Affection-Centric Metaverse in 6G: Initiative Learning Based In-Network Human-Like Sentimental Analysis

The forthcoming Metaverse will have to be based on future 6G networks, as 5G/5G-Advanced networks cannot cope with the assumed requirements, especially regarding latency, robustness of connections, and wireless access throughput. Another important aspect missing in current networks is the understanding of the role of human affections in interactions between humans and interfaces accessing the Metaverse. Such affections are hidden in large amounts of human-centric content, semantics, and sentiment-related data. Moreover, existing methods and schemes lack the capability of intelligently analyzing human sentiments. There is therefore the need to research sentimental analysis to thoroughly comprehend human demands in 6G-driven Metaverse scenarios. With that enhancement, 6G will be able to fulfill the expectation of providing a revolution in interactions between humans and virtual realities. To properly address that needed enhancement, this article proposes an initiative learning-based, affection-centric networking (ACN) for the 6G-enabled Metaverse, which utilizes artificial intelligence to enable human-like affection analysis at the network layer. First, an affection-centric, in-networking sentimental analysis framework is proposed, where the ACN architecture and the heterogeneous affection transformation mechanism are designed. Second, an initiative learning-based, in-network human-like sentimental analysis mechanism is developed. This mechanism maps data of devices and humans into hierarchical forms, analyzes emotions of humans in the network layer, and makes affection-centric decisions and predictions. Third, Metaverse standardization activities are studied. Finally, The case study and simulation results demonstrate the efficiency of the proposed ACN framework. To the best of our knowledge, this work is the first to propose a novel ACN architecture.