AI-ENABLED IOT ARCHITECTURE FOR COMPREHENSIVE ENVIRONMENTAL MONITORING AND PREDICTION

Abstract

Accurate environmental monitoring and prediction are essential for effective ecosystem management, pollution control, and disaster prevention. Traditional monitoring systems often face challenges such as limited scalability, delayed data acquisition, and inadequate predictive capabilities. This paper proposes an AI-enabled Internet of Things (IoT) framework for comprehensive environmental monitoring and prediction. The system integrates heterogeneous IoT sensor networks, hybrid edge–cloud computing, and advanced AI models, including machine learning and deep learning algorithms, to monitor environmental parameters such as temperature, humidity, air quality, and water quality in real time. Preliminary data processing is conducted at the edge to reduce latency, while cloud servers support large-scale storage, advanced analytics, and model training. The proposed framework enables accurate short-term and long-term prediction, anomaly detection, and automated alert generation for critical environmental events. Performance evaluation demonstrates high prediction accuracy, low latency, energy-efficient operations, and improved system scalability compared to traditional approaches. The results highlight the potential of AI-enabled IoT systems to enhance environmental decision-making and provide timely insights for researchers, policymakers, and environmental agencies