THE 6G CONTINUUM: A PLATFORM ARCHITECTURE FOR REAL-TIME INDUSTRIAL DIGITAL TWINS

Dr. Elena J. Foster; Dr. Marcus T. Delgado

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THE 6G CONTINUUM: A PLATFORM ARCHITECTURE FOR REAL-TIME INDUSTRIAL DIGITAL TWINS

Dr. Elena J. Foster ¹ , Dr. Marcus T. Delgado ²

¹ Department of Communication, University of Montana, Missoula, MT, USA

² Department of Political Science, University of South Alabama, Mobile, AL, USA

Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles --- Published Date: 2024-12-30

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Abstract

The convergence of next-generation wireless technologies, particularly 6G, with the Internet of Things (IoT), edge computing, and cloud infrastructure is set to revolutionize industries by enabling ultra-responsive and intelligent applications. A key application in this domain is the Digital Twin (DT), which requires a seamless and powerful computational continuum to operate in real-time. This paper presents a novel, flexible, and hyper-distributed platform that spans the IoT-Edge-Cloud continuum, designed specifically to support real-time DT applications for the logistics and industrial sectors. We detail the architecture of this platform, which leverages a multi-tiered approach to computation and data processing, and its implementation on a 6G-intended testbed. The platform's design addresses the critical challenges of latency, data throughput, and scalability inherent in large-scale industrial environments. We validate our approach through two specific use cases: a smart logistics scenario and an industrial automation process. The results from our testbed demonstrate the platform's capability to meet the stringent Key Performance Indicators (KPIs) required for real-time DT operations, such as ultra-low latency and high reliability, paving the way for the next generation of cyber-physical systems.

Keywords

6G, Digital Twin, IoT, Edge Computing

References

[1] Uusitalo, M.A.; et al. 6G Vision, Value, Use Cases and Technologies From European 6G Flagship Project Hexa-X. IEEE Access 2021, 9, 160004–160020.

[2] Viswanathan, H.; Mogensen, P.E. Communications in the 6G Era. IEEE Access 2020, 8, 57063–57074.

[3] Wang, C.X.; et al. On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds. IEEE Commun. Surv. Tutorials 2023, 25, 905–974.

[4] Allioui, H.; Mourdi, Y. Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors 2023, 23, 8015.

[5] Kwon, J.H.; Kim, H.J.; Lee, S. Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking. Electronics 2024, 13, 2837.

[6] Nie, Z.; Chen, K.C.; Alanezi, Y. Socially Networked Multi-Robot System of Time-Sensitive Multi-Link Access in a Smart Factory. In Proceedings of the ICC 2023-IEEE International Conference on Communications, Rome, Italy, 28 May–1 June 2023; pp. 4918–4923.

[7] Hazarika, A.; Rahmati, M. Towards an evolved immersive experience: Exploring 5G-and beyond-enabled ultra-low-latency communications for augmented and virtual reality. Sensors 2023, 23, 3682.

[8] Gkonis, P.; Giannopoulos, A.; Trakadas, P.; Masip-Bruin, X.; D’Andria, F. A survey on IoT-edge-cloud continuum systems: Status, challenges, use cases, and open issues. Future Internet 2023, 15, 383.

[9] Jamil, M.N.; Schelén, O.; Monrat, A.A.; Andersson, K. Enabling Industrial Internet of Things by Leveraging Distributed Edge-to-Cloud Computing: Challenges and Opportunities. IEEE Access 2024, 12, 127294–127308.

[10] Hlophe, M.C.; Maharaj, B.T. From cyber–physical convergence to digital twins: A review on edge computing use case designs. Appl. Sci. 2023, 13, 13262.

[11] Lubrano, F.; Caragnano, G.; Scionti, A.; Terzo, O. Challenges, Novel Approaches and Next Generation Computing Architecture for Hyper-Distributed Platforms Towards Real Computing Continuum. In Proceedings of the Advanced Information Networking and Applications, Kitakyushu, Japan, 17–19 April 2024; pp. 449–459.

[12] Alkhateeb, A.; Jiang, S.; Charan, G. Real-time digital twins: Vision and research directions for 6G and beyond. IEEE Commun. Mag. 2023, 61, 128–134.

[13] Nguyen, T.N.; Zeadally, S.; Vuduthala, A.B. Cyber-physical cloud manufacturing systems with digital twins. IEEE Internet Comput. 2021, 26, 15–21.

[14] Barnabas, J.; Raj, P. The human body: A digital twin of the cyber physical systems. In Advances in Computers; Elsevier: Amsterdam, The Netherlands, 2020; Volume 117, pp. 219–246.

[15] Xu, H.; et al. Smart mobility in the cloud: Enabling real-time situational awareness and cyber-physical control through a digital twin for traffic. IEEE Trans. Intell. Transp. Syst. 2023, 24, 3145–3156.

[16] Kaigom, E.G.; Roßmann, J. Value-driven robotic digital twins in cyber–Physical applications. IEEE Trans. Ind. Inform. 2020, 17, 3609–3619.

[17] Shi, S. Industrial cloud, automation: The industrial. Internet of Things (IIoT) is being embraced by manufacturers as a natural extension of automation and controls development. Control. Eng. 2023, 70, 31–32.

[18] Zorchenko, N.; Tyupina, T.; Parshutin, M. Technologies Used by General Electric to Create Digital Twins for Energy Industry. Power Technol. Eng. 2024, 58, 521–526.

[19] Gupta, R.; Reebadiya, D.; Tanwar, S. 6G-enabled edge intelligence for ultra-reliable low latency applications: Vision and mission. Comput. Stand. Interfaces 2021, 77, 103521.

[20] Santos, J.; Wauters, T.; Volckaert, B.; De Turck, F. Towards low-latency service delivery in a continuum of virtual resources: State-of-the-art and research directions. IEEE Commun. Surv. Tutor. 2021, 23, 2557–2589.

[21] Vaish, R.; Hollinger, M.C. Case Study: IBM–Automating Visual Inspection. In Springer Handbook of Automation; Springer: Berlin/Heidelberg, Germany, 2023; pp. 1439–1450.

[22] Fortino, G.; Guerrieri, A.; Pace, P.; Savaglio, C.; Spezzano, G. Iot platforms and security: An analysis of the leading industrial/commercial solutions. Sensors 2022, 22, 2196.

[23] Kherbache, M.; Maimour, M.; Rondeau, E. Digital twin network for the IIoT using eclipse ditto and hono. IFAC-PapersOnLine 2022, 55, 37–42.

[24] De Benedictis, A.; Rocco di Torrepadula, F.; Somma, A. A Digital Twin Architecture for Intelligent Public Transportation Systems: A FIWARE-Based Solution. In Proceedings of the International Symposium on Web and Wireless Geographical Information Systems, Yverdon-Les-Bains, Switzerland, 17–18 June 2024; pp. 165–182.

[25] Conde, J.; Munoz-Arcentales, A.; Alonso, Á.; Huecas, G.; Salvachúa, J. Collaboration of digital twins through linked open data: Architecture with fiware as enabling technology. IT Prof. 2022, 24, 41–46.

[26] Robles, J.; Martín, C.; Díaz, M. OpenTwins: An open-source framework for the development of next-gen compositional digital twins. Comput. Ind. 2023, 152, 104007.

[27] Zeb, S.; et al. Industrial digital twins at the nexus of NextG wireless networks and computational intelligence: A survey. J. Netw. Comput. Appl. 2022, 200, 103309.

[28] Mihai, S.; et al. Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Commun. Surv. Tutor. 2022, 24, 2255–2291.

[29] Mirani, A.A.; Velasco-Hernandez, G.; Awasthi, A.; Walsh, J. Key challenges and emerging technologies in industrial IoT architectures: A review. Sensors 2022, 22, 5836.

[30] Dong, J.; et al. Mixed cloud control testbed: Validating vehicle-road-cloud integration via mixed digital twin. IEEE Trans. Intell. Veh. 2023, 8, 2723–2736.

[31] Alimi, I.A.; et al. Towards enhanced mobile broadband communications: A tutorial on enabling technologies, design considerations, and prospects of 5G and beyond fixed wireless access networks. Appl. Sci. 2021, 11, 10427.

[32] Milovanovic, D.A.; Bojkovic, Z.S. 5G Ultrareliable and Low-Latency Communication in Vertical Domain Expansion. In Driving 5G Mobile Communications with Artificial Intelligence Towards 6G; CRC Press: Boca Raton, FL, USA, 2023; pp. 137–181.

[33] Kong, L.; et al. Edge-computing-driven Internet of Things: A Survey. ACM Comput. Surv. 2022, 55, 1–41.

[34] Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016, 3, 637–646.

[35] Lin, X.; et al. 6G digital twin networks: From theory to practice. IEEE Commun. Mag. 2023, 61, 72–78.

[36] AlSobeh, A.M.; Hammad, R.; Al-Tamimi, A.K. A modular cloud-based ontology framework for context-aware EHR services. Int. J. Comput. Appl. Technol. 2019, 60, 339–350.

[37] Arbab-Zavar, B.; Palacios-Garcia, E.J.; Vasquez, J.C.; Guerrero, J.M. Message queuing telemetry transport communication infrastructure for grid-connected AC microgrids management. Energies 2021, 14, 5610.

[38] Baig, M.J.A.; Iqbal, M.T.; Jamil, M.; Khan, J. A low-cost, open-source peer-to-peer energy trading system for a remote community using the internet-of-things, blockchain, and hypertext transfer protocol. Energies 2022, 15, 4862.

[39] Yassein, M.B.; et al. Challenges and techniques of constrained application protocol (CoAP) for efficient energy consumption. In Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 7–9 April 2020; pp. 373–377.

[40] Marino, C.A.; Chinelato, F.; Marufuzzaman, M. AWS IoT analytics platform for microgrid operation management. Comput. Ind. Eng. 2022, 170, 108331.

[41] Satapathi, A.; Mishra, A. Build an IoT Solution with Azure IoT Hub, Azure Functions, and Azure Cosmos DB. In Developing Cloud-Native Solutions with Microsoft Azure and NET; Springer: Berlin/Heidelberg, Germany, 2022; pp. 193–218.

[42] Fortino, G.; et al. A review of internet of things platforms through the iot-a reference architecture. In Proceedings of the International Symposium on Intelligent and Distributed Computing, Freiburg, Germany, 4–8 October 2021; pp. 25–34.

[43] Alabbas, A.; et al. Performance analysis of apache openwhisk across the edge-cloud continuum. In Proceedings of the 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), Chicago, IL, USA, 2–8 July 2023; pp. 401–407.

[44] Daki´c, V.; Kovaˇc, M.; Slovinac, J. Evolving High-Performance Computing Data Centers with Kubernetes, Performance Analysis, and Dynamic Workload Placement Based on Machine Learning Scheduling. Electronics 2024, 13, 2651.

[45] Tricomi, G.; et al. Paving the way for an Urban Intelligence OpenStack-based Architecture. In Proceedings of the 2024 IEEE International Conference on Smart Computing (SMARTCOMP), Osaka, Japan, 29 June–2 July 2024; pp. 284–289.

[46] Ullah, A.; et al. Orchestration in the Cloud-to-Things compute continuum: Taxonomy, survey and future directions. J. Cloud Comput. 2023, 12, 135.

[47] Alsobeh, A.; Shatnawi, A. Integrating data-driven security, model checking, and self-adaptation for IoT systems using BIP components: A conceptual proposal model. In Proceedings of the International Conference on Advances in Computing Research, Orlando, FL, USA, 8–10 May 2023; pp. 533–549.

[48] Khan, W.Z.; et al. Edge computing: A survey. Future Gener. Comput. Syst. 2019, 97, 219–235.

[49] Fogli, M.; et al. Performance evaluation of kubernetes distributions (k8s, k3s, kubeedge) in an adaptive and federated cloud infrastructure for disadvantaged tactical networks. In Proceedings of the 2021 International Conference on Military Communication and Information Systems (ICMCIS), The Hague, The Netherlands, 4–5 May 2021; pp. 1–7.

[50] Banaei, A.; Sharifi, M. Etas: Predictive scheduling of functions on worker nodes of apache openwhisk platform. J. Supercomput. 2022, 78, 5358–5393.

[51] Santos, Á.; Correia, N.; Bernardino, J. On the Suitability of Cloud Models for MEC Deployment Purposes. In Proceedings of the 2023 6th Experiment@ International Conference; (exp. at’23); Evora, Portugal, 5–7 June 2023; pp. 255–260.

[52] Seabold, S.; Perktold, J. Statsmodels: Statistical Models in Python; Python Software Foundation: Wilmington, DE, USA, 2010.

[53] 3rd Generation Partnership Project (3GPP). NR; User Equipment (UE) radio access capabilities (Release 18). Technical Report TS 38.306 V18.3.0, 3GPP, 2024.

[54] Keysight Technologies. P8900S LoadCore—Core Network Solutions. 2024.

[55] 5G Infrastructure Public Private Partnership (5G PPP). Beyond 5G/6G KPIs and Target Values. 2022.

[56] Khan, T.; et al. Understanding effects of visual feedback delay in ar on fine motor surgical tasks. IEEE Trans. Vis. Comput. Graph. 2023, 29, 4697–4707.

[57] Zhao, L.; et al. Remote Driving of Road Vehicles: A Survey of Driving Feedback, Latency, Support Control, and Real Applications. IEEE Trans. Intell. Veh. 2024, 1–22.

[58] Dreibholz, T.; Mazumdar, S. Towards a lightweight task scheduling framework for cloud and edge platform. Internet Things 2023, 21, 100651.

[59] Makondo, N.; et al. An efficient architecture for latency optimisation in 5G using Edge Computing for uRLLC use cases. In Proceedings of the 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Port Louis, Mauritius, 1–2 August 2024; pp. 1–7.

[60] Velayutham, A. Optimizing sase for low latency and high bandwidth applications: Techniques for enhancing latency-sensitive systems. Int. J. Intell. Autom. Comput. 2023, 6, 63–83.

[61] Lin, Y.H.; et al. Hapticseer: A multi-channel, black-box, platform-agnostic approach to detecting video game events for real-time haptic feedback. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Online, 8–13 May 2021; pp. 1–14.

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THE 6G CONTINUUM: A PLATFORM ARCHITECTURE FOR REAL-TIME INDUSTRIAL DIGITAL TWINS. (2024). European Journal of Emerging Real-Time IoT and Edge Infrastructures, 1(01), 69-76. https://parthenonfrontiers.com/index.php/ejertiotei/article/view/138

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