IoT-based environmental sensing solutions for smart city monitoring

Authors

  • Abhinav Baranwal School of Computer Science Engineering, KIIT University, Bhubaneswar, India

DOI:

https://doi.org/10.22105/sci.v2i1.28

Keywords:

Internet of Things , Smart city monitoring, Environment sensing, Real time data, Predictive modeling

Abstract

IoT-based environmental sensing solutions are pivotal for smart city monitoring, providing real-time data that enhances urban management, sustainability, and quality of life. These systems integrate sensors to measure and monitor various environmental parameters such as air quality, temperature, humidity, noise levels, and water quality. Data collected from these sensors is transmitted to centralized systems for analysis, often using cloud-based or edge-computing architectures. The insights from this data help city authorities make informed decisions about environmental policies, urban planning, and resource allocation. Furthermore, IoT-enabled environmental monitoring facilitates predictive maintenance, anomaly detection, and emergency response, optimizing city operations and ensuring a healthier, safer environment for citizens.

References

Raj, E. F. I., Appadurai, M., Darwin, S., & Rani, E. F. I. (2022). Internet of things (IoT) for sustainable smart cities. In Internet of things (pp. 163–188). CRC Press. https://doi.org/10.1201/9781003219620-9

Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), 5206. https://doi.org/10.3390/s23115206

Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., … & Chung, Y. S. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in environmental science, 12, 1336088. https://doi.org/10.3389/fenvs.2024.1336088

Adjovu, G. E., Stephen, H., James, D., & Ahmad, S. (2023). Overview of the application of remote sensing in effective monitoring of water quality parameters. Remote sensing, 15(7), 1938. https://doi.org/10.3390/rs15071938

Kumar, S., Mohapatra, H., & Dalai, A. K. (2024). Enhancing energy efficiency in wireless sensor networks via clustering approach. 2024 4th international conference on artificial intelligence and signal processing (AISP) (pp. 1–6). IEEE. https://doi.org/10.1109/AISP61711.2024.10870799

Rivera, A., Ponce, P., Mata, O., Molina, A., & Meier, A. (2023). Local weather station design and development for cost-effective environmental monitoring and real-time data sharing. Sensors, 23(22), 9060. https://doi.org/10.3390/s23229060

Selvam, A. P., & Al-Humairi, S. N. S. (2023). The impact of IoT and sensor integration on real-time weather monitoring systems: A systematic review. https://doi.org/10.21203/rs.3.rs-3579172/v1

Swaminathan, S., Guntuku, A. V. S., Sumeer, S., Gupta, A., & Rengaswamy, R. (2022). Data science and IoT based mobile monitoring framework for hyper-local PM2. 5 assessment in urban setting. Building and environment, 225, 109597. https://doi.org/10.1016/j.buildenv.2022.109597

Iqubal, S., Khan, S., Pant, N., Sarkar, S., Rey, T., & Mohapatra, H. (2025). A study on IoT-enabled smart bed with brain-computer interface for elderly and paralyzed individuals. In Future innovations in the convergence of AI and internet of things in medicine (pp. 61–88). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7703-1.ch004

Forkan, A. R. M., Kang, Y.-B., Marti, F., Banerjee, A., McCarthy, C., Ghaderi, H., … & Jayaraman, P. P. (2024). Aiot-citysense: Ai and iot-driven city-scale sensing for roadside infrastructure maintenance. Data science and engineering, 9(1), 26–40. https://doi.org/10.1007/s41019-023-00236-5

Subramaniam, S., Raju, N., Ganesan, A., Rajavel, N., Chenniappan, M., Prakash, C., … & Dixit, S. (2022). Artificial intelligence technologies for forecasting air pollution and human health: A narrative review. Sustainability, 14(16), 9951. https://doi.org/10.3390/su14169951

Nourildean, S. W., Hassib, M. D., & Mohammed, Y. A. (2022). Internet of things based wireless sensor network: a review. Indonesian journal of electrical engineering and computer science, 27(1), 246–261. https://doi.org/10.11591/ijeecs.v27.i1.pp246-261

Liu, C. C., Lin, T. C., Yuan, K. Y., & Chiueh, P. Te. (2022). Spatio-temporal prediction and factor identification of urban air quality using support vector machine. Urban climate, 41, 101055. https://doi.org/10.1016/j.uclim.2021.101055

Ullah, S., Zheng, J., Din, N., Hussain, M. T., Ullah, F., & Yousaf, M. (2023). Elliptic curve cryptography; applications, challenges, recent advances, and future trends: A comprehensive survey. Computer science review, 47, 100530. https://doi.org/10.1016/j.cosrev.2022.100530

de Neira, A. B., Kantarci, B., & Nogueira, M. (2023). Distributed denial of service attack prediction: challenges, open issues and opportunities. Computer networks, 222, 109553. https://doi.org/10.1016/j.comnet.2022.109553

Published

2025-03-22

How to Cite

IoT-based environmental sensing solutions for smart city monitoring. (2025). Smart City Insights, 2(1), 1-16. https://doi.org/10.22105/sci.v2i1.28