A novel community-driven recommendation-based approach to predict and select friendships on the social IoT utilizing deep reinforcement learning

Abstract

The study of how to integrate Complex Networks (CN) within the Internet of Things (IoT) ecosystem has advanced significantly because of the field's recent expansion. CNs can tackle the biggest IoT issues by providing a common conceptual framework that encompasses the IoT scope. To this end, the Social Internet of Things (SIoT) perspective is introduced. In this study, a dynamic community-driven recommendation-oriented connection prediction and choice strategy utilizing Deep Reinforcement Learning (DRL) is proposed to deal with the key challenges located in the SIoT friendship selection component. To increase the efficiency of exploration, we incorporate an approach motivated by curiosity to create an intrinsic bonus signal that encourages the DRL agent to efficiently interact with its surroundings. Also, a novel method for Dynamic Community Detection (DCD) on SIoT to carry out community-oriented object recommendations is introduced. Lastly, we complete the experimental verifications utilizing datasets from the real world, and the experimental findings demonstrate that, in comparison to the related baselines, the approach presented here can enhance the accuracy of the social IoT friendship selection task and the effectiveness of training.