## 1 Introduction
### Background and Related Works
#### 1.1.2 Technologies in Smart Classrooms
In Reference [32], a system for giving in-class presenter real-time suggestions to improve the quality of the presentation based on deep learning-based emotion recognition was proposed. The proposed system in Reference [32] demonstrates a novel smart classroom application. However, the application needs lots of computation resource. Thus, a backend high-performance cloud service is needed for this application. In Reference [33], a framework is proposed to perform learning analytics tasks in a smart classroom. The communications between components are based on Simple Object Access Protocol (SOAP) Web Services. Web services can be registered, discovered, and invoked by agents. A middleware named AmiCL (Ambient Intelligence and Cloud Learning), is used for objects in the smart classroom to adjust behavior according to the context, and the requirements of the users. The learning analytic example in Reference [33] demonstrates the process of recognizing students, supervise learning process, apply analytic tasks, recommend learning resources, identify communities of students, reinforce concepts with AR technology, predict learning performance, and evaluate the quality of learning resources. In Reference [34], a model for solving the performance issue of location-aware application in smart classrooms is proposed. In Reference [35], a storage management system is proposed to improve energy consumption and performance in smart classroom multi-media applications. In Reference [36], Preference Instrument of Smart Classroom Learning Environments (PI-SCLE) is proposed for understanding students' preferences toward the smart classroom. According to the instrument documentation of Reference [36], communication with others, idea sharing, enough workspace, classroom atmosphere, flexible furniture arrangements, interactive visual displays, multiple small group discussions, wireless connectivity, user-friendly learning tools, present learning topics in various ways, share contents with digital devices, face-to-face digital instruction, are topics that should be considered while building a smart classroom. As a summary, smart classrooms may be used as a tool or target to do education-related study, technology-related factors are positive for learning outcome, and technology problems may cause user dissatisfaction. Therefore, technologies matters to learning activities and the ways for building, integrating, and maintaining a massive amount of smart classrooms are included in the proposed architecture. Technologies and devices used in this work, including Raspberry Pi, Python, Flask, PHP, MariaDB, RFID, RS232, and RS485 are briefly addressed as follows:  
* Raspberry Pi [7]: Raspberry Pi Foundation, which is a UK-based charity, starts to build a series of low-cost, high-performance palm-size computer boards for teaching computer-related subjects since 2012. Raspberry Pi single board computers become very popular because of its price, quality, compatibility, documentation, and community support. In our proposed model, Raspberry Pi is used to integrate hardware devices, which have various kinds of interfaces, and provide web application interfaces for high-level applications. * Python [37]: Python is a very popular general purpose computer programming language. The official operating system of Raspberry Pi provides a built-in python development environment, and official documents of Raspberry Pi provide a massive amount of example of using Python to get things done. Therefore, Python is used in this work to develop device control procedures and web application interfaces on Raspberry Pi boards. * Flask [38]: Flask is one of the most popular Python web application frameworks. With Flask, which is a lightweight framework, things are simple, and developers can start to build web applications without installing many packages and configuration. Therefore, prototype implementation and proof of concepts can be done quickly, and researchers start to use Flask in research projects [39; 40; 41; 42; 43; 44; 45; 46; 47] in recent years.