IoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.
Summary Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.
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