Intelligent Environmental Monitoring System using AI & IoT for Realtime Data Analytics and Remote Monitoring
DOI:
https://doi.org/10.15662/IJEETR.2026.0802214Keywords:
Artificial Intelligence (AI), Internet of Things, Environmental Monitoring, Sensors, Real-Time Data Analy cs, Remote Monitoring, Cloud Compu ng, Air Quality MonitoringAbstract
Environmental monitoring has become an imperative global requirement due to escalating pollution levels and rapid climate change. Conventional monitoring techniques often rely on manual data logging, which is inefficient, time-consuming, and incapable of providing real-time ecological insights. To address these critical limitations, this paper presents a comprehensive Intelligent Environmental Monitoring System that seamlessly integrates the Internet of Things (IoT) and Artificial Intelligence (AI).The proposed architecture employs a robust network of precision sensors to continuously capture vital environmental parameters, including ambient temperature, humidity levels, air quality indices (PM2.5, PM10), and the concentration of hazardous gases such as carbon monoxide (CO) and carbon dioxide (CO2). An advanced microcontroller unit, such as an ESP32 or NodeMCU, serves as the edge processing gateway, aggregating and digitizing sensor data before transmitting it to a centralized cloud infrastructure via wireless communication protocols.
Unlike traditional frameworks that are restricted to mere data collection, this system incorporates machine learning algorithms to perform dynamic data analytics. The integrated AI module intelligently identifies complex pollution patterns, detects hazardous anomalies, and utilizes predictive forecasting to anticipate potential environmental risks before they escalate. Furthermore, the system features an automated early-warning mechanism that instantly triggers notifications through SMS or mobile alerts when predefined safety thresholds are breached, facilitating swift preventive interventions.For user accessibility, the framework provides an intuitive web and mobile dashboard for the remote visualization of both real-time conditions and historical data trends. Ultimately, this highly scalable, energy-efficient, and cost-effective solution is highly adaptable for diverse operational domains—ranging from smart city infrastructure and precision agriculture to industrial safety and healthcare. The fusion of AI and IoT technologies significantly elevates the reliability and accuracy of ecological monitoring, paving the way for proactive environmental management and longterm sustainable development.
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