Published March 25, 2026 | Version v1
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DESIGNING GREEN DIGITAL TWIN-IOT SYSTEMS: ARCHITECTURAL PATTERNS, ENERGY TRADE OFFS AND EVALUATION METRICS

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ABSTRACT: Digital Twin (DT) systems combined with the Internet of Things (IoT) are revolutionizing sectors such as industry, healthcare, and energy, but the environmental impact of software design choices remains poorly assessed. This study reviews 42 peer-reviewed papers (2015–2025) on DT–IoT software architectures to examine how computation distribution, service design, and communication layers influence energy efficiency, carbon emissions, and long-term maintainability. The analysis categorizes edge, cloud, and hybrid-based frameworks, outlining trade-offs among latency, throughput, and energy use. A unified benchmarking framework is proposed, featuring standardized metrics for latency, throughput, energy per event, carbon intensity, and orchestration overhead. Through a smart-campus case example, a hybrid setup demonstrates reduced energy and carbon intensity while maintaining latency below 100 ms. The findings emphasize sustainability as a central principle in the design of next-generation DT–IoT architectures.

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  • Grieves, M.; Vickers, J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems; Springer: Cham, Switzerland, 2017; pp. 85–113. Tao, F.; Qi, Q.; Liu, A.; Kusiak, A. Data-Driven Smart Manufacturing. J. Manuf. Syst. 2018, 48, 157–169. Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016, 3, 637–646. Satyanarayanan, M. The Emergence of Edge Computing. Computer 2017, 50, 30–39. Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A Survey. Comput. Netw. 2010, 54, 2787–2805. Chen, Y.; Li, Y.; Zhang, T. Energy Consumption Modeling of Edge Devices for Sustainable IoT. Future Gener. Comput. Syst. 2021, 125, 82–92. Greenberg, A.; Hamilton, J.; Maltz, D.A.; Patel, P. The Cost of a Cloud: Research Problems in Data Center Networks. ACM SIGCOMM Comput. Commun. Rev. 2008, 39, 68–73. Li, S.; Da Xu, L.; Zhao, S. 5G Internet of Things: A Survey. J. Ind. Inf. Integr. 2018, 10, 1–9. An, Y.; Kim, J.; Kim, H. Lightweight Orchestration for Microservices in Edge–Cloud Environments. IEEE Access 2020, 8, 21667–21677. Singh, K.; Rajan, R.; Verma, S. Comparative Analysis of MQTT and HTTP Protocols for IoT Applications. Procedia Comput. Sci. 2018, 132, 1611–1618. Marjani, M.; Nasaruddin, F.; Gani, A.; Karim, A.; Hashem, I.A.T.; Siddiqa, A.; Yaqoob, I. Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges. IEEE Access 2017, 5, 5247–5261. Leitner, S.; Mahnke, W. OPC UA Service-Oriented Architecture for Industrial Applications. Softw. Eng. 2006, 3, 26–31. Thubert, P.; Papadopoulos, G.Z.; Wang, Q. Time-Sensitive Networking for IoT: Impact and Opportunities. IEEE Commun. Mag. 2019, 57, 70–76. FIWARE Foundation. FIWARE: The Open Source Platform for the Smart Digital Future. FIWARE Foundation. 2021. Available online: https://www.fiware.org (accessed on 15 September 2025). EdgeX Foundry. EdgeX Foundry: An Open Interoperability Framework for IoT Edge Computing. Linux Foundation. 2020. Available online: https://www.edgexfoundry.org (accessed on 15 September 2025). Kansal, A.; Zhao, F.; Liu, J. Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services. USENIX NSDI 2010, 1–14. Pathak, A.; Hu, Y.C.; Zhang, M. Where Is the Energy Spent Inside My App? Fine-Grained Energy Accounting on Smartphones with Eprof. Proceedings of the 7th ACM EuroSys Conference 2012, 29–42. Deng, R.; Lu, R.; Lai, C.; Luan, T.H. Toward Power Consumption and Carbon Footprint Modeling of Data Centers. IEEE Commun. Mag. 2014, 52, 92–99. Liu, Z.; Lin, M.; Wierman, A.; Low, S.H.; Andrew, L.L.H. Greening Geographical Load Balancing. ACM SIGMETRICS Perform. Eval. Rev. 2011, 39, 233–244. Baccarelli, E.; Naranjo, P.G.V.; Scarpiniti, M.; Shojafar, M.; Abawajy, J.H. Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study. IEEE Access 2017, 5, 9882–9910. Dragoni, N.; Giallorenzo, S.; Lafuente, A.L.; Mazzara, M.; Montesi, F.; Mustafin, R.; Safina, L. Microservices: Yesterday, Today, and Tomorrow. In Present and Ulterior Software Engineering; Springer: Cham, Switzerland, 2017; pp. 195–216. Zeng, L.; Zhou, Z.; Wang, Q. Energy-Aware Digital Twin Benchmarking for Sustainable IoT Systems. IEEE Trans. Sustain. Comput. 2022, 7, 455–468. Verdouw, C.; Wolfert, J.; Tekinerdogan, B. Digital Twins in Smart Farming: Conceptual Framework and Use Cases. Agric. Syst. 2021, 189, 103046. Pautasso, C.; Zimmermann, O.; Leymann, F. RESTful Web Services vs. "Big" Web Services: Making the Right Architectural Decision. Proceedings of the 17th Int. Conf. World Wide Web 2008, 805–814. Xu, X.; Lu, Y.; Vogel-Heuser, B.; Wang, L. Industry 4.0 and Digital Twins for Smart Manufacturing: A Survey. J. Manuf. Syst. 2021, 58, 319–335. Gholami, A.; Qu, Z.; He, Y.; Wang, L. Energy-to-Performance Ratios in Sustainable Cloud–IoT Systems. Future Gener. Comput. Syst. 2022, 128, 223–234. Verdouw, C.; Wolfert, J.; Beulens, A.; Üstündağ, A. Virtualization of Food Supply Chains with Digital Twins. Comput. Electron. Agric. 2020, 169, 105234. Kantarci, B.; Mouftah, H.T. Mobility-Aware Secure and Energy-Efficient IoT: A Survey. IEEE Commun. Surv. Tutor. 2018, 20, 1131–1150. Wurm, J.; Hoang, D.T.; Kowalski, S.; Gajic, Z. On the Energetic Cost of Data Transmission in IoT Systems: Measurements and Models. Sensors 2019, 19, 1234. Li, H.; Ouyang, Y.; Peng, Q.; Hu, R. Carbon-Aware Scheduling for Cloud and Edge Resources. IEEE Trans. Green Commun. Netw. 2021, 5, 112–125. Chen, X.; He, Y.; Li, Z.; Wang, P. Benchmarking IoT Middleware: Performance, Scalability, and Energy Footprint. IEEE Access 2019, 7, 102312–102327. Kerl, S.; Bröring, A.; Krco, S.; et al. NGSI-LD as a Standardized Context Information Model for Smart Systems Interoperability. Sensors 2020, 20, 4476. Dutta, S.; Banerjee, S.; Alam, M. Energy Measurement Methodologies for IoT Testbeds: A Comparative Study. Proceedings of the ACM SenSys 2018, 1–12. Saha, D.; Mukherjee, S.; Roy, S. A Survey on Resource Allocation Techniques in Edge–Cloud Environments. Comput. Commun. 2020, 151, 75–88. Khosravi, H.; Sadeghi, A.R.; Ghodsi, R.; et al. Lifecycle Assessment of IoT Devices: Methodologies and Open Challenges. J. Clean. Prod. 2022, 345, 130789. Morabito, R.; Cozzolino, V.; Ding, A.Y.; Beijar, N.; Ott, J. Consolidate IoT Fog Architectures: A Survey on Fog Computing and Edge Intelligence. IEEE Commun. Surv. Tutor. 2018, 20, 1826–1859. Hölbl, M.; Welzer, T.; Ristolainen, M.; et al. Energy-Efficient Machine Learning at the Edge: Techniques and Benchmarks. IEEE Trans. Emerg. Top. Comput. 2021, 9, 1234–1249. Boos, H.; Rothermel, K.; Wacker, K.; et al. Reproducible Benchmarking for Distributed IoT Systems: Design and Lessons Learned. J. Syst. Softw. 2020, 165, 110572. Wirth, N. Microservice Architecture and Its Impact on Software Maintainability and Energy Consumption. Softw. Pract. Exper. 2019, 49, 1238–1255. European Commission. Report on Energy Consumption of ICT and the Role of Digitalization for the European Green Deal. Publications Office of the EU, 2021. Muka, E.; Marinova, G. Digital Twins to Monitor IoT Devices for Green Transformation of University Campus. In Proceedings of the 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom), Graz, Austria, 10–12 July 2024; pp. 1–6. Muka, E.; Mankolli, E.; Marinova, G. Anomaly Detection in Digital Twins: Leveraging AI for Real-Time Insight. In Proceedings of the 2025 32nd International Conference on Systems, Signals and Image Processing (IWSSIP), Skopje, North Macedonia, 25–27 June 2025; pp. 1–4