## 2 State of the Art
### Internet of Things and Smart Classroom  
First applications of smart classroom aimed to give remote student a real live learning experience. In [13], the authors have integrated voice-recognition, computer vision, and other technologies in order to provide both remote students and teachers more ways to interact. The classroom contains several cameras and some of them are used to track teacher movements, while the others show the classroom from different views. Artificial intelligence is used to choose which camera should broadcast video to students at a time. Speech recognition allows teachers to control the classroom system with the use of voice. Furthermore, the teaching board is a touch sensitive screen, while the teacher's pen also containsa laser pointer, which is used as an interactive tool. Teacher and students' identities are verified with face and voice biometrics. A use of IOT in a classroom environment was described in [14]. The authors used affordable sensors to measure the impact of environmental conditions on the student's learning focus. Five parameters of physical environment were tracked: CO\({}_{2}\), temperature, humidity, noise level, and lecturer's voice. The study included data collected from 197 students during 14 different lectures. The authors have extracted 22 features from the lecturer's voice, combined them with other parameters and applied various classifiers in order to find out which was the best in correctly separating focused and unfocused segments of a student's attention. AdaBoost M1 had the best recognition accuracy. The results of the study were meant to be later used to implement a smart classroom which would be able to determine if the classroom environment is optimized to maximize student attention. Research study [15] presents a smart classroom with the use of NFC (Near Field Communication) technology. The NFC was used in combination with LED (Light-Emitting Diodes) displays and multi-touch displays for attendance management, tracking the student's location and enabling the real-time student feedback. An evaluation of the proposed system effect on student's attitude towards science education was given. The different aspects evaluated were the self-concept in computer science, learning computer science outside of school, learning computer science at school, future participation in computer science, and the importance of computer science. Case study results showed improvement in the student's attitudes. In [16], an IoT approach was used for classroom management. Arduino and NFC were used for teacher authentication, and collected data is transmitted to master node via the radio frequency, while the master node stores collected data on cloud storage. Collected data is sent to Xively server, an IoT platform owned by Google. A web app which uses Xively API and Google maps was created to allow visualization of collected data. Social networks were also used to propagate collected information. A service which allows combining several web applications called Zapier was used to publish data from Xively to Twitter. Applied approach showed that data collected from classrooms can be easily used by different applications. Application of IoT in education domain is described in [17]. The authors have developed a system which allows students to interact with objects in the laboratory. RFID (Radio-Frequency Identification) and NFC are used to identify objects. Students use their mobile phones to receive information about objects in front of them, and are assigned tasks related to those objects. System supports multimedia contents, such as animations, pictures, audio, and video. Content is uploaded online by the teaching personnel. The system was evaluated with two groups, experimental and control. The experimental group used the system for learning, while the control group used a traditional learning approach. System evaluation has shown a positive impact of the use of IoT as a tool for enhancing the teaching process. [PERSON] et al.