基于LoRa的智慧城市物联网空气质量监测系统

Evariste Twahirwa, Kambombo Mtonga, Desire Ngabo, S. Kumaran
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引用次数: 8

摘要

将主要空气污染物控制在世界卫生组织建议的限度以下,对于防治因相关健康问题造成的日益增加的死亡人数至关重要。室内环境尤其如此,通风不良会放大空气污染的影响。了解空气中污染物的水平可以作为采取缓解措施的垫脚石。在这项工作中,提出了一种基于LoRa的物联网框架的家用空气污染监测系统。在卢旺达大学科技学院的自助餐厅、厨房和实验室部署了两个二氧化碳和pm2.5传感器,这两个传感器对空气质量监测和补偿天气监测功能很重要。感知到的参数读数然后通过LoRaWAN协议支持的网关发送到云,该网关连接传感器和网络的云部分。最终用户可以通过开发的基于web的用户界面仪表板查询系统并访问数据以及分析信息。对11个月的数据进行了分析,结果显示厨房环境中二氧化碳的百万分率超过800 ppm, PM2.5浓度超过100 ppm。而在实验室室内观察到的二氧化碳浓度为500 ppm, PM2.5为0 ppm。提出并实现了基线算法,该算法便于为每个传感节点设置触发器,并在测量参数超过某个阈值时推送通知。
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A LoRa enabled IoT-based Air Quality Monitoring System for Smart City
Keeping key air pollutants below the World Health Organization recommended limits is important for combating the ever-increasing deaths resulting from the associated health problems. This is especially true for indoor environments where poor ventilation can magnify the effects of air pollution. Having Knowledge about the level of pollutants in the air would serve as a stepping stone to take mitigation measures. In this work, a domesticated air pollution monitoring system over the LoRa enabled Internet of Things framework is proposed. Two sensors for CO2 and PM2.5that are important for air quality monitoring with compensated weather monitoring capabilities were deployed in the cafeteria kitchen and laboratory room of the University of Rwanda, College of Science and Technology. The sensed parameter readings are then sent to the cloud via LoRaWAN protocol supported gateway that interfaces the sensors and the cloud part of the network. The end users can query the system and access the data together with the analytic information via the developed Web-based user interface dashboard. An analysis of the data over a period of eleven (11) months is carried out and results show high parts per million of CO2of over 800 ppm and PM2.5 concentration of over 100 ppm in the kitchen environment. Whilst a concentration of 500 ppm for CO2and zero ppm for PM2.5 were observed in the laboratory room. Baseline algorithms that facilitate setting of triggers for each sensing node and pushing of notifications for when a measured parameter exceeds a certain threshold value are proposed and implemented.
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