## 1 Introduction  
The Internet of Things (IoT) has transformed the way we interact with the world, bringing greater connectivity and intelligence to devices and systems, especially in the context of smart cities [1]. However, traditional IoT networks have limitations in terms of intelligence, context-awareness, and interoperability, which impede their ability to create value for businesses and individuals [2, 3]. This is where the Social Internet of Things (SIoT) paradigm comes in; it combines the best of social networks with IoT networks to create a more collaborative and efficient system [4, 5]. One of the main benefits of SIoT is its ability to facilitate collaboration and cooperation between devices and applications [6, 7]. This results in more streamlined and effective processes, as devices and applications can work together to achieve common goals [5]. Additionally, SIoT offers enhanced scalability, allowing systems to handle large amounts of data and devices, which can be used to create complex, distributed networks [8]. In addition, the adoption and utilization of the SIoT paradigm presents several other benefits. One of these advantages is better navigability in a dynamic network of billions of devices, enabling faster access times among devices [9]. SIoT takes advantage of an enhanced connection to find an optimal navigable route between components, and each node has knowledge of the surrounding nodes and the capability to choose friends to navigate the global system [10]. Another advantage is flexibility, as SIoT is customized to social network behavior to enable friends to search and connect with each other [11]. The node's connection searches faster by accepting the friends' structure, and each node has the necessary information about its friends, neighboring nodes, and the respective concerned friends. This feature eliminates the need for centralized traditional systems, and there is no limitation on the number of nodes that can be connected in an SIoT. Additionally, SIoT provides decision-making models based on the shared level of trustworthiness between friends [12]. This situation improves SIoT reliability because the node interacts exclusively with trustworthy friends, leading to dynamic discovery of services, higher data access, and real-time decision making in more effective and efficient ways [13]. On the other hand, the SIoT presents several challenges that need to be addressed to fully exploit the potential of this paradigm [14]. One of the main challenges is the dynamic nature of social interactions among IoT devices. Relationship selection mechanisms need to be adaptive and capable of adjusting to changes in the social context [10; 15]. Static approaches may lead to suboptimal or even ineffective social interactions. Thus, there is a need for dynamic friendship selection mechanisms, such as dynamic ontology-based reasoning, to enable efficient and effective social interactions among IoT devices [9; 16]. Another challenge is related to privacy and security in SIoT environments [17; 18]. IoT devices may collect sensitive data about users and their social interactions, which could be used for malicious purposes. Therefore, there is a need for robust privacy and security mechanisms that can protect users' data and ensure the trustworthiness of social interactions among IoT devices [19]. One possible solution is to incorporate privacy and security policies into the ontology used to model social interactions [20]. Such policies could govern the access and use of users' data and enable fine-grained control over social interactions. Moreover, semantic interoperability and standardization are key challenges in SIoT [21; 22; 23; 24] environments. The heterogeneity of IoT devices and the lack of standard protocols and interfaces may hinder social interactions among devices from different vendors or platforms. Thus, there is a need for interoperable and standardized solutions that enable seamless social interactions among IoT devices.