An Internship Study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management

Internship Report (M. Eng.) - Asian Institute of Technology, 2025

With the rapid advancement of technology, AI-powered surveillance systems are becoming increasingly essential for security and monitoring applications. The AS Box, an AI-driven computer vision device, is designed to perform license plate recognition, motion detection, and other real-time surveillance tasks. However, configuring the AS Box for network connectivity remains a challenge, requiring users to manually connect peripherals and input Wi-Fi credentials, making the setup process complex and inefficient. This study focuses on the development of a mobile application to streamline the network configuration process of the AS Box. The application, developed using Flutter, facilitates hotspot-based pairing, Wi-Fi/LAN connectivity, and automated IP address retrieval. A FastAPI backend is integrated into the AS Box to handle network requests and ensure seamless communication between the device and the mobile application. The system also incorporates SQLite for local storage, allowing users to manage multiple AS Boxes efficiently. The development process follows the Agile methodology to ensure rapid iterations, software quality, and risk reduction. Extensive unit testing and quality assurance are carried out, focusing on backend stability, API reliability, and overall system performance. The final product undergoes rigorous testing phases, from internal validation to beta deployment, ensuring a robust and user-friendly experience before full-scale implementation.By automating network setup and device management, this mobile application enhances the usability and scalability of the AS Box, making AI-powered surveillance solutions more accessible, efficient, and user-friendly.