This document discusses data science applications for Internet of Things (IoT) systems, specifically regarding air pollution monitoring. It introduces the presenter and provides an overview of topics like the data science life cycle in IoT, fog computing applications, and a case study on using IoT sensors and machine learning to monitor and predict particulate matter (PM2.5) air pollution levels in Thailand. The case study deployed IoT sensor nodes and mist sprayers to collect local weather and pollution data, which was analyzed using linear regression and support vector regression to better understand pollution trends and identify influential factors.

1. Data Science for IoT
FERDIN JOE JOHN JOSEPH, PHD
THAI – NICHI INSTITUTE OF TECHNOLOGY, BANGKOK

2. About Me
Lecturer, Data Science and Analytics,
Faculty of Information Technology,
Thai – Nichi Institute of Technology
First of its kind in
Thailand

3. Overview
Intro to Data Science
IoT in Data Science
Fog Computing in IoT

4. Introduction to Data Science

7. Data Science Life Cycle in IoT

9. Sensors

10. IoT Adoption

17. Focus on??

20. 3S of IoT

21. Scalability

22. Possible solution

24. Applications of Fog Computing in IoT
Data acquisition and preprocessing
Condition Monitoring
Rule-Based Decision Making
Short-term data storage

25. Benefits
Minimize latency
Conserve network bandwidth
Address security concerns at all level of the network
Operate reliably with quick decisions
Collect and secure wide range of data
Move data to the best place for processing
Lower expenses of using high computing power only when needed and less bandwidth
Better analysis and insights of local data

26. 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

27. Magnitude
Source: MDPI

28. 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

29. 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

30. Bangkok
Samutprakan
Narathiwat
Kanchanaburi
Rayong
Chiang Mai

31. Daily trend over years

32. Case: PM 2.5 Project

33. Intranet system at TNI

34. Intranet system at TNI

35. Intranet system at TNI

36. Nodes

37. Mist Sprayers

38. Machine Learning in PM2.5 data

39. Feature Importance using Linear
Regression

40. Feature Importance using SVR

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

42. 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).

43. 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.

44. About Me
Advisor, Bellerophon Gem Lab, Bangkok

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

46. Youtube Channel

47. THANK YOU!