Modelling the social Internet of Things network using random graphs

Abstract

The Social Internet of Things (SIOT) is a paradigm in which things communicate with one another based on social relationships. The goals are for things to search for services and retrieve and provide users with them independently from their owners. The existing approaches lack models for analyzing the efficiency of SIOTs. The objective of the present research is to model SIOTs with regard to various relationships and their different topological properties. In this approach, the graphs related to the SIOT are modeled based on the similarity of their topological properties and random graphs in such a way that these properties are preserved by increasing the size of the network, the intended topological properties are preserved. For this purpose, first, the topological properties of real SIOT graphs have been extracted. Then, using numerical and intuitive comparisons, the degree of resemblance between the SIOT topological properties and random graphs has been examined. In order to prove this resemblance and network scalability, the connection between the average route length and the descending gradient algorithm has been implemented. The obtained results have shown the resemblance of ownership object relationship (OOR) real graph to the ErdoS Renyi (ER) random graph at p=0.9, the POR graph to the ER random graph at p=0.009, the CLOR graph to the ER random graph at p=0.00009 and the SOR graph to the Barbasi Albert (BA) random graph at m=50. In order to evaluate the proposed framework, the real SIOT dataset has been used, and the scalability and maintenance of the topological properties have been proven.