{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:17:48Z","timestamp":1780611468451,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24993051"],"award-info":[{"award-number":["BR24993051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM2.5 and r = 0.86 for PM10, and low error levels (RMSE \u2248 2.1\u20132.2 \u00b5g\/m3). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency &lt; 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems.<\/jats:p>","DOI":"10.3390\/a19050395","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:36:40Z","timestamp":1779107800000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["IoT-Based Air Quality Monitoring with Low-Cost Sensors: Adaptive Filtering and RPA-Based Decision Automation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1596-561X","authenticated-orcid":false,"given":"Aiman","family":"Moldagulova","sequence":"first","affiliation":[{"name":"Information Systems Department, Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4970-3095","authenticated-orcid":false,"given":"Zhuldyz","family":"Kalpeyeva","sequence":"additional","affiliation":[{"name":"Information Systems Department, Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raissa","family":"Uskenbayeva","sequence":"additional","affiliation":[{"name":"Information Systems Department, Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-6715","authenticated-orcid":false,"given":"Nurdaulet","family":"Tasmurzayev","sequence":"additional","affiliation":[{"name":"Farabi AI Center, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-6337","authenticated-orcid":false,"given":"Bibars","family":"Amangeldy","sequence":"additional","affiliation":[{"name":"Farabi AI Center, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3736-0291","authenticated-orcid":false,"given":"Yeldos","family":"Altay","sequence":"additional","affiliation":[{"name":"Department of Science and Innovation, Mukhametzhan Tynyshbayev ALT University, Almaty 050012, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Srinivasan, R. (2020). A Systematic Review of Air Quality Sensors, Guidelines, and Measurement Studies for Indoor Air Quality Management. Sustainability, 12.","DOI":"10.3390\/su12219045"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A. (2019). Review of the Performance of Low-Cost Sensors for Air Quality Monitoring. Atmosphere, 10.","DOI":"10.3390\/atmos10090506"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Narayana, M.V., Jalihal, D., and Nagendra, S.M.S. (2022). Establishing a Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art. Sensors, 22.","DOI":"10.3390\/s22010394"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C., and Zaldei, A. (2018). Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors. Sensors, 18.","DOI":"10.3390\/s18092843"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, H.-Y., Schneider, P., Haugen, R., and Vogt, M. (2019). Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway. Atmosphere, 10.","DOI":"10.3390\/atmos10020041"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti, A., Kalkavouras, P., Fameli, K.M., Hatzianastassiou, N., Mihalopoulos, N., and Gerasopoulos, E. (2020). Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece. Atmosphere, 11.","DOI":"10.3390\/atmos11090926"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Velasco, A., Ferrero, R., Gandino, F., Montrucchio, B., and Rebaudengo, M. (2016). A Mobile and Low-Cost System for Environmental Monitoring: A Case Study. Sensors, 16.","DOI":"10.3390\/s16050710"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Correia, C., Martins, V., Matroca, B., Santana, P., Mariano, P., Almeida, A., and Almeida, S.M. (2023). A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20054073"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Popovi\u0107, I., Radovanovic, I., Vajs, I., Drajic, D., and Gligori\u0107, N. (2022). Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing. Sensors, 22.","DOI":"10.3390\/s22031026"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schilt, U., Barahona, B., Buck, R., Meyer, P., Kappani, P., M\u00f6ckli, Y., Meyer, M., and Schuetz, P. (2023). Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements. Sensors, 23.","DOI":"10.3390\/s23020794"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Schneider, P., Vogt, M., Haugen, R., Hassani, A., Castell, N., Dauge, F.R., and Bartonova, A. (2023). Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems. Atmosphere, 14.","DOI":"10.3390\/atmos14030540"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Clements, A.L., Griswold, W.G., Johnston, J.E., Herting, M.M., Thorson, J., Collier-Oxandale, A., and Hannigan, M. (2017). Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary). Sensors, 17.","DOI":"10.3390\/s17112478"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Johnston, S.J., Basford, P.J., Bulot, F.M.J., Apetroaie-Cristea, M., Easton, N.H.C., Davenport, C., Foster, G.L., Loxham, M., Morris, A.K.R., and Cox, S.J. (2019). City Scale Particulate Matter Monitoring Using LoRaWAN Based Air Quality IoT Devices. Sensors, 19.","DOI":"10.3390\/s19010209"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Penza, M., Suriano, D., Pfister, V., Dipinto, S., Prato, M., and Cassano, G. (2025). Networked Low-Cost Sensor Systems for Urban Air Quality Monitoring: A Long-Term Use-Case in Bari (Italy). Chemosensors, 13.","DOI":"10.3390\/chemosensors13110380"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahangar, F.E., Freedman, F.R., and Venkatram, A. (2019). Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16071252"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tanzer, R., Malings, C., Hauryliuk, A., Subramanian, R., and Presto, A.A. (2019). Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16142523"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Han, P., Mei, H., Liu, D., Zeng, N., Tang, X., Wang, Y., and Pan, Y. (2021). Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2. Sensors, 21.","DOI":"10.3390\/s21010256"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Venkatraman Jagatha, J., Klausnitzer, A., Chac\u00f3n-Mateos, M., Laquai, B., Nieuwkoop, E., van der Mark, P., Vogt, U., and Schneider, C. (2021). Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research. Sensors, 21.","DOI":"10.3390\/s21123960"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ta\u015ftan, M. (2025). Machine Learning\u2013Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors. Sensors, 25.","DOI":"10.3390\/s25103183"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"De Vito, S., Esposito, E., Massera, E., Formisano, F., Fattoruso, G., Ferlito, S., Del Giudice, A., D\u2019Elia, G., Salvato, M., and Polichetti, T. (2021). Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors, 21.","DOI":"10.3390\/s21155219"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vajs, I., Drajic, D., and Cica, Z. (2023). Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring. Sensors, 23.","DOI":"10.3390\/s23052815"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Idrees, Z., Zou, Z., and Zheng, L. (2018). Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development. Sensors, 18.","DOI":"10.3390\/s18093021"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Samad, A., Kieser, J., Chourdakis, I., and Vogt, U. (2024). Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors. Sensors, 24.","DOI":"10.3390\/s24030945"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Broday, D.M., and The Citi-Sense Project Collaborators (2017). Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement\u2014The Promise and the Current Reality. Sensors, 17.","DOI":"10.3390\/s17102263"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wesseling, J., de Ruiter, H., Blokhuis, C., Drukker, D., Weijers, E., Volten, H., Vonk, J., Gast, L., Voogt, M., and Zandveld, P. (2019). Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science. Atmosphere, 10.","DOI":"10.3390\/atmos10080445"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kosmopoulos, G., Salamalikis, V., Matrali, A., Pandis, S.N., and Kazantzidis, A. (2022). Insights about the Sources of PM2.5 in an Urban Area from Measurements of a Low-Cost Sensor Network. Atmosphere, 13.","DOI":"10.3390\/atmos13030440"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Trejo, A., B\u00f6hnel, H.N., Ibarra-Ortega, H.E., Salcedo, D., Gonz\u00e1lez-Guzm\u00e1n, R., Casta\u00f1eda-Miranda, A.G., S\u00e1nchez-Ramos, L.E., Chaparro, M.A.E., and Chaparro, M.A.E. (2024). Air Quality Monitoring with Low-Cost Sensors: A Record of the Increase of PM2.5 during Christmas and New Year\u2019s Eve Celebrations in the City of Queretaro, Mexico. Atmosphere, 15.","DOI":"10.3390\/atmos15080879"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qin, X., Do, T.H., Hofman, J., Bonet, E.R., La Manna, V.P., Deligiannis, N., and Philips, W. (2022). Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density. Remote Sens., 14.","DOI":"10.3390\/rs14112613"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guo, R., Qi, Y., Zhao, B., Pei, Z., Wen, F., Wu, S., and Zhang, Q. (2022). High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19138005"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Felici-Castell, S., Segura-Garcia, J., Perez-Solano, J.J., Fayos-Jordan, R., Soriano-Asensi, A., and Alcaraz-Calero, J.M. (2023). AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems. Sensors, 23.","DOI":"10.3390\/s23239585"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bainomugisha, E., Ssematimba, J., and Okure, D. (2023). Design Considerations for a Distributed Low-Cost Air Quality Sensing System for Urban Environments in Low-Resource Settings. Atmosphere, 14.","DOI":"10.3390\/atmos14020354"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mahajan, S., Gabrys, J., and Armitage, J. (2021). AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality. Sensors, 21.","DOI":"10.3390\/s21124044"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tseng, W.-C., and Chang, W.-T. (2025, January 16\u201318). A Study on the Application of Robotic Process Automation: Air Quality Index Monitoring Using UiPath. Proceedings of the 2025 IEEE International Conference on Consumer Electronics\u2013Taiwan (ICCE-Taiwan), Kaohsiung, Taiwan.","DOI":"10.1109\/ICCE-Taiwan66881.2025.11207921"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116856","DOI":"10.1016\/j.ecoenv.2024.116856","article-title":"Real-Time IoT-Powered AI System for Monitoring and Forecasting of Air Pollution in Industrial Environment","volume":"283","author":"Ramadan","year":"2024","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s12599-018-0542-4","article-title":"Robotic process automation","volume":"60","author":"Bichler","year":"2018","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tasmurzayev, N., Amangeldy, B., Smagulova, G., Baigarayeva, Z., and Imash, A. (2025). A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty. Sensors, 25.","DOI":"10.3390\/s25144521"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ao, R., Chen, H., Li, J., Wei, L., and Wang, Z. (2024). Characteristics of PM2.5 and CO2 Concentrations in Typical Functional Areas of a University Campus in Beijing Based on Low-Cost Sensor Monitoring. Atmosphere, 15.","DOI":"10.3390\/atmos15091044"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.envres.2015.07.022","article-title":"Urban Air Quality Comparison for Bus, Tram, Subway and Pedestrian Commutes in Barcelona","volume":"142","author":"Moreno","year":"2015","journal-title":"Environ. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shahid, S., Brown, D.J., Wright, P., Khasawneh, A.M., Taylor, B., and Kaiwartya, O. (2025). Innovations in Air Quality Monitoring: Sensors, IoT and Future Research. Sensors, 25.","DOI":"10.3390\/s25072070"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Marques, G., and Pitarma, R. (2019). A Cost-Effective Air Quality Supervision Solution for Enhanced Living Environments through the Internet of Things. Electronics, 8.","DOI":"10.3390\/electronics8020170"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lee, J.J., Hwang, H., Hong, S.C., and Lee, J.Y. (2022). Effect of Air Purification Systems on Particulate Matter and Airborne Bacteria in Public Buses. Atmosphere, 13.","DOI":"10.3390\/atmos13010055"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Casanova-Chafer, J., Romero, A., Vilanova, X., Mitrovics, J., Meneses-Albala, E., Montalban-Faet, G., Felici-Castell, S., Perez-Solano, J.J., and Fayos-Jordan, R. (2025). Assessment of a Multisensor ZPHS01B-Based Low-Cost Air Quality Monitoring System: Case Study. Electronics, 14.","DOI":"10.3390\/electronics14081531"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Caselles Nu\u00f1ez, J.G., Contreras Negrette, O.A., de Jes\u00fas Bele\u00f1o S\u00e1enz, K., and D\u00edaz S\u00e1enz, C.G. (2025). Design and Implementation of an Indoor and Outdoor Air Quality Measurement Device for the Detection and Monitoring of Gases with Hazardous Health Effects. Eng. Proc., 83.","DOI":"10.3390\/engproc2025083013"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Papp, E., Angyal, A., Furu, E., Szoboszlai, Z., T\u00f6r\u00f6k, Z., and Kert\u00e9sz, Z. (2022). Case Studies of Aerosol Pollution in Different Public Transport Vehicles in Hungarian Cities. Atmosphere, 13.","DOI":"10.3390\/atmos13050692"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446005","article-title":"Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis","volume":"17","author":"Concas","year":"2021","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Toma, C., Alexandru, A., Popa, M., and Zamfiroiu, A. (2019). IoT Solution for Smart Cities\u2019 Pollution Monitoring and the Security Challenges. Sensors, 19.","DOI":"10.3390\/s19153401"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/5\/395\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:46:42Z","timestamp":1779108402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/5\/395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["a19050395"],"URL":"https:\/\/doi.org\/10.3390\/a19050395","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]}}}