Air quality monitoring using multi node slave IoT
Main Article Content
Abstract
Jakarta is the city with the second poorest air quality in the world. IQAir data show that Jakarta's air quality is 159. In addition, the concentration of air particles in Jakarta is 14.2 times higher than the annual guidelines of the World Health Organization (WHO). According to the WHO, exposure to air pollution causes around 7 million premature deaths and millions of years of lost health time each year. Air pollution also stunts children's growth, impairs lung function, etc. Therefore, we need a system that can be used to combine air quality to determine how dangerous a place is with air quality. Knowing air quality, certain policies or actions being taken to overcome this danger. This research aims to build and test a prototype air quality monitoring system using multi-node slaves with the Internet of Things. The prototype development process was carried out by adapting the architectural framework of the air quality monitoring system with the Internet of Things. The testing of prototype results is carried out to sound sensor values and functional success. The results of the test show that the system can run well according to the design made. The DSM501A sensor device functions to detect particles of a size larger than one micrometer, which usually include cigarette smoke, house dust, ticks, spores, pollen, and mildew, and works well so that the controller can read the surrounding air conditions well.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
P. Lestari, S. Damayanti, and M. K. Arrohman, “Emission Inventory of Pollutants (CO, SO 2 , PM 2.5 , and NO X ) In Jakarta Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 489, no. 1, p. 012014, Apr. 2020, doi: 10.1088/1755-1315/489/1/012014.
W. Warsono et al., “Modeling generalized statistical distributions of PM2.5 concentrations during the COVID-19 pandemic in Jakarta, Indonesia,” Decis. Sci. Lett., vol. 10, pp. 393–400, Jan. 2021, doi: 10.5267/j.dsl.2021.1.005.
E. M. C. Wattimena, A. Annisa, and I. S. Sitanggang, “CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM,” Sci. J. Informatics, vol. 9, no. 2, pp. 123–132, Oct. 2022, doi: 10.15294/sji.v9i2.33791.
R. Chen et al., “Beyond PM2.5: The role of ultrafine particles on adverse health effects of air pollution,” Biochim. Biophys. Acta - Gen. Subj., vol. 1860, no. 12, pp. 2844–2855, Dec. 2016, doi: 10.1016/j.bbagen.2016.03.019.
M. Morozesk, I. da C. Souza, M. N. Fernandes, and D. C. F. Soares, “Airborne particulate matter in an iron mining city: Characterization, cell uptake and cytotoxicity effects of nanoparticles from PM2.5, PM10 and PM20 on human lung cells,” Environ. Adv., vol. 6, p. 100125, Dec. 2021, doi: 10.1016/j.envadv.2021.100125.
Z. Cheng, L. Li, and J. Liu, “The impact of foreign direct investment on urban PM2.5 pollution in China,” J. Environ. Manage., vol. 265, p. 110532, Jul. 2020, doi: 10.1016/j.jenvman.2020.110532.
S. Pramana, D. Yoga, Y. Adhinugroho, and M. Nurmalasari, “Air Pollution Changes of Jakarta, Banten, and West Java, Indonesia During the First Month of COVID-19 Pandemic,” J. Bus. Econ. Environ. Stud., vol. 10, pp. 15–19, Oct. 2020, doi: 10.13106/jbees.2020.vol10.no4.15.
H. Zheng et al., “Transition in source contributions of PM2.5 exposure and associated premature mortality in China during 2005–2015,” Environ. Int., vol. 132, p. 105111, Nov. 2019, doi: 10.1016/j.envint.2019.105111.
Q. Xiao et al., “Changes in spatial patterns of PM2.5 pollution in China 2000–2018: Impact of clean air policies,” Environ. Int., vol. 141, p. 105776, Aug. 2020, doi: 10.1016/j.envint.2020.105776.
Z. Li, Y. Tang, X. Song, L. Lazar, Z. Li, and J. Zhao, “Impact of ambient PM2.5 on adverse birth outcome and potential molecular mechanism,” Ecotoxicol. Environ. Saf., vol. 169, pp. 248–254, Mar. 2019, doi: 10.1016/j.ecoenv.2018.10.109.
L. Yang, C. Li, and X. Tang, “The Impact of PM2.5 on the Host Defense of Respiratory System,” Front. Cell Dev. Biol., vol. 8, Mar. 2020, doi: 10.3389/fcell.2020.00091.
C. N. Noviyanti and A. Alamsyah, “Early Detection of Diabetes Using Random Forest Algorithm,” J. Inf. Syst. Explor. Res., vol. 2, no. 1, Jan. 2024, doi: 10.52465/joiser.v2i1.245.
A. D. Lestari, D. A. A. Pertiwi, and M. A. Muslim, “Increasing package delivery efficiency through the application of the prim algorithm to find the shortest route on the expedition route,” J. Student Res. Explor., vol. 1, no. 1, pp. 7–14, Dec. 2022, doi: 10.52465/josre.v1i1.105.
L. O. Aghenta and M. Tariq Iqbal, “Design and implementation of a low-cost, open source IoT-based SCADA system using ESP32 with OLED, ThingsBoard and MQTT protocol,” AIMS Electron. Electr. Eng., vol. 4, no. 1, pp. 57–86, 2020, doi: 10.3934/ElectrEng.2020.1.57.
R. B. Salikhov, V. K. Abdrakhmanov, and I. N. Safargalin, “Internet of Things (IoT) Security Alarms on ESP32-CAM,” J. Phys. Conf. Ser., vol. 2096, no. 1, p. 012109, Nov. 2021, doi: 10.1088/1742-6596/2096/1/012109.
I. L. B. M. Paris, M. H. Habaebi, and A. M. Zyoud, “Implementation of SSL/TLS Security with MQTT Protocol in IoT Environment,” Wirel. Pers. Commun., vol. 132, no. 1, pp. 163–182, Sep. 2023, doi: 10.1007/s11277-023-10605-y.
E. Nemlaha, P. Střelec, T. Horák, S. Kováč, and P. Tanuška, “Suitability of MQTT and REST Communication Protocols for AIoT or IIoT Devices Based on ESP32 S3,” 2023, pp. 225–233. doi: 10.1007/978-3-031-21435-6_19.
K. Alghamdi, A. Alqazzaz, A. Liu, and H. Ming, “IoTVerif: An Automated Tool to Verify SSL/TLS Certificate Validation in Android MQTT Client Applications,” in Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, New York, NY, USA: ACM, Mar. 2018, pp. 95–102. doi: 10.1145/3176258.3176334.
H. Y. Chien et al., “A MQTT-API-compatible IoT security-enhanced platform,” Int. J. Sens. Networks, vol. 32, no. 1, p. 54, 2020, doi: 10.1504/IJSNET.2020.104463.
N. Nikolov and O. Nakov, “Research of Secure Communication of Esp32 IoT Embedded System to.NET Core Cloud Structure using MQTTS SSL/TLS,” in 2019 IEEE XXVIII International Scientific Conference Electronics (ET), IEEE, Sep. 2019, pp. 1–4. doi: 10.1109/ET.2019.8878636.
M. J. A. Baig, M. T. Iqbal, M. Jamil, and J. Khan, “Design and implementation of an open-Source IoT and blockchain-based peer-to-peer energy trading platform using ESP32-S2, Node-Red and, MQTT protocol,” Energy Reports, vol. 7, pp. 5733–5746, Nov. 2021, doi: 10.1016/j.egyr.2021.08.190.
A. Triantafyllou, P. Sarigiannidis, and S. Bibi, “Precision Agriculture: A Remote Sensing Monitoring System Architecture †,” Information, vol. 10, no. 11, p. 348, Nov. 2019, doi: 10.3390/info10110348.
X. Guo et al., “Monitoring and modelling of PM2.5 concentration at subway station construction based on IoT and LSTM algorithm optimization,” J. Clean. Prod., vol. 360, p. 132179, Aug. 2022, doi: 10.1016/j.jclepro.2022.132179.
Q. Abbas and A. Alsheddy, “Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis,” Sensors, vol. 21, no. 1, p. 56, Dec. 2020, doi: 10.3390/s21010056.
“Electrical Energy Monitoring System and Automatic Transfer Switch (ATS) Controller with the Internet of Things for Solar Power Plants,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.2.