Environmental Monitoring using Machine Learning and IoT: Applications, Challenges, and Cyber security Threats

Abstract

The convergence of the Internet of Things (IoT) and Machine Learning (ML) has transformed environmental monitoring, enabling real-time data acquisition, predictive analytics, and decision support for sustainable management of natural resources. IoT-based sensor networks offer continuous, low-cost, and scalable monitoring of air and water quality, while ML algorithms enhance accuracy through anomaly detection, calibration, and predictive modeling. This paper explores the applications, challenges, and cybersecurity threats associated with IoT–ML frameworks for environmental monitoring. Two case studies are presented: (i) Smart Air Quality Monitoring in Urban Cities, where low-cost IoT sensors combined with ML models such as Random Forest and LSTM provided improved forecasting of Air Quality Index (AQI), and (ii) IoT-enabled Water Quality Monitoring for Smart Agriculture, where classification and regression models supported irrigation management through predictive water safety assessment. Both cases demonstrate significant improvements in accuracy, cost-efficiency, and timeliness compared to traditional monitoring methods, but they also reveal challenges including sensor calibration, energy constraints, data imbalance, and security vulnerabilities such as spoofing, denial-of-service, and ransomware. The study underscores the importance of integrating cybersecurity frameworks with IoT–ML systems to ensure resilience, reliability, and trustworthiness. By analyzing technical, operational, and security aspects, this paper provides a holistic perspective on leveraging IoT and ML for sustainable environmental management.

Introduction

Environmental monitoring is increasingly critical due to rising pollution, climate change, and their effects on health, agriculture, and ecosystems. Traditional monitoring methods, though accurate, are slow, expensive, and limited in spatial and temporal resolution. Recent advances in Internet of Things (IoT) and Machine Learning (ML) technologies enable continuous, real-time, and cost-effective monitoring systems that improve data collection, analysis, and decision-making.

IoT sensors gather environmental data (e.g., air pollutants, water quality parameters) and transmit it to cloud platforms, where ML models analyze trends, predict pollution peaks, classify safety levels, and detect anomalies. Examples include LSTM networks forecasting air pollution and Random Forest classifiers assessing water quality. These systems support stakeholders like governments, farmers, and citizens with timely insights.

However, challenges persist, such as sensor calibration drift, energy constraints, data overload, ML model generalization, and cybersecurity vulnerabilities (e.g., data tampering, DoS attacks). Cybersecurity must be integral to monitoring frameworks to ensure data integrity and system trustworthiness.

The paper reviews literature on IoT and ML applications in air and water quality monitoring, highlighting gaps like lack of standardization, limited anomaly detection, underexplored cybersecurity in real deployments, and the need for multi-technique integration and computational workflows. Two case studies illustrate the synergy of IoT and ML: smart urban air quality monitoring for health advisories and IoT-enabled water quality monitoring for smart agriculture.

The study outlines foundational concepts in IoT architecture, Air Quality Index (AQI), Water Quality Index (WQI), machine learning models, data preprocessing, evaluation metrics, and cybersecurity measures. It proposes a structured methodology combining IoT data acquisition, ML-based analysis, decision support, and cybersecurity protections to advance environmental monitoring systems.

Future directions include integrating AI-driven anomaly detection, blockchain for security, and edge computing to enhance system reliability and scalability.

Conclusion

This study has demonstrated the transformative potential of integrating Internet of Things (IoT) technologies with Machine Learning (ML) for environmental monitoring, specifically in the domains of air quality and water quality management. The two case studies—urban air quality prediction using IoT sensor networks and ML, and water quality assessment for smart agriculture—highlight how real-time sensing combined with predictive analytics enables proactive decision-making, cost reduction, and enhanced public and ecological health outcomes. However, the findings also reveal persistent challenges. Technical issues such as sensor calibration, data drift, network reliability, and energy constraints limit the scalability of IoT deployments. On the analytical side, data imbalance, lack of generalizability, and the need for advanced anomaly detection remain critical concerns for ML models. Furthermore, cybersecurity threats—including data tampering, denial-of-service, and ransomware attacks—pose significant risks to the integrity, availability, and trustworthiness of environmental monitoring systems. Future work must focus on developing robust cybersecurity frameworks, energy-efficient IoT architectures, and adaptive ML models capable of handling heterogeneous, noisy, and large-scale environmental data. Interdisciplinary collaborations between computer scientists, environmental engineers, and policymakers will be vital to ensuring that IoT–ML systems are not only technically sound but also ethically and socially responsible. By addressing these gaps, IoT and ML can serve as powerful enablers of sustainable environmental management, contributing to global goals of climate resilience, public health, and resource optimization.

References

[1] Singh, Y., &Walingo, T. (2024). Smart Water Quality Monitoring with IoT Wireless Sensor Networks. Sensors, 24(9), 2871. [2] Johnson, T., & Woodward, K. (2025). Enviro-IoT: Calibrating Low-Cost Environmental Sensors in Urban Settings. arXiv preprint. [3] Moses, L., Tamilselvan, R., Karthikeyan, et al. (2020). IoT enabled Environmental Air Pollution Monitoring and Rerouting system using Machine learning algorithms. IOP Conference Series: Materials Science and Engineering, 955, 012005. [4] Rana, P., & Patil, B. P. (2023). Cyber security threats in IoT: A review. Journal (publisher), volume(issue). ([SAGE Journals][10]) [5] Lee, I. (2020). Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management. Future Internet, 12(9), 157. [https://doi.org/10.3390/fi12090157](https://doi.org/10.3390/fi12090157) ([MDPI][11]) [6] Johnson, T., & Woodward, K. (2025). Enviro-IoT: Calibrating Low-Cost Environmental Sensors in Urban Settings. arXiv preprint [7] Garcia, A., Saez, Y., Harris, I., Huang, X., & Collado, E. (2025). Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies. Artificial Intelligence Review. Volume 2025, Article (Issue 9). [8] Alsamrai, O., Redel-Macias, M. D., Pinzi, S., & Dorado, M. P. (2024). A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things. Sustainability, 16(11), 4353.

Copyright

Copyright © 2025 Amit Puri, Navneet Kaur, Kirandeep Kaur, Er. Sanjogdeep Singh, Charanjit Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.