DOI: 10.5937/jaes17-17429
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
Volume 17 article 572 pages: 18 - 25
Carbon monoxide (CO) pollution is a threat both to our health and well-being. CO concentration above safety threshold
triggers serious illnesses that may even lead to death. Unfortunately, no system is yet capable of detecting and
making decision online and in real-time concerning carbon oxide threat. Hence, decisions related to CO threat are
often made late as they require expert analyses. This paper proposes a solution to this problem by developing a
decision support system for CO threat using internet-based online measurement and an early warning system using
cellular phone. Node station of CO sensor has been built using System on Chip (SOC) WIFI-Microcontroller capable
of sending data via internet gateway. The pollution index value and the rule-based algorithm used to determine CO
pollution categories in the web server program are in line with those stated in the Indonesia Air Pollutant Index (IAPI).
Expert system programming based on expert knowledge is used to make decision on pollution. At the detrimental
level, information is sent to users using a cellular phone. Results in this research show that the use of wireless sensor
system integrated to the internet helps provide precise information on CO concentration that in turn, results in proper
analyses using the expert system, in line with the regulations in place.
1. Mir, M.A., Bhat, M.A., Majid, S. A., Lone, S.H., Malla, M.A., Tiwari, K.R., Pandit, A.H., Tomar, R., Bhat, T.A. (2018). Studies on the synthesis and characterization of polyaniline zeolite nanostruc-tures and their role in carbon monoxide sensing. Environmental Chemical Engineering, vol. 6, pp.1137–1146.
2. Aggarwal, P., Jain, S. (2015). Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach. Environment International, vol. 83, pp.146–157.
3. Levy, R.J. (2015). Carbon monoxide pollution and neurodevelopment: a public health concern. Neurotoxicol.Teratol, vol. 49, pp. 31–40.
4. Sandilands, E.A., Bateman, D.N. (2016). Carbonmonoxide. Medicine. Vol. 44, Issue 3, pp. 151-152.
5. Dingenena, J. V., Steiger, C., Zehe, M., Meinel, L., Lefebvre, R.A. (2018). Investigation of orally deliveredcarbon monoxide for postoperative ileus. EuropeanJournal of Pharmaceutics and Biophar-maceutics,vol. 130, pp. 306-313.
6. Somov, A., Baranov, A., Savkin, A., Spirjakin, D.,Spirjakin, A., Passerone, R. (2011). Developmentof wireless sensor network for combustible gasmonitoring. Sensors and Actuators A: Physical, vol.171, pp. 398– 405.
7. Li, H., Lin, Z. (2017). Study on location of wireless sensor network node in forest environment.Procedia Computer Science, vol. 107, pp. 697-704.
8. Wu, M., Tan, L., Xiong, N. (2016). Data prediction,compression, and recovery in clustered wirelesssensor networks for environmental monitoringapplications. Information Sciences, vol. 329, pp.800–818.
9. Denov, A., Milochkin, A. (2016). Wireless data transfer channel in the monitoring systems of oil production wells. Journal of Applied Engineering Science, vol. 14, pp. 477 – 480.
10. Vujic, D. (2015). Wireless sensor networks applications in aircraft structural health monitoring. Journal of Applied Engineering Science, vol. 13, pp. 79-86.
11. Kuang, K. S. C. (2018). Wireless chemilumi-nescence- based sensor for soil deformation detection. Sensors and Actuators A: Physical, vol. 269, pp. 70–78.
12. Jiang, Y., Yang, X., Liang, P., Liu, P., Huang, X. (2018). Microbial fuel cell sensors for water quality early warning systems: fundamentals, signal resolution, optimization and future challenges. Renewable and Sustainable Energy Reviews, vol. 81, pp. 292–305.
13. Zambrano, A. M., Perez, I., Palau, C., Esteve, M. (2017). Technologies of internet of things applied to an earthquake early warning system. Future Generation Computer Systems, vol. 75, pp. 206-215.
14. Ai, F., Comfort, L.K., Dong, Y., Znati, T. (2016). A dynamic decision support system based on geographical information and mobile social networks: a model for tsunami risk mitigation in Padang, Indonesia. Safety Science, vol. 90, pp. 62-74.
15. Krzhizhanovskaya, V. V., Shirshov, G.S., Melni-kova, N.B., Belleman, R.G., Rusadi, F.I., Broe-khuijsen, B.J., Gouldby, B.P., Lhomme, J., Balis, B., Bubak, M., Pyayt, A.L., Mokhov, I.I., Ozhigin, A.V., Lang, B., Meijer, R.J. (2011). Flood early warning system: design, implementation and computational modules. Procedia Computer Science 4 : International Conference on Compu-tational Science(ICCS) 2011, pp.106–115.
16. Kumar, S.P.L. (2017). State of the art – intense review on artifi cial intelligence systems application in process planning and manufacturing. Engineering Applications of Artifi cial Intelligence, vol. 65, pp. 294-329.
17. Lukasiewicz, K., Teymourian, K., Paschke, A. (2014). A rule-based system for monitoring of micro-blogging disease report. The Semantic Web: ESWC 2014 Satellite Events, pp. 401-406.
18. Diaz, G. J. A, Martinez , L. M. C., Montoya, J. P. G., Olsen, D. B. (2019). Methane number measurements of hydrogen / carbon monoxide mixtures diluted with carbon dioxide for syngas spark ignited internal combustion engine applications. Fuel, vol. 236, pp. 535-543.
19. Ortega, P. P., Rocha, L.S.R., Cortés, J.A., Ramirez, M.A., Buono, C., Ponce, M.A., Simões, A.Z. (2019). Towards carbon monoxide sensors based on europium doped cerium dioxide. Applied Surface Science, vol. 464, pp. 692-699.
20. Nasir, E. F., Farooq, A. (2018). Intra-pulse cavity enhanced measurements of carbon monoxide in a rapid compression machine, IEEE Xplore : 2018 Conference on Lasers and Electro-Optics (CLEO), pp. 1-2.
21. Borkov, Y.G., Petrova, T.M., Solodov, A.M., Solodov, A.A. (2019). Measurements of the broadening and shift parameters of the carbon monoxide spectral lines in the 1–0 band induced by pressure of carbon dioxide. Journal of Quantitative Spectroscopy and Radiative Transfer, vol. 219, pp. 379-382.
22. Mok, J., Park, S. S., Lim, H. K., Kim, J., Edwards,D. P., Lee, J., Yoon, J., Lee, Y. G., Koo, J. (2017). Correlation analysis between regional carbon monoxide and black carbon from satellite measurements. Atmospheric Research, vol. 196, pp. 29-39.
23. Luo, M., Shephard, M. W., Cady-Pereira, K. E, Henze, D. K., Zhu, L., Bash, J. O, Pinder R. W., Capps, S. L., Walker, J.T., Jones, M. R. (2015). Satellite observations of tropospheric ammonia and carbon monoxide: Global distributions, regional correlations and comparisons to model simulations. Atmospheric Environment, vol. 106, pp. 262-277.
24. Karaca, Y., Moonis, M., Zhang, Y., Gezgez, C. (2018). Mobile cloud computing based stroke healthcare system. International Journal of Information Management, Article in press: Available online 12 October 2018.
25. Chang, N., Pongsanone, N. P., Ernest, A. (2012). A rule-based decision support system for sensor deployment in small drinking water networks. Journalof Cleaner Production, vol. 29, pp. 28–37.
26. Gorzalczany, M.B., Rudzinski, F. (2016). A multi-objectivegenetic optimization for fast, fuzzy rulebased credit classification with balanced accuracy and interpretability. Applied Soft Computing, vol. 40,pp. 206–220.
27. Minutolo, A., Esposito, M., Pietro, D.G. (2017).Optimization of rule-based systems in mHealthapplications. Engineering Applications of Artifi cialIntelligence59, pp. 103-121.
28. Gorzalczany, M., Rudzinski, F., (2016). A multi-objective genetic optimization for fast, fuzzy rulebased credit classification with balanced accuracy and interpretability. Applied Soft Computing, vol. 40, pp. 206–220.
29. Moghimi, M., Varjani, A. Y. (2016). New rule-based phishing detection method. Expert Systems with Applications, vol. 53, pp. 231-242.
30. Rahmani, A.M., Gia, T.N., Negash, B., Anzanpour A., Azimi, I., Jiang, M., Liljeberg, P., (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems 78, pp. 641–658.
31. Ferrada, X., Núñez, D., Neyem, A., Serpell, A., Sepúlveda, M. (2016). A cloud-based mobile system to manage lessons-learned in construction projects. Procedia Engineering 164. Pp. 135–142.
32. Zou, H., Zhou.Y., Yang, J., Spanosa, J.C. (2018). Device-free occupancy detection and crowd counting in smart buildings with Wi-Fi-enabled IoT. Energy and Buildings, vol.174, pp. 309-322.
33. Mantoro, T., Suryasa, I. N., Moedjiono, S., Nugroho, M. R. (2016). Automatic early warning for vehicles accidents based on user location. Advance Science Letters, vol. 22, pp. 3065–3070.
34. Dutta, L., Hazarika, A., Bhuyan, M. (2018). Nonlinearity compensation of DIC-based multi-sensor measurement. Measurement, vol. 126, pp. 13-21.
35. Mao, Q., Hu, F., Kumar, S. (2018). Simulation methodology and performance analysis of network coding based transport protocol in wireless big data networks. Simulation Modeling Practice and Theory, vol. 84, pp. 38–49.