Smart Monitoring in the Internet of Things Using Deep Learning for Real-Time Analysis and Prediction

Subject Areas : Electrical engineering (electronics, telecommunications, power, control)

sina nasrolahi ¹ , seyedebrahim dashti ^{2 *}

1 - Islamic Azad university
2 -

Received: 2025-01-07 Accepted : 2025-09-21 Published : 2025-10-06

Keywords: Internet of Things, Deep Learning, Convolutional Neural Network, Recurrent Neural Network, Real-time Anomaly Detection,

Abstract :

With the increasing use and applications of the Internet of Things, especially in industry, the need for smart and real-time monitoring is strongly felt. In this study, the combination of the Internet of Things (IoT) and deep learning with CNN and RNN hybrid models is proposed in real-time monitoring and predictive analysis. This method presents a novel system that combines IoT sensor data using CNN to analyze spatial features and RNN to process time sequences. This system detects anomalies and predicts possible failures, and enables preventive interventions to reduce operating costs and increase efficiency. The system was evaluated with a dataset containing more than one million records (parameters such as temperature, humidity, and pressure). The results showed that the accuracy of temperature detection increased to 99.2%. This high accuracy indicates the system's ability to accurately predict critical failures.

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