## 4 Use Case Evaluation
### Green Route: A Use Case Scenario to Find a Sustainable Path  
The Green Route is a use case that aims to find the most environmentally friendly route for a citizen named [PERSON] to walk from her house to the port in a smart city. The scenario involves multiple components of the S2 NeTM architecture, including _Context Management (CM)_, _Semantic Engines (SEs)_, _Trustworthiness Management (TM)_, _Friendship Selection (FS)_, and _Relationship Management (RM)_. The entities involved in the scenario include IoT environmental devices with sensors for temperature, humidity, and air quality, a sensor web service for accessing open environmental data, and a mobile crowdsourcing application for citizen participation. The _Data Collection Platform_ is responsible for connecting the IoT devices and collecting data from them. In the Green Route scenario, environmental sensors are placed throughout the city, and their data are collected by a sensor web service. The data are then made available for access by other entities through APIs. For example, the air quality sensors can detect pollution levels in different areas of the city and send this information to the _CM_ component of the S2 NeTM, which then determines the _co-location_ relationships between the devices. The _co-location_ relationship indicates that the sensors are physically close to each other and can be used to find a green route that avoids areas with high pollution levels. The _SEs_ of the S2 NeTM provide a common ontology that facilitates interoperability and alignment between the heterogeneous entities. The ontology alignment and reasoning mechanisms in the _SEs_ component can dynamically find new relationships among entities based on their shared knowledge. In the Green Route scenario, the _Semantic Annotation_ module allows entities to annotate their data with semantic tags that enable the alignment and reasoning mechanisms to identify and utilize new relationships. For instance, the environmental sensors and the sensor web service share the same environmental data ontology, which enables them to establish a _co-semantics_ relationship. This relationship means that they can collaborate more effectively in finding the greenest route for [PERSON]. The _TM_ component of the S2 NeTM approach evaluates the trustworthiness of the entities based on their behavior, history, and feedback from other entities. In the Green Route scenario, the trustworthiness of the sensor web service is evaluated based on its data quality and availability. The trustworthiness of the mobile crowdsourcing application is evaluated based on the accuracy and usefulness of the route suggestions provided by its users. The _FS_ and _RM_ components of the S2 NeTM facilitate the social relationships among entities. The _co-location_ relationship is established among entities that are physically close to each other, such as [PERSON] and the environmental sensors near her house. The _co-work_ relationship is established among entities that collaborate to achieve a common goal, such as [PERSON] and the mobile crowdsourcing application users who share their route suggestions. The _co-semantics_ relationship is established among entities that share similar semantics, such as the environmental sensors and the sensor web service that share the same environmental data ontology. These relationships enable the entities to collaborate more effectively and contribute to finding the greenest route for [PERSON]. Using these social relationships and semantic annotations, the entities collaborate to find the greenest route for [PERSON]. The environmental sensors provide real-time data on the temperature, humidity, and air quality in the area, and the sensor web service provides historical and open data on environmental conditions in the city. [PERSON] uses the mobile crowdsourcing application to share her preferences, such as avoiding busy roads and congested areas, and the application users provide their route suggestions.