## 5 Discussion  
Over the past decades, companies and industry organisations have increasingly adopted RAs to facilitate digitalisation transitions and interoperability between their systems. An RA can be essentially defined as an architectural definition of a collection of systems in a given domain, providing the knowledge about how to develop and evolve software in this domain. However, the heterogeneity of industrial needs, the variety of use cases and scenarios, and the different technical requirements require great flexibility and high connectivity features. Despite the good performance of current FIWARE components to address interoperability with IoT devices, the proposed FIL component demonstrates in the performance experiments some advantages in integration, data management efficiency, and deployment cost. First, FIL distinguishes itself with its user-friendly design for integration with various IoT platforms and industrial systems, which is particularly advantageous for SMEs. In contrast to the F4I IDAS OPC UA Agent that requires more technical knowledge for effective integration, posing potential challenges in complex industrial environments, FIL only requires a first configuration based on web standard technologies that dynamically update over time. In addition, experiments demonstrated FIL's capability in managing high-speed data processing which ensures real-time data handling with minimal latency, a crucial requirement in dynamic industrial settings. On the other hand, while F4I IDAS OPC UA Agent is efficient in data management, it might struggle with very high-volume data streams in real-time scenarios. Finally, FIL's development strategy emphasizes cost-effectiveness, with both FIL and the F4I IDAS OPC UA Agent being open-source solutions that inherently reduce software acquisition costs. The distinguishing factor lies in FIL's streamlined configuration process, which significantly cuts down on the time developers spend setting up the system. This efficiency translates to lower labour costs, positioning FIL as a particularly economical choice for SMEs aiming to deploy semantic interoperability solutions efficiently. Through its seamless integration capabilities, real-time data management efficiency, and commitment to affordability, FIL sets itself apart. A side-by-side analysis with the F4I IDAS OPC UA Agent, as detailed in the accompanying table, showcases FIL's superior performance in integration ease, data processing, and cost savings. These qualities demonstrate FIL's precise alignment with SME needs within Industry 4.0, marking it as a versatile and economically viable choice for overcoming semantic interoperability hurdles. This is a novel approach that addresses these three aspects, providing an interoperability solution for cyber-physical systems. In this way, the FIL component makes FIWARE compatible with OpenAPI and OPC UA facilitating the integration of any type of virtual or real component (i.e., sensors and applications in the cloud) embedded in complex frameworks (i.e., Industry 4.0) into RA with reduced effort, cost, and required skills. In this way, it is possible to take advantage of the greater power of NGSI-LD by allowing us to describe semantic contexts. Although the layered architecture may initially suggest higher \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & **Workload** & **Delivered Messages (Average)** & **Delay (Average Time (s))** \\ \hline FIL & Low & 100\% & 0.10 \\ F4I OPC UA Agent & Low & 100\% & 0.13 \\ FIL & Medium & 99.02 \(\pm\) 0.27\% & 0.32 \\ F4I OPC UA Agent & Medium & 98.99 \(\pm\) 1.12\% & 0.58 \\ FIL & High & 98.32 \(\pm\) 1.06\% & 1.27 \\ F4I OPC UA Agent & High & 93.55 \(\pm\) 2.58\% & 1.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative Performance Outcomes of FIL and F4I OPC UA Agent Under Varied Workloads. implementation costs, the use of Docker containers for deployment adds flexibility and reduces the overall cost.