Design and Performance Evaluation of an ESP32-Based Internet of Things Smart Lighting Control System
Keywords:
Blynk, ESP32, Internet of Things, Smart home, Smart lighting, Wi-FiAbstract
The rapid advancement of Internet of Things (IoT) technology has significantly transformed smart home automation by enabling real-time monitoring and remote control of household electrical devices. Among various smart home applications, lighting control systems have attracted considerable attention due to their potential to improve energy efficiency, operational convenience, and user accessibility. However, conventional lighting systems still rely primarily on manual switching, often resulting in unnecessary electricity consumption and inefficient energy utilization. Therefore, this study aimed to design, implement, and evaluate the performance of an ESP32-based Internet of Things smart lighting control system employing Wi-Fi communication and the Blynk cloud platform. An engineering design and experimental evaluation approach was adopted, consisting of system design, hardware and software development, prototype implementation, and functional performance testing. The developed prototype integrates an ESP32 microcontroller, relay module, Wi-Fi communication, and smartphone-based control into a compact smart lighting system. Experimental results demonstrated that the prototype successfully established stable wireless communication between the ESP32 and the Blynk application, enabling real-time remote operation with reliable relay switching performance. The lighting devices responded accurately to ON/OFF commands transmitted through the smartphone application, while the overall system exhibited stable operation during repeated testing. The integrated architecture effectively combines embedded systems, cloud computing, wireless networking, and electrical switching into a practical smart home automation platform. Furthermore, the proposed system provides a low-cost, energy-efficient, and user-friendly solution for residential lighting management while demonstrating the practical application of Internet of Things technology in engineering education. Future research should focus on integrating occupancy sensors, ambient light sensors, energy consumption monitoring, artificial intelligence algorithms, and enhanced cybersecurity mechanisms to improve system autonomy, operational efficiency, and data security in next-generation smart home applications.
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