IoT-Driven Smart City Water Ouality Monitoring with Cloud-Based Analytics
Abstract
This paper explores integrating Internet of Things (IoT) technologies with cloud-based analytics for effective water quality monitoring in smart cities. As urban populations grow, the demand for safe and clean drinking water intensifies, necessitating innovative solutions to monitor water quality in real-time. The proposed IoT-driven system employs a network of sensors to continuously collect data on critical water quality parameters such as pH, turbidity, and contaminant levels. This data is transmitted to cloud platforms where advanced analytics are applied to interpret the information, enabling timely responses to potential contamination events. By leveraging real-time monitoring capabilities, municipalities can enhance public health outcomes and ensure compliance with environmental regulations. The paper also discusses case studies demonstrating successful implementations of IoT-based water quality monitoring systems, highlighting their effectiveness in urban management.
Keywords:
Internet of things, Smart cities, Water quality monitoring, Cloud-based analytics, Real-time data, Public health, Environmental sustainabilityReferences
- [1] Bartram, J., & Ballance, R. (Eds.). (1996). Water quality monitoring: a practical guide to the design and implementation of freshwater quality studies and monitoring programmes. CRC press. https://www.who.int/publications/i/item/0419217304
- [2] Fakheri, S., Komazec, N., & Saberi Najafi, H. (2023). IoT-based river water quality monitoring. Computational Algorithms and Numerical Dimensions, 2(4), 216–220. https://doi.org/10.22105/cand.2023.166518
- [3] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
- [4] Amador-Castro, F., González-López, M. E., Lopez-Gonzalez, G., Garcia-Gonzalez, A., Díaz-Torres, O., Carbajal-Espinosa, O., & Gradilla-Hernández, M. S. (2024). Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication. Journal of Environmental Management, 352, 119959.https://doi.org/10.1016/j.jenvman.2023.119959
- [5] Essamlali, I., Nhaila, H., & El Khaili, M. (2024). Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon, 10(6), e27920. https://doi.org/10.1016/j.heliyon.2024.e27920
- [6] Chen, Y., & Han, D. (2018). Water quality monitoring in smart city: A pilot project. Automation in Construction, 89, 307-316. https://doi.org/10.1016/j.autcon.2018.02.008
- [7] Raj, A. H. (2015, November). Development of an intelligent sensor based inspection robot for closed environment. In 2015 5th Nirma University International Conference on Engineering (NUiCONE) (pp. 1-6). IEEE. https://doi.org/10.1109/NUICONE.2015.7449591
- [8] Iancu, G., Ciolofan, S. N., & Drăgoicea, M. (2024). Real-time IoT architecture for water management in smart cities. Discover Applied Sciences, 6(4), 191. https://doi.org/10.1007/s42452-024-05855-9
- [9] Srivastava, N., Sen, A., Srivastava, A., Ali, S. E., & Khare, S. (2025). Cost-benefit analysis of automated water quality management: Investment, savings, and strategic advantages. In Computational Automation for Water Security: Enhancing Water Quality Management (pp. 157–174). Elsevier. https://doi.org/10.1016/B978-0-443-33321-7.00021-4
- [10] Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. https://doi.org/10.1007/s10708-013-9516-8