Encoding reality with semantic interpretation in metaverse interactions

Abstract

In the rapidly evolving landscape of the metaverse, efficiently managing and processing vast amounts of data is a critical challenge. This paper introduces a novel data management framework designed specifically for the metaverse, addressing the complexities of handling extensive data in its increasingly intricate and expansive digital environments. Leveraging advancements in wireless communication technologies, our framework optimizes resource allocation while managing massive data loads. We propose an innovative approach that integrates change grading and semantic encoding techniques, utilizing edge computing to process data locally and reduce server load. By processing data locally and minimizing server load, our approach stores static scenes on local devices and detects significant changes by comparing them with newly captured data. This selective transmission of only new or altered information based on change grades dramatically reduces bandwidth requirements and boosts network efficiency. This paper details the technical aspects of our methodology, showcases experimental results that demonstrate its effectiveness, and discusses its implications for the future scalability and sustainability of metaverse architectures. Our framework aims to refine the real-time digital representation of the metaverse, ensuring its operational efficiency and continuous accessibility as it evolves.