## 4 System Model
### Technical Layer
#### 4.1.1 IoT Layer  
In the IoT layer, a variety of smart devices and sensors are employed to transmit data to servers operating within the fog layer. These devices (e.g., sensors, cameras, smart boards, tablets, laptops, and more) are typically installed in institutions. They can operate automatically or be controlled by users such as students, teachers, administrators, or parents. This section provides a detailed exploration of these devices and sensors. (a) Radio frequency identification: The RFID automatically identifies objects by frequencies attached to them. RFID technology enables automatic attendance tracking for students and staff in institutions. Smart cards with RFID tags are issued to them and RFID readers are installed at the class entrance. As individuals enter, the system records their entry times in the database. This eliminates the need for roll call, which can waste valuable class time. Moreover, accurate timing reports encourage teachers and students to be punctual for their classes, which is often lacking in underdeveloped countries. \[\alpha_{att}(x)= \begin{cases}Present&\text{if }F_{ID}==std_{ID}\\ Absent&\text{if }F_{ID}\
eq std_{ID}\\ Nil&\text{Otherwise}\end{cases}  \tag{1}\]  
The \(\alpha_{att}(x)\) data are updated on fog and cloud servers. This alert message is also forwarded to the guardians of the learners. (b) Smart Board: Learners forget what they listen to, they retain more by doing and watching. Interactive smart boards aid in learning. Animated presentations and teaching methods are becoming increasingly popular for clarifying concepts. Instead of passively listening to theoretical lectures, students actively participate in practical activities that enhance their understanding. The smart board is a central feature of modern classrooms. It employs contemporary teaching methodologies to keep students engaged. Teachers can display their own material or access resources from the cloud or the internet. Interactive boards are user-friendly, and anyone can operate them. Game-based activities can be seamlessly integrated to teach and stimulate students' interest in learning. (c) Smartphones: The internet is expected to host a large number of devices in the near future. Smartphones are widely accessible, even in low-income regions, and can serve as powerful learning tools for educators, learners, and parents. One of the emerging  
Figure 2: Structure of the technical layers in the SIF. pedagogical approaches that relies on smartphones is the flipped classroom, which has gained popularity in recent years. \[\alpha_{reports}(x)= \begin{cases}\begin{aligned} & Homework&&\text{if Updated}\\ & Attendance&&\text{if Updated}\\ & Assessments&&\text{if Updated}\\ & P_{Report}&&\text{if Updated}\\ & Meetings&&\text{if Updated}\\ & Nil&&\text{Otherwise}\end{aligned}\end{cases}  \tag{2}\]  
(d) Smart Sensors: Different types of smart sensors are installed in institutions. These sensors continuously monitor and capture data from various parameters within the institutions in real time. \[\alpha_{Env}(x)= \begin{cases}\begin{aligned} & Temp&&\text{if Requested}\\ & Humidity&&\text{if Requested}\\ & Air&&\text{if Requested}\\ & Sounds&&\text{if Requested}\\ & Vibrator&&\text{if Requested}\\ & Nil&&\text{Otherwise}\end{aligned}\end{cases}  \tag{3}\]  
(e) Smart Cameras: Institutions and classrooms use cameras to track and record their activities, which are transmitted to the fog layer in real time. Cameras enhance the safety and accountability of institutions. AI applications can analyze the learners' behavior from the camera data. \[\alpha_{Streaming}(x)= \begin{cases}\begin{aligned} & FogServer&&\text{Round the clock}\\ & CloudServer&&\text{if Requested}\\ & Parents&&\text{if Permitted}\end{aligned}\end{cases}  \tag{4}\]  
(f) Sound frequency identification: Classrooms are equipped with sound frequency identification (SFID), which measures the sound intensity in the rooms. If the sound intensity exceeds a predefined threshold, a message is sent to the principal's office. \[\alpha_{Noise}(x)=