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Dynamic framework to mining Internet of Things for multimedia services

The rapid and unprecedented technological advancements are currently dominated by two technologies. At one hand, we witness the rise of the Internet of Things (IoT) as the next evolution of the Internet. At the other hand, we witness a vast spread of social networks that connects people together socially and opens the door for people to share and express ideas, thoughts, and information. IoT is overpopulated by a vast number of objects, millions of multimedia services, and interactions. Therefore, the search of the right object that can provide the specific multimedia service is considered as an important issue. The merge of these two technologies resulted in new paradigm called Social IoT (SIoT). The main idea in SIoT is that every object can mine IoT in search for certain multimedia service. We investigate the issue of friends' management in SIoT and propose a framework to manage friends' requests. The proposed framework employs several mechanisms to better manage friends' relationships. The proposed framework consists of friend selection, friendship removal, and an update module. It proposes a weight‐based algorithm and Naïve Bayes Classifier‐based algorithm for the selection component. Moreover, a random service allocation model is proposed to construct service‐specific network model. This model is then used in the simulation setup to examine the performance of different friends' management algorithms. The performance of the proposed framework is evaluated using simulation under different scenarios. The obtained simulation results show improvement over other strategies in terms of average degree of connections, average path length, local cluster coefficients, and throughput.