## 4 Results and Analysis
### Cognitive Workload Monitoring System  
The real-time monitoring of the CWMS is evaluated using the Things Board platform, allowing us to track various parameters, including end-to-end latency, model classification processing time, and cognitive workload levels. The Raspberry Pi Zero W gateways efficiently manage data transmission between the EEG device and the AI server without significant delays, ensuring smooth operation. These modules are essential in this setup due to their cost-effectiveness, making them ideal for large-scale experiments involving multiple users and allowing for scalable deployments. Their small size ensures easy integration into experimental setups, particularly with wearable EEG devices, keeping the setup portable and unobtrusive. Additionally, the built-in Wi-Fi functionality enables seamless wireless communication between the virtual users and the Ubuntu server. The Things Board platform provides a user-friendly interface for visualizing and analyzing the results, with real-time tracking capabilities crucial for monitoring the system's performance. Our experiment involved five laptops, with four representing virtual users and one for the live monitoring and classification results. Additionally, four Raspberry Pi Zero W gateways are used to facilitate data transmission between the EEG device and the AI server. To accommodate the connectivity and power needs of Raspberry Pi modules, a single Wi-Fi router and power cables are used. Figure 12 illustrates the experimental setup, depicting the interconnected components. A set of monitoring data is presented, where each figure comprises three distinct graphs and a table representing the data from four gateways. In our scenario, each gateway serves only one virtual user; thus, the four gateways correspond to four virtual users. The first graph in each figure displays end-to-end latency, providing insights into the transmission delays. The second graph showcases AI model classification processing time, indicating the duration taken for cognitive workload assessment. Lastly, the third graph delineates workload levels, where 0 signifies a low workload level, 1 denotes an average workload level, and 2 indicates a high workload level. The corresponding table in each graph offers a direct representation of workload classification. Figure 13 displays the information from gateway-001 on Things Board. We observe the latest latency recorded as 237 ms, indicating the time taken for data transmission. The classification processing time is noted as 4621.21 ms, reflecting the duration for cognitive workload assessment. Additionally, the workload levels are showcased separately, providing a comprehensive overview of the system's performance. This detailed visualization aids in identifying potential issues and optimizing the network's efficiency. Figure 11: Plotted comparison of state-of-the-art results from Table 1.  
Figure 12: Cognitive workload classification monitoring experiment. Figure 13: Gateway-001 cognitive workload monitoring data graphs. Figure 14 presents the monitoring data from gateway-002 on Things Board, encompassing three critical graphs and a comprehensive table. The first graph indicates an end-to-end latency of 459 ms, a metric that measures the time taken for the data to travel from the virtual user to the server and back, reflecting the network's efficiency. Higher latency can adversely affect user experience, particularly in real-time applications. The second graph showcases the classification processing time, recorded at 6717.97 ms, which is significantly higher than that of gateway-001, suggesting potential variability in computational load or hardware performance across different gateways. The third graph delineates the workload levels for gateway-002, distinguishing between low (0), average (1), and high (2) workload levels. Figure 15 displays the data from gateway-003 on Things Board, following the same structure as the previous figures but with distinct performance characteristics.