Year: 2025
Type: article
Abstract: : Smart home is slowly but steadily becoming a part of our daily life in today's world. In this context, Intelipole, an IoT and Machine Learning (ML)-powered smart home system designed for advanced home security and automatic IoT device control, has been proposed. With the rise of IoT-connected devices, the integration of ML techniques is essential for enabling real-life predictions and automating device control. To achieve this, synthetic data along with a portion of real-time sensor data is collected to train the system's control models. Key features used include human presence count and environmental factors such as temperature, humidity, and luminosity. The system predicts appropriate control levels for devices like lights, fans, and ACs. The architecture includes real-time sensors, IP cameras for person counting, a microprocessor, cloud storage for historical data and ML models, and manual mobile control via Wi-Fi. The development process involves data collection, preprocessing, relevant feature selection, dataset splitting, and model implementation. Model performance, assessed using accuracy, precision, recall, and F1 score, shows strong potential for realworld application. Future work includes integrating user preferences and embedded systems to improve system adaptability and efficiency.
Author: Aishwarya Jannu
Language: English
Topic: IoT-based Smart Home Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410469266