## 4 FIL Component Quantitative Analysis: Experimental Testbed Design
### Performance Comparison: FIL vs. F4I OPC UA Agent  
In this section, we analyse the FIL component and the F4I OPC UA Agent that allow the OPC UA server to connect to the FIWARE Context Broker and send data updates to the deployed backend component.  
To ensure the correctness and completeness of the translation performed by the Context Consumer and the FIWARE Entity Converter, the messages produced by the OPC UA server were compared with the messages subscribed into the Context Broker and the ones stored into MongoDB. The completeness was manually validated by comparing the number in entities of all messages and verifying that it was the same in all inputs and outputs. Correctness was verified by manually comparing the content of all the outputs and verifying that they are the same entities in all of them. No major differences were found in the translations, and a value of correct translation close to 100% of the messages was found.  
In our analysis with Prometheus and Grafana, FIL showed exceptional performance compared to the F4I OPC UA Agent, especially in resource utilization and speed. Specifically, FIL's CPU usage peaked at only 1.75%, averaging around 0.5%, whereas the F4I OPC UA Agent averaged 2.5% (Figures 4 and 5).  
The memory usage is higher in F4I OPC UA Agent caused by Orion which updates the attribute stored into MongoDB any time an attribute is updated, increasing the processing demand of the database. The memory consumption for FIL averaged at 75.3 MiB, significantly less than the F4I OPC UA Agent's 218 MiB (Figures 6 and 7). The CPU usage in the F4I OPC UA Agent grows fast. While the CPU usage in the FIL component seems to grow linearly with the load in the MongoDB, in the F4I Agent this growth is faster than linear and it is maintained over time.  
Figure 4: FIL CPU Usage (%).  
Figure 5: F4I OPC UA Agent CPU Usage (%).  
Data transmission rates for FIL peaked at 250 B/s, generally staying below 50 B/s, in stark contrast to the F4I OPC UA Agent's nearly constant rate of 600 B/s (Figures 8 and 9). These findings, supported by subsequent graphs, highlight FIL's efficiency and its potential to provide cost-effective solutions for Industry 4.0 interoperability.  
Finally, we have inspected the number of lost messages. All messages were delivered by the IoT agent, and minimum of them were lost when higher workload rates were sent in the case of the FIL component. The delay was higher in the F4I OPC UA Agent when translating into a FIWARE Entity, as shown in the partial description of the results in Table 5.  
The analysis reveals that while both FIL and the F4I OPC UA Agent exhibit high message delivery rates, FIL demonstrates a slight advantage, particularly under high workload  
Figure 8: FIL Transmitted Data.  
Figure 6: FIL Memory Usage (%).  
Figure 7: F4I OPC UA Agent Memory Usage (%).  
Figure 9: F4I OPC UA Agent Transmitted Data.  
conditions. This performance edge becomes more pronounced with increased delay times for the F4I OPC UA Agent during FIWARE Entity translation. This comparison underscores FIL's robustness and efficiency, showcasing its potential to enhance the FIWARE platform's interoperability and real-time data handling capabilities.