A Unified IoT Framework for Behaviour-Adaptive Appliance Control in Smart Residential Spaces

Abstract

The paper focuses on the architectural development of an IoT Framework that unifies the way you control various smart appliances. It takes a nodal approach to reduce the need of redundant sensors across various appliances. The benefits of this approach is that, it provides for a more inclusive automation adapting to the behaviour of the user.

Introduction

The text discusses the vision of a fully integrated smart home where all devices communicate and adapt seamlessly to user behavior, unlike today’s mostly isolated smart gadgets. Current systems like Google Nest and Samsung SmartThings have limitations such as sensor redundancy, simple rule-based automation, and lack of holistic, adaptive control.

To address these issues, the authors propose a centralized IoT smart home system architecture comprising Room Sensor Nodes (RSNs) that gather environmental data and a Central Control Unit (CCU) that processes this data, learns user habits, and controls appliances accordingly. Communication relies on MQTT, HTTP REST APIs, and Wi-Fi to ensure efficient, real-time data exchange.

Mathematical models aggregate sensor data and compute comfort scores to guide appliance control, balancing user comfort and energy efficiency. The system also predicts user behavior to automate device management.

A Python-based simulation with three rooms tested the system’s ability to maintain comfort and reduce energy use by learning habits and adapting appliance operation dynamically. Results show the system effectively manages comfort and energy, though challenges include network reliability, initial learning period, and security considerations.

Overall, the proposed system offers centralized intelligence, personalized automation, and scalability but requires robust communication and security measures for real-world deployment.

Conclusion

In this study, we introduce a smart home design that brings all the “brains” into one central hub while still using flexible, plug-and-play sensor modules in each room. By having room-level sensors talk to a single control unit, we cut down on extra hardware and streamline how everything works—improving both your comfort and your energy savings. Our main achievements are threefold: a lightweight network of sensor nodes, a habit-learning algorithm that predicts what you’ll do next, and an energy-smart control system that balances efficiency with your personal comfort. Looking ahead, we see exciting possibilities like adding facial or voice recognition, tapping into more powerful AI for deeper personalization, rolling this out in offices or hotels, and even blending edge-and-cloud processing to keep things fast and efficient in real time.

References

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Copyright

Copyright © 2025 Prashant Pandey, Rajnish Kumar Rai, Shivam Mishra, Praveen Kumar Pandey, Smrita Chaubey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.