# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 4 Implementation Case Study
### Problem Description
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm Name** & **Requirement Description** \\ \hline Working at Heights & Monitor the compliance of personnel wearing safety equipment in the screen, and determine whether protective railings are installed in high-risk areas for high-altitude operations \\ \hline Hot Work & Detect and analyze whether the workers in the screen are performing hot work correctly, and determine whether one person is operating and one person is monitoring according to regulations \\ \hline Lifting Operations & Determine whether there is anyone standing under the lifting arm in violation of regulations while the lifting equipment is performing lifting operations in the screen \\ \hline Loitering Detection & Automatically identify personnel entering and exiting the monitoring area and continuously track the duration of stay for statistical purposes \\ \hline Fatigue Work & Automatically identify whether there are workers who have been in the camera for a long time within the monitoring range \\ \hline Digital Meter Reading & Identify the instrument readings in the video \\ \hline Personnel Fall & Check if there is any falling behavior in the screen \\ \hline Wearing of Safe Helmet & Testing the behavior of personnel wearing standard safety helmets correctly on their heads \\ \hline Wearing Long Sleeves & Detect the positions of personnel and local target areas in the image, and judge the wearing of short sleeves based on compliance criteria \\ \hline Using Mobile Phones & Identify the behavior of workers using mobile phones within the designated area \\ \hline Smoking Detection & Real time monitoring of key fire prevention areas and designated monitoring and analysis points, and detection of smoking violations by power station personnel \\ \hline Sleeping on Duty & Automatically identify whether personnel are sleeping or leaving their posts within the monitoring range \\ \hline Cross Border Invasion & Automatically identify whether personnel have entered hazardous areas or entered designated areas in violation of regulations within the monitoring range \\ \hline Facial Attendance & Analyze the real-time video stream obtained by the surveillance camera, automatically identify and compare whether it is an internal employee, and record it \\ \hline Personnel Flow Count & Automatically identify the number of personnel within the monitoring range \\ \hline Flames Identification & Identify the presence of flames in the video \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithms’ requirements. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Item** & **Model** & **Specification** \\ \hline CPU & 2* Intel(R) Xeon(R) Silver 4310 CPU & 2.10 GHz, 12 cores, 24t threads \\ \hline GPU & 4* NVIDIA GeForce RTX 3080 & 10 GB Memory, 1440 MHz \\ \hline Memory & 6* SKhynix HMA82 GR7 CJR4N-WM & 16 GB, 2666 MT/s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Server hardware configuration. main server had two network ports, with two LAN ports connected to the internal network of the office area. Users were able to access the platform through this internal network.