Abstract
Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.
References
- Sumithra, J. Jane Ida, K. Karthika, S. Gavaskar. (2016). A Smart Environmental Monitoring System Using Internet of Things. International Journal of Scientific Engineering and Applied Science (IJSEAS), Volume-2, Issue-3.
- Giovanni B. Fioccola, Raffaele Sommese, ImmaTufano, Roberto Canonico, Giorgio Ventre, Polluino (2016). An efficient cloud-based management of IoT devices for air quality monitoring. IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).
- SRM.ArthiShri, NB. Keerthana, S. Sandhiyaa, P. Deepa, D. Mythili ( 2017). Noise and Air Pollution Monitoring System Using IOT. SSRG International Journal of Electrical and Electronics Engineering– (ICETM-2017), Special Issue.
- Ren, J., et al. (2009). Naive Bayes classification of uncertain data. Ninth IEEE International Conference on in Data Mining.
- Jha, Mukesh, Prashanth Reddy Marpu, Chi-Kin Chau, and Peter Armstrong (2015). Design of sensor network for urban micro-climate monitoring. First IEEE International Conference in Smart Cities (ISC2), pp.1-4.
- Nastic, Stefan, SanjinSehic, Duc-Hung Le, Hong-Linh Truong,and Schahram Dustdar, (2014). Provisioning software-defined IoT cloud systems. International Conference in Future Internet of Things and Cloud (FiCloud), pp.288-295.
- Duraipandian, M., and Mr R. Vinothkanna (2019). Cloud Based Internet of Things for Smart Connected Objects. Journal of ISMAC, 1(02), 111-119.
- Valanarasu, M. R. (2019). Smart and Secure IoT and AI Integration Framework for Hospital Environment. Journal of ISMAC, 1(03), 172-179.
- Pandian, A. P. (2019). Enhanced Edge Model for Big Data in The Internet Of Things Based Applications. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), 63-73.
- https://securedstatic.greenpeace.org/india/Global/india/Airpoclypse--Not-just-Delhi--Air-in-mostIndian-cities-hazardous--Greenpeace-report.pdf
- content/uploads/2008/04/5v-regulator-using7805.JPG
- https://store.arduino.cc/arduino-uno-rev3
- Shakya, S. (2019). An Efficient Security Framework for Data Migration in a Cloud Computing Environment. Journal of Artificial Intelligence, 1(01), 45-53.