The social or human actions in the IoT platform derive the new paradigm in the IoT environment called the Social Internet of Things (SIoT). The Social Internet of Things is that part of an IoT capable of establishing social relationships with other objects concerning humans. SIoT attempts to moderate IoT challenges in scalability, trust, and resource discovery by taking a cue from social computing. In the IoT family, there is a subset of SIoT, a relatively recent concept. Moreover, a method of integrating IoT with social networking. SIoT is a simulation of human-to-human and object-to-object social networks where Humans are called intellectual and relational objects. They build their social network to accomplish shared objectives such as enhancing accessibility, success, and productivity and providing their needed services. This paper has extensively surveyed the SIoT (social Internet of things) for beginners involved in SIoT Studies. This paper gives you a clear view and ideas about SIoT's architecture, relationships, trust management, and applications and challenges implemented related to SIoT.

Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.

Today, energy consumption is important in calculating the heating and cooling loads of residential, industrial, and other units. In order to calculate, design, and select the heating-cooling system, a suitable method of consumption and cost analysis is needed to prepare the required data for air conditioning motors and design an intelligent system. In this research, a method for balancing the temperature of an intelligent building in the context of the Internet of Things is presented based on a combination of network cutting and clustering techniques. In order to achieve the optimization of the algorithm in this method, it is necessary to convert heterogeneous data into homogeneous data, which was done by introducing a complex network and appropriate clustering techniques. In this method, information was collected by the IoT, and a graph matrix of these data was generated, then recorded by an artificial intelligence method and a combination of three methods of hierarchical clustering, Gaussian mixture, and K-means for comparison with the preliminary results. Finally, due to the reliability of the K-means method and the use of majority voting for weights, the K-means method reached 0.4 and was selected as the clustering method. The main part of the proposed method is based on different classifications in Appropriate criteria that were evaluated. Acceptable results were recorded so that with the minimum value of 88% and the highest value of about 100, the results of the proposed method can be confirmed. All hypotheses of the method can be declared possible and acceptable.