Maritime Metaverse: A Historical Graph-based NGSI-LD Framework for Digital Twin Integration

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Abstract
Leveraging the innovative Historical Graph-based NGSI-LD Framework, this research proposes a novel approach to enriching the metaverse by seamlessly integrating real-world data and digital twin platforms. The framework is meticulously designed to address the existing limitations of NGSI-LD API in handling complex queries and relationships, introducing a sophisticated integration with labeled-property graph technology for enhanced data retrieval and analysis. The core advancement lies in the framework's capability to reconstruct and analyze Auto Identification System (AIS) data, enabling the dynamic simulation of maritime activities within the metaverse. This not only enhances the realism and accuracy of digital twins but also facilitates real-time predictive modeling based on historical data analysis. Moreover, the framework introduces a robust method for complex query resolution through graph traversal, significantly improving user interaction within the metaverse. This is complemented by a comprehensive approach to historical data management with versioning, ensuring the metaverse is enriched with both current and past data for a more thorough understanding and interaction with digital twins. The integration of IoT and digital twin platforms via this framework stands to revolutionize metaverse applications across various sectors, including maritime, logistics, environmental monitoring, and beyond. It not only bridges the gap between the physical and digital worlds but also ensures a secure, intelligent, and dynamically updatable metaverse environment.