An IoT–GIS integrated architecture for real-time urban noise monitoring and intelligent management

Authors

https://doi.org/10.22105/sci.v2i4.50

Abstract

Urban noise pollution, exacerbated by traffic, construction, and high population density, seriously threatens public health and quality of life. While effective on a small scale, traditional noise monitoring methods fail to provide the real-time data needed for widespread urban applications. This paper explores the potential of the Internet of Things (IoT) technology for noise monitoring in urban environments, examining an IoT architecture that integrates Wireless Sensor Networks (WSNs), cloud computing, and machine learning to provide comprehensive noise data collection and analysis. Key case studies from Barcelona, New York, and Delhi highlight real-world applications, and challenges such as data privacy, sensor calibration, and scalability are examined with proposed solutions. By adopting IoT-enabled noise monitoring, cities can implement data-driven noise management policies that improve public health and urban living conditions.

Keywords:

Internet of things, Noise monitoring, Urban management, Wireless sensor networks, Smart cities, Environmental pollution

References

  1. [1] Van Kempen, E., Casas, M., Pershagen, G., & Foraster, M. (2018). WHO environmental noise guidelines for the European region: A systematic review on environmental noise and cardiovascular and metabolic effects: A summary. International journal of environmental research and public health, 15(2), 379. https://doi.org/10.3390/ijerph15020379
  2. [2] Münzel, T., Gori, T., Babisch, W., & Basner, M. (2014). Cardiovascular effects of environmental noise exposure. European heart journal, 35(13), 829–836. https://doi.org/10.1093/eurheartj/ehu030
  3. [3] Sankar Manthina, B., Gujar, S., Chaudhari, S., Vemuri, K., & Chhirolya, S. (2025). IoT-based noise monitoring using mobile nodes for smart cities. https://arxiv.org/abs/2509.00979
  4. [4] Luo, L., Qin, H., Song, X., Wang, M., Qiu, H., & Zhou, Z. (2020). Wireless sensor networks for noise measurement and acoustic event recognitions in urban environments. Sensors, 20(7), 2093. https://doi.org/10.3390/s20072093
  5. [5] Chen, L. J., Saraswat, S., Ching, F. S., Su, C. Y., Huang, H. L., & Pan, W. C. (2025). Development and implementation of EcoDecibel: A low-cost and IoT-based device for noise measurement. Ecological informatics, 85, 102968. https://doi.org/10.1016/j.ecoinf.2024.102968
  6. [6] Zhang, Z. (2025). Environmental noise monitoringand managementinthe contextof artificial intelligence. International journal of education and social development, 2(3), 90–95. https://ijesd.com/index.php/ijesd/article/view/105/101
  7. [7] Obaid, H. S., Al-Shareefi, N. A., & Abbas, S. A. (2019). Internet of things and wireless sensor networks for environmental noise sensing: Issues and challenges. Journal of southwest jiaotong university, 54(6), 181–187. https://doi.org/10.35741/issn.0258-2724.54.6.24
  8. [8] Mydlarz, C., Sharma, M., Lockerman, Y., Steers, B., Silva, C., & Bello, J. P. (2019). The life of a New York City noise sensor network. Sensors, 19(6), 1415. https://doi.org/10.3390/s19061415
  9. [9] Central Pollution Control Board. (2015). Protocol for ambient level noise monitoring. https://cpcb.nic.in/openpdffile.php?id=UmVwb3J0RmlsZXMvNDU2XzE1MDIxNjk1MDBfbWVkaWFwaG90bzE0NTE3LnBkZg==
  10. [10] Central Pollution Control Board. (2016). Status of ambient noise level in India. https://cpcb.nic.in/noise_data/Noise_Report_2016.pdf
  11. [11] Bonilla, V., Campoverde, B., & Yoo, S. G. (2023). A systematic literature review of LoRaWAN: Sensors and applications. Sensors, 23(20), 8440. https://doi.org/10.3390/s23208440
  12. [12] Foster, N., McKeown, N., Rexford, J., Parulkar, G., Peterson, L., & Sunay, O. (2020). Using deep programmability to put network owners in control. ACM SIGCOMM Computer Communication Review, 50(4), 82-88. https://doi.org/10.1145/3431832.3431842
  13. [13] Sasnauskas, R., Landsiedel, O., Alizai, M. H., Weise, C., Kowalewski, S., & Wehrle, K. (2010). KleeNet: discovering insidious interaction bugs in wireless sensor networks before deployment [presentation]. Proceedings of the 9th acm/ieee international conference on information processing in sensor networks (pp. 186–196). https://doi.org/10.1145/1791212.1791235
  14. [14] Koop, G., McKitrick, R., & Tole, L. (2010). Air pollution, economic activity and respiratory illness: Evidence from Canadian cities, 1974--1994. Environmental modelling & software, 25(7), 873–885. https://doi.org/10.1016/j.envsoft.2010.01.010
  15. [15] Fan, Y., Chong, Y. S., Choolani, M. A., Cregan, M. D., & Chan, J. K. Y. (2010). Unravelling the mystery of stem/progenitor cells in human breast milk. PloS one, 5(12), e14421. https://doi.org/10.1371/journal.pone.0014421
  16. [16] Sait, S., Abbas, Y., & Boubenider, F. (2015). Estimation of thin metal sheets thickness using piezoelectric generated ultrasound. Applied acoustics, 99, 85–91. https://doi.org/10.1016/j.apacoust.2015.05.011
  17. [17] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: vision and challenges. IEEE internet of things journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
  18. [18] Gupta, S. K., & Pradhan, S. (2021). A review of recent advances and the role of nanofluid in solar photovoltaic thermal (PV/T) system. Materials today: proceedings, 44, 782–791. https://doi.org/10.1016/j.matpr.2020.10.708
  19. [19] Yang, H., Li, Y., Zhou, H., Zhao, Y., & Song, L. (2022). A research on the sharing platform of wild bird data in yunnan province based on blockchain and interstellar file system. Sensors, 22(18), 6961. https://doi.org/10.3390/s22186961
  20. [20] Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2017). Online advertising: Analysis of privacy threats and protection approaches. Computer communications, 100, 32–51. https://doi.org/10.1016/j.comcom.2016.12.016
  21. [21] Saldana-Barrios, J. J., Aguilar, E., Ng, W., & Orocu, R. (2023). Designing an IoT-based system for monitoring noise levels in the computer science faculty and library of the technological university of Panama. Sensors, 23(22), 9083. https://doi.org/10.3390/s23229083
  22. [22] Alvear, O., Calafate, C. T., Cano, J. C., & Manzoni, P. (2018). Crowdsensing in smart cities: Overview, platforms, and environment sensing issues. Sensors, 18(2), 460. https://doi.org/10.3390/s18020460
  23. [23] Fantozzi, S., Coloretti, V., Piacentini, M. F., Quagliarotti, C., Bartolomei, S., Gatta, G., & Cortesi, M. (2022). Integrated timing of stroking, breathing, and kicking in front-crawl swimming: A novel stroke-by-stroke approach using wearable inertial sensors. Sensors, 22(4), 1419. https://doi.org/10.3390/s22041419
  24. [24] Haq, A. U., Li, J. P., Saboor, A., Khan, J., Wali, S., Ahmad, S., … & Zhou, W. (2021). Detection of breast cancer through clinical data using supervised and unsupervised feature selection techniques. IEEE access, 9, 22090–22105. https://doi.org/10.1109/ACCESS.2021.3055806
  25. [25] Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., Ramos-Munoz, J. J., Lorca, J., & Folgueira, J. (2017). Network slicing for 5G with SDN/NFV: Concepts, architectures, and challenges. IEEE communications magazine, 55(5), 80–87. https://doi.org/10.1109/MCOM.2017.1600935
  26. [26] Gaur, A., Scotney, B., Parr, G., & McClean, S. (2015). Smart city architecture and its applications based on IoT. Procedia computer science, 52, 1089–1094. https://doi.org/10.1016/j.procs.2015.05.122

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Published

2025-12-23

Issue

Vol. 2 No. 4 (2025): Smart City Insights

Section

Articles

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How to Cite

Ghanbari Talouki, A., & Ghasem Abadi, N. (2025). An IoT–GIS integrated architecture for real-time urban noise monitoring and intelligent management. Smart City Insights, 2(4), 205-213. https://doi.org/10.22105/sci.v2i4.50

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