Investigation into a low cost low energy IoT enabled wireless sensor node for particulate matter prediction for environmental applications

Abstract

In recent years, increased transportation, removal of trees for making buildings, establishment of new industries, are the main sources of increased pollution. Increased pollution is one of the major challenge faced by all countries as it a ects environment and human health. On of the way to deal with this challenge is monitoring the environment quality and taking corrective steps for the same. The conventional instruments used for environment monitoring are accurate but costly, time consuming, requires human intervention and lacking in terms of portability. Internet of Things (IoT) enabled wireless sensor node is one of the ideal solutions for real time monitoring of environment in today's urban ecosystems. We have developed a low power IoT enabled wireless sensing and monitoring platform for simultaneous monitoring, real time data of ten di erent environmental parameters such as Temperature, Relative Humidity, Light, Barometric Pressure, Altitude, Carbon dioxide (CO2), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and Ammonia (NH3). We have tried to achieve low power through modi cation in sensor node hardware architecture and developing prediction model which eliminates the need of power hungry sensor. The proposed hardware architecture for wireless sensor node helps in reducing power and number of interfacing pins required from the microcontroller. The proposed wireless sensor node architecture is also adaptable for any other applications after replacement or removal of sensors and/or modi cation of supply. The developed system consists of the transmitter node and the receiver node. The data received at the receiver node is monitored and recorded in an excel sheet in a personal computer (PC) through a Graphical User Interface (GUI), made in LabVIEW. An Android application has also been developed through which data is transferred from LabVIEW to a smartphone and enables IoT. The system is validated through experiments and deployment for real time monitoring. For the proposed system reliability of transmission achieved is 97.4%. Power consumption of the sensor node is quanti ed which is equal to 25.67mW and can be varied by varying the sleep time or sampling time of the node. Battery life of approximately 31 months can be achieved for the measurement cycle of 60 secs. PM2.5 is one of the important pollutants for measuring air quality. Existing methods and instruments used for the measurement of PM2.5 are more laborious, not applicable for both online and o ine, having response time from a few minutes to hours and lacking in terms of portability. In this work we present the correlation study of PM2.5 with other pollutants based on the data received by Central Pollution Control Board (CPCB) online station at N 23 0' 6.6287, E 72 35' 48.7816. Based on the correlation results, CO, NO2, SO2 and VOC parameters (Benzene, Toluene, Ethyl Benzene, M+P Xylene, O-Xylene) are selected as predictors for developing PM2.5 prediction model. PM2.5 prediction model is developed using Arti cial Neural Network (ANN), resulting in a simple analytical equation. Since the proposed model is expressed in simple mathematical equation, it can be deployed on a wireless sensor node enabling online monitoring of PM2.5. Closeness of predicted and actual values of PM2.5 are veri ed through processing derived model equations using low cost processing tool (e.g. excel sheet), thereby eliminating the need for proprietary tools. The RMSE and regression coe cient of the derived model is 1.7973µg/m3 and 0.9986 respectively. Predicted and actual values of PM2.5 are found very close to each other and variation is in the acceptable range. Derived model is recalibrated in terms of predictors and coe cients to test it, in a di erent city, using data of developed low power wireless sensor node. Based on the availability of the sensors on wireless sensor node, recalibration is done for the reduction of predictors to three; CO, NO2 and VOC. For recalibrated model, results show RMSE of 7.5372 µg/m3 and R2 0.9708. The obtained results show the feasibility and e ectiveness of the proposed approach. Improvement in these results is possible by recalibrating prediction model based on data from multiple stations at the place of deployment. Predicted model can be used for online or o ine measurement. Time involved in the measurement is less compared to conventional methods, which is equal to the processing time of the equations. To provide accurate results proposed wireless sensor node is calibrated against the standard calibrated instruments. The proposed system has advantages over conventional methods such as less costly, automated, portable, less time consuming and having higher temporal and spatial resolution.