IoT-enabled Low Power Environment Monitoring System for of PM2.5

Abstract

Air pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the�air quality monitoring�stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although energy-efficient,�wireless sensor network�with the high spatio-temporal resolution is one of the potential solutions, real-time remote monitoring of all significant air quality parameters with�low power consumption�is challenging. To address this challenge, we propose internet of things-enabled low power environment�monitoring system�for real-time monitoring of ten significant air quality parameters. Moreover, the proposed system enables remote monitoring and storage of data for future analysis. Unlike earlier�research work, further expansion of the proposed system is easily possible, as the proposed�Wireless Sensor Node�(WSN) can interface a higher number of sensors with the same number of interfacing pins. We did an in-depth analysis through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed system. Results illustrate the low power consumption of 25.67mW,�data transmission�reliability of 97.4%, and battery life of approximately 31 months for a sampling time of 60�min. The study of the correlation between�Particulate Matter�2.5 (PM2.5) and other pollutants is performed using Central�Pollution Control�Board data of 41 months. The initial study related to correlation is performed for the future work of developing a prediction model of PM2.5 using highly correlated pollutants. The future approach for developing a prediction model in the form of analytical equations with the help of�artificial neural network�is demonstrated. This approach can be implemented using the proposed WSN or low-cost processing tool for evaluating PM2.5 from precursor gases. Therefore, this approach can be one of the promising approaches in the future for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.