AN INTELLIGENT IOT-BASED SYSTEM FOR REAL-TIME MONITORING AND PREDICTIVE ANALYTICS

The rapid advancement of Internet of Things (IoT) technologies has enabled continuous real time monitoring across various domains, yet the effective utilization of the generated data for predictive decision-making remains a challenge. This paper presents an intelligent IoT based system for real-time monitoring and predictive analytics that integrates distributed sensor networks, edge computing, and machine learning techniques to deliver proactive and data-driven insights. The proposed system continuously collects heterogeneous data from IoT sensors and performs real-time preprocessing at the edge layer to reduce latency and network overhead. Predictive analytics models, including time-series forecasting and supervised machine learning algorithms, are employed to analyze both streaming and historical data for trend detection, anomaly identification, and future state prediction. The architecture is designed to ensure scalability, reliability, and data security while supporting seamless integration with cloud-based analytics platforms. Experimental evaluation shows that the proposed system achieves higher prediction accuracy and faster response times compared to conventional cloud-centric IoT monitoring frameworks. By enabling early warnings and intelligent decision support, the system enhances operational efficiency and reliability. The proposed framework is adaptable to a wide range of applications, including smart cities, healthcare monitoring, industrial automation, and environmental surveillance, making it a robust solution for next-generation intelligent monitoring systems.