Keynote on Data Science for IoT

1. Data Science for IoT
FERDIN JOE JOHN JOSEPH, PHD
THAI – NICHI INSTITUTE OF TECHNOLOGY, BANGKOK
Keynote for 2nd Cloud Based International Conference on Computational Systems for Health and Sustainability, 5 June 2020

2. About Me
Lecturer, Data Science and Analytics,
Faculty of Information Technology,
Thai – Nichi Institute of Technology

3. Overview
Intro to Data Science
IoT in Data Science
Case Study in IoT

4. Introduction to Data Science

6. Data Science Life Cycle in IoT

8. Sensors

9. IoT Adoption

11. Focus on??

14. 3S of IoT

15. Scalability

16. Case: PM2.5
Particulate Matter 2.5
Carbon particles of size equal or less than 2.5 x 106
Measured in
Responsible for severe health hazards
Can mix into blood and not easy to excrete

17. Magnitude
Source: MDPI

18. Mortality Map
Source: Aungkulanon, S., Tangcharoensathien, V.,
Shibuya, K. et al. Post universal health coverage trend
and geographical inequalities of mortality in Thailand Int
J Equity Health (2016) 15: 190.
https://doi.org/10.1186/s12939-016-0479-5

19. Data Sources
PM 2.5 – Berkeley Earth, Air4Thai
Other weather parameters – Weather Channel API (Collected data from Pathumwan
Demonstration School)
Timestamp of data: 2016 – 19

20. Bangkok
Samutprakan
Narathiwat
Kanchanaburi
Rayong
Chiang Mai

21. Daily trend over years

22. Case: PM 2.5 Project

23. Intranet system at TNI

24. Intranet system at TNI

25. Intranet system at TNI

26. Nodes

27. Mist Sprayers

28. Machine Learning in PM2.5 data

29. Feature Importance using Linear
Regression

30. Feature Importance using SVR

31. Source Code Available at Github
IoT-Based-Weather-Monitoring-for-Effective-Analytics

32. References
Alif, Y., Utama, K., Widianto, Y., Hari, Y., & Habiburrahman, M. (2019). Design of Weather Monitoring Sensors and Soil Humidity in
Agriculture Using Internet of Things ( IoT ). Transactions on Machine Intelligence and Artificial Intelligence, 7(1), 10–20.
Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., & Kenski, D. (2009). Expert Systems with Applications PM 2 . 5 concentration prediction
using hidden semi-Markov model-based times series data mining. Expert Systems with Applications, 36(2009), 9046–9055.
Hien, P. D., Bac, V. T., Tham, H. C., Nhan, D. D., & Vinh, L. D. (2002). Influence of meteorological conditions on PM 2 . 5 and PM 2 . 5 À 10
concentrations during the monsoon season in Hanoi , Vietnam. Atmospheric Environment, 36, 3473–3484.
John Joseph, F. J. (2019a). Empirical Dominance of Features for Predictive Analytics of Particulate Matter Pollution in Thailand. In 5th
Thai-Nichi Institute of Technology Academic Conference TNIAC 2019 (pp. 385–388).
John Joseph, F. J. (2019b). IoT Based Weather Monitoring System for Effective Analytics. International Journal of Engineering and
Advanced Technology, 8(4), 311–315.
John Joseph, F. J., T, R., & C, J. J. (2011). Classification of correlated subspaces using HoVer representation of Census Data. In 2011
International Conference on Emerging Trends in Electrical and Computer Technology (pp. 906–911). Ieee.
Kanabkaew, T. (2013). Prediction of Hourly Particulate Matter Concentrations in Chiangmai, Thailand Using MODIS Aerosol Optical Depth
and Ground-Based Meteorological Data. Environment Asia, 6(2), 65–70.
Math, R. K. M., & Dharwadkar, N. V. (2018). IoT Based Low-cost Weather Station and Monitoring System for Precision Agriculture in India.
In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile,
Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on (pp. 81–86).

33. References (contd)
Ordieres, J. B., Vergara, E. P., Capuz, R. S., & Salazar, R. E. (2005). Neural network prediction model for fine particulate matter ( PM 2 . 5 ) on the US e Mexico border in ´ rez (
Chihuahua ) El Paso ( Texas ) and Ciudad Jua. Environmental Modelling & Software, 20, 547–559.
Pe, P., Trier, A., & Reyes, J. (2000). Prediction of PM concentrations several hours in advance using neural networks in Santiago , Chile. Atmospheric Environment, 34, 1189–
1196.
Pollution Control Board, T. (n.d.). Thailand’s air quality and situation reports. Retrieved from http://air4thai.pcd.go.th/webV2/index.php
Ray, P. P. (2016). Internet of Things Cloud Based Smart Monitoring of Air Borne PM2 . 5 Density Level. In International conference on Signal Processing, Communication, Power
and Embedded System (SCOPES) (pp. 995–999).
Shah, J., & Mishra, B. (2016). IoT enabled environmental monitoring system for smart cities. In 2016 International Conference on Internet of Things and Applications (IOTA) (pp.
383–388).
Shete, R., & Agrawal, S. (2016). IoT Based Urban Climate Monitoring using Raspberry Pi. In International Conference on Communication and Signal Processing (pp. 2008–2012).
Siva, K., Ram, S., & Gupta, A. N. P. S. (2016). IoT based Data Logger System for weather monitoring using Wireless sensor networks. International Journal of Engineering Trends
and Technology (IJETT), 32(2), 71–75.
Strigaro, D., & Cannata, M. (2019). Boosting a Weather Monitoring System in Low Income Economies Using Open and Non-Conventional Systems : Data Quality Analysis.
Sensors, 19(5), 1–22.
Thilagam. J, S. T., Babu, T. S., & Reddy, B. S. (2018). Weather monitoring system application using LabVIEW. In 2018 2nd International Conference on I-SMAC (IoT in Social,
Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on (pp. 52–55).
Vinitketkumnuen, U., Kalayanamitra, K., Chewonarin, T., & Kamens, R. (2002). Particulate matter, PM 10 & PM 2.5 levels, and airborne mutagenicity in Chiang Mai, Thailand.
Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 519(1–2), 121–131.

34. MOOC
https://bit.ly/2l38dYU

35. Youtube Channel

36. THANK YOU!