A MODULAR IOT PLATFORM FOR NEXT-GENERATION SMART HOMES: ARCHITECTURE, REAL-TIME CONTROL, AND EDGE AI READINESS
Authors
0000-0002-4459-9436
0009-0009-9366-3017
0009-0000-7165-7497
Keywords
Internet of things (IoT), Smart home automation, Modular architecture, Edge computing, Real-time monitoring, Energy optimization, Edge ai, Event-driven control
Abstract
Rapid technological progress and increasingly pressing needs for energy efficiency, safety, and personalised comfort have driven the development of intelligent systems for residential automation. This paper presents the design and implementation of a modular IoT smart-home system based on a microcontroller architecture with real-time data processing. The developed prototype integrates sensor modules for detecting temperature, humidity, air quality, illuminance, vibration, precipitation, and flame, as well as actuators for automated control of windows, doors, lighting, ventilation, and alarm mechanisms. The system is connected to a mobile application that enables monitoring and interactive control in real time, and users can define scenarios such as ānight modeā or āaway modeā. Special emphasis in the design is placed on the systemās modularity, its energy optimisation, and the ability to adapt behaviour based on historical data and user habits. The systemās functionality was tested on a physical model and in real conditions, establishing that it reacts within a time window of 1ā3 seconds from the moment a change in environmental parameters is detected. The obtained results indicate significant potential for integrating microcontrollers, an IoT platform, and adaptive control algorithms in the domain of smart buildings and future concepts of urban automation. The paper also opens up avenues for further development with integrated machine-learning and artificial-intelligence algorithms aimed at achieving fully autonomous control of the residential environment. This iteration includes a fully functional physical prototype and application, while the predictive AI part is evaluated offline via simulation/emulation based on recorded logs, without on-device inference.
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