## 2 Related Work
In a related work, researchers examined how the SIoT could potentially revolutionize technology by incorporating a social perspective into existing IoT applications [40]. The paper proposed an architecture for SIoT and outlined a semantic structure to improve comprehension of the concepts and services. The author's objective is to develop a semantic-oriented platform architecture for SIoT and explore its integration into various domains, including industrial manufacturing and agriculture. The goal is to create a more comprehensive understanding of SIoT and establish its potential applications in different fields. A hybrid recommender system for IoT applications that combines ontology and collaborative filtering techniques to recommend services to users was presented in a relevant work [41]. The proposed approach extends social relationships between users and objects to find alternative sources of missing data. An ontology is used to describe relationships between objects and users, while collaborative filtering is used to predict user preferences. The experimental results showed that the proposed algorithm outperformed existing algorithms in terms of accuracy. Future work includes incorporating spatiotemporal criteria to improve accuracy, and applying privacy preservation techniques to consider the trust and privacy of users. Finally, a research study proposed a grey wolf algorithm-based user object affiliation mechanism for smarter SIoT [42]. This approach combines object predilection and object sociality to rank objects in SIoT. The proposed approach is a combination of preference, semantic, and context-based approaches, and it has five different models. The paper also discusses a machine learning strategy using the maximum ranked neighborhood approach, which focuses on the delivery of desired services. The proposed model achieved high performance in terms of proper metrics after conducting extensive trials on two real-time SIoT datasets. However, the model lacks a privacy management strategy and only considers an undirected graph, which can be extended in future work. The proposed model can also be used in other scopes of IoT with higher accuracy and certainty. Table 1 presents an overview of the explored studies focusing on the utilization of semantic technologies for SIoT. It highlights the strengths of these studies, in terms of the challenges they address, as well as their existing limitations. In conclusion, the reviewed literature on SIoT approaches that utilize semantics highlights the significant progress made in addressing several of the challenges within this field. The use of semantics has enabled SIoT to achieve a higher level of intelligence and sociality, allowing for more efficient and effective interactions between objects and users. However, despite the promising results, there are still some areas that require further research and development. For instance, dynamic friendship selection is a critical area that needs attention to achieve more accurate and reliable recommendations in SIoT networks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Ref. ** & **Year** & \begin{tabular}{c} **Semantic** \\ **Technologies** \\ \end{tabular}