evaluation of mesh LoRa strategies using our instrument to overcome network disruption problems at the cross-sections and extend the coverage area within the duct environment. A mobile LoRa-based data collection technique is implemented for various data sensors such as DHT22, MQ7, MQ2, MQ135, and DSM50A to identify carbon monoxide, carbon dioxide, smoke, and PM2.5 levels. This study analyzed the efficiency of data transmission and signal strength to cover the air duct environment using several network topologies. The experimental design covered four different scenarios with different configurations in a multi-story building. The network performance evaluations focused on the packet delivery ratio (PDR) and the received signal strength indicator (RSSI). Experimental results in all scenarios showed an improvement in Packet Delivery Ratio (PDR) and significant improvement in the coverage area in the mesh network setup. The results conclude that the transmission efficiency and coverage area are significantly enhanced using the proposed LoRa mesh network and potentially expanded in larger duct environments.