DEVELOPMENT OF AUTOMATED HOME MANAGEMENT SYSTEM USING ARDUINO UNO WITH MOTION TRACKER CAMERA

Abstract

The rapid advancement of technology continues to transform daily life, accelerating the shift toward smart home environments that combine artificial intelligence (AI) and the Internet of Things (IoT). This study focuses on developing an innovative smart home management system that prioritizes security, automation, and energy efficiency. The system integrates advanced components including Amazon Alexa for natural language processing, RFID technology for secure access, motion-tracking cameras, smoke sensors, and regulated exhaust fans. These devices are interconnected through microcontrollers and relay modules to ensure seamless operation and intelligent control of home automation features. The research utilizes a functionality-driven design approach focused on optimizing home security, automation, and energy management. Core components include Amazon Alexa, RFID access, motion and smoke detectors, and Arduino Uno microcontrollers. The system underwent rigorous testing for operational performance, security reliability, and energy efficiency. Additionally, a user evaluation survey was conducted to assess satisfaction, ease of use, and system responsiveness in real-world scenarios. The developed prototype successfully integrated all targeted components, demonstrating high performance across key metrics. RFID access achieved 98% accuracy, while Alexa voice command recognition reached 95%. Smoke detection proved to be 100% reliable. The motion sensor responded within 1.3 seconds, and system uptime over 48 hours averaged 99.1%. User feedback indicated strong approval, with 92% expressing satisfaction, 94% noting ease of use, and 90% reporting improved home security. The smart home system effectively met its goals of improving security, automation, and energy management. The integration of voice control, RFID access, and responsive sensors contributed to a reliable and user-friendly experience. However, minor latency issues were observed due to the system’s dependence on Wi-Fi connectivity. The study was also limited by its sample size and testing environment. Future development should include broader testing with diverse user groups and explore added features such as biometric authentication and mobile app integration to enhance scalability and usability. Overall, the findings underscore the system’s potential as a viable solution for modern home automation.