Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry

Crespo-Aguado, Maria; Lozano, Raúl; Hernandez-Gobertti, Fernando; Molner, Nuria; Gómez‐Barquero, David · 2024 · OpenAlex-citations

DOI: 10.3390/fi16110431

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the challenges of implementing real-time Digital Twin applications in complex logistics and industrial environments, particularly regarding the high latency and bandwidth saturation caused by massive IoT data streams. Motivated by the need for 6G-ready infrastructure, the authors propose a flexible, hyper-distributed IoT–Edge–Cloud computing platform. This system is designed as a living lab and testbed to support future 6G applications by integrating a private 5G network with extended Edge and Cloud computing functionalities. The primary goal is to offload processing from resource-constrained IoT devices to optimal locations within the continuum, thereby reducing latency and enabling real-time synchronization for critical tasks such as immersive remote driving and autonomous monitoring. The platform’s architecture leverages a private 5G network to connect sensors, machines, and robots on a large scale. It employs an end-to-end intelligent orchestrator driven by artificial intelligence and machine learning to dynamically allocate computing resources across the IoT, Edge, and Cloud layers. This hyper-distributed approach ensures that computation occurs at the most efficient point based on specific application requirements, filtering data locally at the Edge to minimize bandwidth usage before forwarding insights to the Cloud. The experimental setup involved two sites and included the deployment of a Digital Twin application prototype focused on immersive remote driving. This prototype served as the validation mechanism for the platform’s capability to handle high-throughput communications and real-time control. Performance evaluations demonstrated the platform’s ability to support the stringent requirements of Digital Twins. The system achieved user-experienced data rates close to theoretical maximums, reaching up to 552 Mb/s for downlink and 87.3 Mb/s for uplink in the n78 frequency band. Furthermore, the platform’s support for Digital Twins was validated through Quality of Experience (QoE) assessments on the immersive remote driving prototype. These assessments indicated high levels of user satisfaction across key dimensions, including presence, engagement, control, sensory integration, and cognitive load. The results confirm that the hyper-distributed architecture effectively mitigates latency issues and manages high-volume data streams without inundating network infrastructures. The significance of this work lies in its provision of a scalable, interoperable testbed for next-generation networking and industrial IoT. By demonstrating the feasibility of AI-driven orchestration in a real-world logistics context, the study highlights the potential of hyper-distributed architectures to bridge the gap between current 5G capabilities and future 6G requirements. The platform enables seamless integration of diverse stakeholder solutions, allowing vertical application developers to leverage distributed computing resources transparently. This advancement supports the broader 6G vision of interconnecting human, digital, and physical worlds, offering a robust foundation for real-time cyber-physical twinning, predictive maintenance, and autonomous operations in industrial settings.

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• teleoperation remote driving