journal article·2025-02-17Open access

IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System

Ali Aghazadeh Ardebili, Cristian Martella, Antonella Longo, Chiara Rucco, Federico Izzi, Antonio Ficarella

Applied Sciences· Volume 15· Issue 4

Abstract

This study focuses on Digital Twin-integrated smart energy systems, which serve as an example of Next-Generation Critical Infrastructures (CI). The resilience of these systems is influenced by a variety of internal features and external interactions, all of which are subject to change following cyber-physical disturbances. This necessitates real-time resilience monitoring for CI during crises; however, a significant gap remains in resilience monitoring. To address this gap, this study leverages the role of Internet of Things (IoT) in monitoring complex systems to enhance resilience through critical indicators relevant to cyber-physical safety and security. The study empirically implements Resilience-Key Performance Indicators (R-KPIs) from the domain, including Functionality Loss, Minimum Performance, and Recovery Time Duration. The main goal is to examine real-time IoT-based resilience monitoring in a real-life context. A cyber-physical system equipped with IoT-driven Digital Twins, data-driven microservices, and a False Data Injection Attack (FDIA) scenario is simulated to assess the real-time resilience of this smart system. The results demonstrate that real-time resilience monitoring provides actionable insights into resilience performance based on the selected R-KPIs. These findings contribute to a systematic and reusable model for enhancing the resilience of IoT-enabled CI, advancing efforts to ensure service continuity and secure essential services for society.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Smart Grid Security and Resilience · Infrastructure Resilience and Vulnerability Analysis · Risk and Safety Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy

Department of Research and Development, HSPI SpA, 00185 Rome, Italy

References (94)

Show source-provided references
1. Artificial intelligence in cyber physical systems10.1007/s00146-020-01049-0 ↗
2. HARCI-EU, a harmonized gridded dataset of critical infrastructures in Europe for large-scale risk assessments10.1038/s41597-019-0135-1 ↗
3. Reviewing qualitative research approaches in the context of critical infrastructure resilience10.1007/s10669-020-09795-8 ↗
4. An Assessment of the Impacts on the International Container Transport and the World Economy Resulting from the 2014/15 U.S. West Coast Port Disruption10.5595/idrim.2018.0295 ↗
5. Development and assessment of a resilient telecoms system
6. Understanding Business Disruption and Economic Losses Due to Electricity Failures and Flooding10.1007/s13753-019-00236-y ↗
7. Impact of power outages: Unveiling their influence on micro, small, and medium-sized enterprises and poverty in Sub-Saharan Africa - An in-depth literature review10.1016/j.heliyon.2024.e33782 ↗
8. Disentangling Resilience From Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms10.1109/MCS.2024.3382415 ↗
9. On the resilience of modern power systems: A comprehensive review from the cyber-physical perspective10.1016/j.rser.2021.111642 ↗
10. Kwasinski, A. (2020, January 21). Modeling of Cyber-Physical Intra-Dependencies in Electric Power Grids and Their Effect on Resilience. Proceedings of the 2020 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, Sydney, Australia.10.1109/MSCPES49613.2020.9133702 ↗
11. Loss and damage assessment in critical infrastructures due to extreme events10.1016/j.ssci.2021.105587 ↗
12. Loveček, T., Straková, L., and Kampová, K. (2021). Modeling and Simulation as Tools to Increase the Protection of Critical Infrastructure and the Sustainability of the Provision of Essential Needs of Citizens. Sustainability, 13.10.3390/su13115898 ↗
13. Carrington, N.K., Ma, S., Dobson, I., and Wang, Z. (2020, January 2–6). Extracting Resilience Statistics from Utility Data in Distribution Grids. Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada.10.1109/PESGM41954.2020.9281596 ↗
14. Identification of indicators, metrics and level of service for the resilience of transport critical infrastructure
15. Monitoring of transport infrastructure exposed to multiple hazards: A roadmap for building resilience10.1016/j.scitotenv.2020.141001 ↗
16. Aghazadeh Ardebili, A., Ficarella, A., Longo, A., and Mazzeo, O. (2024, December 03). Critical Infrastructure Resilience. Available online: https://iris.unisalento.it/handle/11587/503626.
17. Aghazadeh Ardebili, A., and Padoano, E. (2020). A literature review of the concepts of resilience and sustainability in group decision-making. Sustainability, 12.10.3390/su12072602 ↗
18. Chaterji, S., Naghizadeh, P., Alam, M.A., Bagchi, S., Chiang, M., Corman, D., Henz, B., Jana, S., Li, N., and Mou, S. (2019). Resilient cyberphysical systems and their application drivers: A technology roadmap. arXiv.
19. Ardebili, A.A., Longo, A., and Ficarella, A. (2024, December 03). Implementing Digital Twins in Energy Systems: Meta Analysis of the State of Art and Evolution. Available online: https://www.semanticscholar.org/paper/Implementing-Digital-Twins-in-Energy-Systems%3A-Meta-Ardebili-Longo/16e088485affb69bd5c5b4f88bb7b68c08bd8a4a.
20. Ardebili, A.A., Longo, A., and Ficarella, A. (2021, January 6–8). Digital Twins bonds society with cyber-physical Energy Systems: A literature review. Proceedings of the 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Melbourne, Australia.
21. Habibi Rad, M., Mojtahedi, M., and Ostwald, M.J. (2021). Industry 4.0, disaster risk management and infrastructure resilience: A systematic review and bibliometric analysis. Buildings, 11.10.3390/buildings11090411 ↗
22. Developing human-centered urban digital twins for community infrastructure resilience: A research agenda10.1177/08854122221137861 ↗
23. Digital technologies can enhance climate resilience of critical infrastructure10.1016/j.crm.2021.100387 ↗
24. Transformative innovation and socio-technical transitions to address grand challenges
25. Rohracher, H. (2018). 45Analyzing the Socio-Technical Transformation of Energy Systems: The Concept of Sustainability Transitions. Oxford Handbook of Energy and Society, Oxford University Press.10.1093/oxfordhb/9780190633851.013.3 ↗
26. Towards future infrastructures for sustainable multi-energy systems: A review10.1016/j.energy.2019.05.057 ↗
27. Digital Twins of smart energy systems: A systematic literature review on enablers, design, management and computational challenges10.1186/s42162-024-00385-5 ↗
28. Ardebili, A.A., Longo, A., and Ficarella, A. (2023, January 15–18). Navigating the Future Data-Driven Automation Tools: State-of-the-Art and Research Roadmap for Digital Twins of Energy Systems. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy.10.1109/BigData59044.2023.10386762 ↗
29. Enhancing resilience in complex energy systems through real-time anomaly detection: A systematic literature review10.1186/s42162-024-00401-8 ↗
30. Understanding household energy consumption behavior: The contribution of energy big data analytics10.1016/j.rser.2015.12.001 ↗
31. Big data driven smart energy management: From big data to big insights10.1016/j.rser.2015.11.050 ↗
32. The role of data analysis in the development of intelligent energy networks
33. Security aspects of future renewable energy systems–A short overview10.1016/j.energy.2013.09.023 ↗
34. Some future scenarios for renewable energy10.1016/j.futures.2011.07.008 ↗
35. Bessa, R., Moreira, C., Silva, B., and Matos, M. (2019). Handling renewable energy variability and uncertainty in power system operation. Advances in Energy Systems: The Large-Scale Renewable Energy Integration Challenge, John Wiley & Sons.10.1002/9781119508311.ch1 ↗
36. Navigating challenges in large-scale renewable energy storage: Barriers, solutions, and innovations10.1016/j.egyr.2024.08.019 ↗
37. Bremen, L.V. (2010). Large-scale variability of weather dependent renewable energy sources. Management of Weather and Climate Risk in the Energy Industry, Springer.10.1007/978-90-481-3692-6_13 ↗
38. Cyber vulnerabilities of energy systems10.1109/JESTIE.2024.3434350 ↗
39. Demertzis, K., and Iliadis, L. (2018). A computational intelligence system identifying cyber-attacks on smart energy grids. Modern Discrete Mathematics and Analysis: With Applications in Cryptography, Information Systems and Modeling, Springer.10.1007/978-3-319-74325-7_5 ↗
40. Cyber-Physical Systems Security for the Electric Power Grid10.1109/JPROC.2011.2165269 ↗
41. Yang, Y., Du, H., Xiong, Z., Xu, R., Niyato, D., and Han, Z. (2025). Exploring Impacts of Age of Information on Data Accuracy for Wireless Sensing Systems: An Information Entropy Perspective. IEEE Trans. Mob. Comput., 1–18.10.1109/TMC.2025.3527587 ↗
42. Age of information in internet of things: A survey10.1109/JIOT.2023.3324879 ↗
43. Information freshness optimization of multiple status update streams in Internet of things: Generation rate control and service rate reservation10.1016/j.dcan.2022.05.001 ↗
44. AoI-Minimal Clustering, Transmission and Trajectory Co-Design for UAV-Assisted WPCNs10.1109/TVT.2024.3461333 ↗
45. Aghazadeh Ardebili, A., Martella, C., Martella, A., Lazari, A., Longo, A., and Ficarella, A. (2024, January 8–12). Smart critical infrastructures security management and governance: Implementation of cyber resilience kpis for decentralized energy asset. Proceedings of the CEUR Workshop Proceedings-Italian Conference on Cyber Security 2024: Proceedings of the 8th Italian Conference on Cyber Security (ITASEC 2024), Salerno, Italy.
46. Infrastructure resilience curves: Performance measures and summary metrics10.1016/j.ress.2021.107926 ↗
47. Aghazadeh Ardebili, A., Boscolo, M., Ficarella, A., Longo, A., and Padoano, E. (2024, January 11–13). Resilience Modeling of Cyber-Physical Systems: A Comparative Study of Statistical vs. AI Methods. Proceedings of the XXIX AIDI Summer School “Francesco Turco” Industrial Systems Engineering, Lecce, Italy.
48. Stochastic indexes for power distribution systems resilience analyzes10.1049/iet-gtd.2018.6667 ↗
49. Computational methodologies for critical infrastructure resilience modeling: A review10.1016/j.aei.2024.102663 ↗
50. Resilience in information stewardship10.1016/j.ejor.2018.10.020 ↗
51. Rezvani, S.M., Silva, M.J.F., and de Almeida, N.M. (2024). Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks. Sustainability, 16.10.3390/su16104143 ↗
52. Stochastic resilient post-hurricane power system recovery based on mobile emergency resources and reconfigurable networked microgrids10.1109/ACCESS.2018.2881949 ↗
53. An integrated tri-level model for enhancing the resilience of facilities against intentional attacks10.1007/s10479-017-2705-y ↗
54. Role of recovery profile dependency in time-dependent resilience10.1002/eng2.12716 ↗
55. Restimate: Recovery Estimation Tool for Resilience Planning
56. Multi-objective optimization for community building group recovery scheduling and resilience evaluation under earthquake10.1111/mice.12882 ↗
57. Resilience-based sequential recovery planning for substations subjected to earthquakes10.1109/TPWRD.2022.3187162 ↗
58. Lee, S., Shin, S., Judi, D., McPherson, T., and Burian, S. (2019). Criticality analyzes of a water distribution system considering both economic consequences and hydraulic loss using modern portfolio theory. Water, 11.10.3390/w11061222 ↗
59. Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analyzes10.1016/j.ress.2019.04.017 ↗
60. On the component resilience importance measures for infrastructure systems10.1016/j.ijcip.2021.100481 ↗
61. Time-dependent resilience assessment of seismic damage and restoration of interdependent lifeline systems10.1061/(ASCE)IS.1943-555X.0000522 ↗
62. Stochastic optimal control methodologies in risk-informed community resilience planning10.1016/j.strusafe.2019.101920 ↗
63. Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems10.1016/j.ress.2023.109200 ↗
64. Value of resilience-based solutions on critical infrastructure protection: Comparing with robustness-based solutions10.1016/j.ress.2019.106506 ↗
65. Power systems resilience assessment: Hardening and smart operational enhancement strategies10.1109/JPROC.2017.2691357 ↗
66. Drinking water supply in resilient cities: Notes from L’Aquila earthquake case study10.1016/j.scs.2016.09.005 ↗
67. Assessing resilience of healthcare infrastructure exposed to COVID-19: Emerging risks, resilience indicators, interdependencies and international standards10.1007/s10669-020-09779-8 ↗
68. Rosales-Asensio, E., Elejalde, J.L., Pulido-Alonso, A., and Colmenar-Santos, A. (2022). Resilience framework, methods, and metrics for the prioritization of critical electrical grid customers. Electronics, 11.10.3390/electronics11142246 ↗
69. González, C., Niño, M., and Ayala, G. (2023). Functionality Loss and Recovery Time Models for Structural Elements, Non-Structural Components, and Delay Times to Estimate the Seismic Resilience of Mexican School Buildings. Buildings, 13.10.3390/buildings13061498 ↗
70. Brown, R.G. (1963). Smoothing, Forecasting and Prediction of Discrete Time Series, Prentice-Hall.
71. Chatfield, C. (2000). Time-Series Forecasting, CRC Press.10.1201/9781420036206 ↗
72. Murphy, J.J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, Penguin.
73. Maddala, G.S. (2001). Introduction to Econometrics, Wiley.
74. Noise modeling for photovoltaic systems using real-world data
75. Noise and signal interference in photovoltaic power data
76. Pratama, I., Prasetyaningrum, P.T., and Setyaningsih, P.W. (2019, January 24–25). Time-Series Data Forecasting and Approximation with Smoothing Technique. Proceedings of the 2019 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.10.1109/ICOIACT46704.2019.8938436 ↗
77. Li, D., Yu, X., Chen, J., Lin, Z., He, S., Liu, J., Yao, Y., and Wu, Y. (2021, January 22–24). Typical Load Analysis and Forecast of a Provincial Power Grid in Mid-Southern China. Proceedings of the 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China.10.1109/EI252483.2021.9713621 ↗
78. Characterizing the ramps and noise in solar power imbalances10.1016/j.solener.2022.10.020 ↗
79. Ernst, B., Wan, Y.H., and Kirby, B. (1999). Short-Term Power Fluctuation of Wind Turbines: Analyzing Data from the German 250-MW Measurement Program from the Ancillary Services Viewpoint, National Renewable Energy Lab.(NREL). Technical Report.
80. Chhabra, M., Lim, M., and Barnes, F. (2011, January 1–3). Frequency stabilization using solar smoothing, leveling and time shifting in a hybrid renewablenetwork. Proceedings of the ISGT2011-India, Kollam, Kerala, India.10.1109/ISET-India.2011.6145396 ↗
81. Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts.
82. Ardebili, A.A., Martella, A., Martella, C., Longo, A., and Ficarella, A. (2024, January 15–18). Digital Twins: Case Study of Energy Metaverse and Edge-Cloud Integration. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.10.1109/BigData62323.2024.10825392 ↗
83. Chai, B.X., Gunaratne, M., Ravandi, M., Wang, J., Dharmawickrema, T., Di Pietro, A., Jin, J., and Georgakopoulos, D. (2024). Smart industrial internet of things framework for composites manufacturing. Sensors, 24.10.3390/s24154852 ↗
84. Yılmaz, N., Alatlı, O., Çiloğlugil, B., and Erdur, R.C. (2018, January 28–30). Evaluation of storage and query performance of sensor based Internet of Things data with MongoDB. Proceedings of the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey.10.1109/IDAP.2018.8620837 ↗
85. Pandey, R. (2020). Performance Benchmarking and Comparison of Cloud-Based Databases MongoDB (NoSQL) vs. MySQL (Relational) Using YCSB. Electron. Resour., Available online: https://www.researchgate.net/publication/344047197_Performance_Benchmarking_and_Comparison_of_Cloud-Based_Databases_MongoDB_NoSQL_Vs_MySQL_Relational_using_YCSB.
86. Performance evaluation of IoT data management using MongoDB versus MySQL databases in different cloud environments10.1109/ACCESS.2020.3002164 ↗
87. Ardebili, A.A., Longo, A., and Ficarella, A. (2023, January 14–17). Digital twinning of PV modules for smart systems-A comparison between commercial and open-source simulation models. Proceedings of the 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Abu Dhabi, United Arab Emirates.10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361505 ↗
88. Aghazadeh Ardebili, A., Ficarella, A., Longo, A., Khalil, A., and Khalil, S. (2023). Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation. Aerospace, 10.10.20944/preprints202307.0981.v1 ↗
89. Aghazadeh Ardebili, A., Longo, A., Ficarella, A., Khalil, A., and Khalil, S. (2023). Exploring Synthetic Noise Algorithms for Real-World Similar Data Generation: A Case Study on Digitally Twining Hybrid Turbo-Shaft Engines in UAV/UAS Applications. Model and Data Engineering: Proceedings of the 12th International Conference (MEDI 2023), Sousse, Tunisia, 2–4 November 2023, Springer.10.1007/978-3-031-49333-1_7 ↗
90. The European Commission (2024, December 03). European Industrial Technology Roadmap for the Next Generation Cloud-Edge Offering. Technical Report. Available online: https://ec.europa.eu/newsroom/repository/document/2021-18/European_CloudEdge_Technology_Investment_Roadmap_for_publication_pMdz85DSw6nqPppq8hE9S9RbB8_76223.pdf.
91. Somma, A., Benedictis, A.D., Zappatore, M., Martella, C., Martella, A., and Longo, A. (2023, January 15–18). Digital Twin Space: The Integration of Digital Twins and Data Spaces. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy.10.1109/BigData59044.2023.10386737 ↗
92. Switching and data injection attacks on stochastic cyber-physical systems: Modeling, resilient estimation, and attack mitigation10.1145/3204439 ↗
93. Collaborative load management with safety assurance in smart grids10.1145/2823351 ↗
94. System resilience through health monitoring and reconfiguration10.1145/3631612 ↗