## 4 Use Case Evaluation
### Performance Evaluation  
To evaluate the effectiveness and efficiency of the S2 NeTM, we conducted a performance evaluation, considering various performance metrics and using the Green Route use case scenario. These metrics include request processing delay, setup computation time, and memory usage. The experiments were performed on a dedicated Ubuntu server with an AMD EPYC 7543 32-Core Processor and 16 GB of RAM. Additionally, we integrated the Neo4j graph database into our setup, to facilitate efficient storage and retrieval of data within the S2 NetM. Leveraging Neo4j's graph database capabilities, we effectively modeled and queried the interconnected entities, services, and relationships in the SIoT environment. The reported times represent the averages obtained from twenty repeated tests. The request processing delay metric measures the time required by the system to process and generate a green route recommendation for a specific user, such as [PERSON]. Our experimental results revealed that the S2 NeTM achieved a processing delay of 500 milliseconds (0.5 s), demonstrating its capability to deliver timely recommendations. The setup computation time metric represents the time required to configure the graph infrastructure, considering multiple factors. Our analysis revealed an average setup time of approximately 3.1 s, indicating the system's ability to handle intricate calculations in advance, enabling timely recommendations upon request. Specifically, the setup time can be divided into two primary components: the processing time for establishing social relationships between entities, and the time taken to populate the graph nodes with data, such as air quality data. Through our experiments, we observed that establishing social relationships, including _co-location, co-work_, and _co-semantic_ relationships, accounted for an average of 2 s. This step involves analyzing the underlying data and identifying pertinent connections based on the S2 NeTM ontology. Subsequently, the task of populating the graph nodes with data, taking into account environmental factors, yielded an average duration of 1.1 s. This step entails evaluating various criteria, including distance, air quality, and eco-friendly transportation options, to facilitate the identification of the most suitable green route for the user. The overall computation time of 3.1 s demonstrates the system's required overhead for handling periodic computations as cached information for generating green route recommendations. This overhead is incurred during the setup stage and subsequently amortized for real-time requests, thereby improving the overall user experience. It is worth noting that the specific computation times mentioned above are based on our experimental setup and may vary depending on the available computational resources. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**IoTEntity 1** & **IoTEntity 2** & **Structural** & **Lexical** & **Semantic** & **Combined** & **Approved** \\
**(S2 NetM Ontology)** & **(SSOR Ontology)** & **Similarity** & **Similarity** & **Similarity** & **Similarity** & **Co-Semantics** \\ \hline Entity & Object & 0.5 & 0.5 & 0.92 & 0.65 & NO \\ User & User & 1.0 & 1.0 & 1.0 & 1.0 & YES \\ Service & Service & 1.0 & 1.0 & 1.0 & 1.0 & YES \\ Location & GeoLocation & 0.67 & 0.8 & 0.92 & 0.8 & YES \\ hasRelationshipWith & hasFriend & 0.5 & 0.5 & 0.75 & 0.58 & NO \\ hasLocation & isLocated & 0.7 & 0.71 & 0.88 & 0.83 & YES \\ owns & Owns & 0.9 & 0.9 & 0.83 & 0.87 & YES \\ timestamp & timestamp & 1.0 & 1.0 & 1.0 & 1.0 & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of similarity calculation between two IoT entities in Green Route scenario. Memory usage is an important aspect of system efficiency. We monitored the memory consumption during the green route computation and recommendation process. Our findings revealed that the system operated within a reasonable memory footprint, with an average memory usage of approximately 100 megabytes (MB), demonstrating efficient memory management.