AI-DRIVEN ENVIRONMENTAL MONITORING USING IOT-BASED SENSOR NETWORKS
Yogesh S. Deshmukh, Kottala Sri Yogi, Sandip Rajendra Aher, Dr.Archana Santosh Ubale, Tanveer Ahmad Wani, Nishant Kulkarni
Abstract: AI-based environmental monitoring has become one of the key prerequisites of sustainable development, climate resilience, and smart management of resources. The old means of monitoring are usually characterized by low scalability, slow response and ineffective use of data. The paper introduces a full AI-driven environmental monitoring system that is based on sensor networks based on IoT to support real-time scalable and energy-efficient environmental observational procedures. The suggested solution combines the use of heterogeneous air, water, soil, noise, and climate sensors with smart communication protocol and adaptable network topology. The trend analysis and prediction of machine learning models used include linear regression, multiple linear regression, and support vectors machines, whereas spatiotemporal pattern extraction of large-scale environmental data streams with deep learning methods includes convolutional neural networks, recurrent neural networks, and long short-term memory networks. There are edge, fog, and cloud layers which work together to provide low-latency analytics, distributed intelligence and long-term data storage. The anomaly and event detection mechanisms are used to make the system more responsive by detecting abnormal environmental changes and sending timely alerts. It also includes adaptive learning and self-optimization mechanisms in the framework to enhance the accuracy of the model, energy efficiency, and network lifetime during dynamical environmental conditions. The essential problems associated with data quality, the generalization of the model, constraints of edge-AI, and interoperability are examined, and the possible research priorities to solve it. All in all, the suggested AI-IoT system has a solid potential of smart environmental monitoring, which can be used in pollution control, smart agriculture, and disaster early-warning systems.
Keywords: Environmental monitoring; Internet of Things; Artificial intelligence; machine learning Deep learning; Edge computing
DOI: 10.24874/PES08.03.007
Recieved: 03.06.2026 Revised: 15.07.2026 Accepted: 19.08.2026
UDC:
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