An Architecture-Centric Evaluation Model for European Union–Funded IoT and Edge Computing Research Ecosystems
Keywords:
IoT architecture, edge computing, fog computing, EU-funded projectsAbstract
The rapid expansion of Internet of Things (IoT) and edge computing technologies has significantly transformed distributed digital infrastructures, particularly within research and innovation programs funded by the European Union (EU). However, despite substantial investment in IoT and edge computing ecosystems, there remains a lack of standardized architecture-centric evaluation frameworks capable of systematically assessing interoperability, scalability, intelligence distribution, and infrastructural maturity across heterogeneous EU-funded projects. This paper proposes a comprehensive architecture-centric evaluation model designed to analyze IoT and edge computing research ecosystems through a structured lens that integrates fog-cloud continuum principles, distributed intelligence paradigms, and platform interoperability considerations.
The study synthesizes established theoretical foundations from industrial IoT, fog computing, and edge analytics literature to construct an evaluation framework that captures architectural complexity across multiple layers of computing—device, edge, fog, and cloud. The proposed model integrates key evaluation dimensions such as system scalability, latency optimization, data analytics efficiency, and privacy-aware distributed processing. Drawing on prior research in fog and edge computing architectures (Aazam, Zeadally, & Harras, 2018), distributed intelligence systems, and EU IoT initiatives, the paper critically examines how architectural design choices influence system performance, sustainability, and real-world deployability.
Findings from the conceptual evaluation demonstrate that EU-funded IoT ecosystems exhibit strong innovation potential but remain fragmented in architectural standardization and cross-project interoperability. The study highlights the need for unified architectural benchmarking mechanisms to ensure long-term sustainability and integration of edge-based intelligence systems. The proposed model contributes to bridging the gap between theoretical advancements and practical deployment in large-scale IoT research ecosystems.
Downloads
References
1. Aazam M, S. Zeadally, and K. A. Harras, ‘‘Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities,’’ Future Gener. Comput. Syst., vol. 87, pp. 278–289, Oct. 2018.
2. T. Alsboui, Y. Qin, R. Hill, and H. Al-Aqrabi, ‘‘Distributed intelligence in the Internet of Things: Challenges and opportunities,’’ Social Netw. Comput. Sci., vol. 2, no. 4, pp. 1–16, Jul. 2021.
3. P. Bellavista, J. Berrocal, A. Corradi, S. K. Das, L. Foschini, and A. Zanni, ‘‘A survey on fog computing for the Internet of Things,’’ Pervasive Mobile Comput., vol. 52, pp. 71–99, Jan. 2019.
4. P. Bellavista, R. D. Penna, L. Foschini, and D. Scotece, ‘‘Machine learning for predictive diagnostics at the edge: An IIoT practical example,’’ in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2020, pp. 1–7.
5. V. Demertzi, S. Demertzis, and K. Demertzis, ‘‘An overview of privacy dimensions on the industrial Internet of Things (IIoT),’’ Algorithms, vol. 16, no. 8, p. 378, Aug. 2023.
6. Eclipse Foundation. Eclipse Ditto. Accessed: Jul. 18, 2024. [Online]. Available: https://eclipse.dev/ditto/intro-overview.html
7. A. E. Edje, M. S. Abd Latiff, and W. H. Chan, ‘‘IoT data analytic algorithms on edge-cloud infrastructure: A review,’’ Digit. Commun. Netw., vol. 9, no. 6, pp. 1486–1515, Dec. 2023.
8. F. Firouzi, B. Farahani, and A. Marinšek, ‘‘The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT),’’ Inf. Syst., vol. 107, Jul. 2022, Art. no. 101840.
9. F. Firouzi, S. Jiang, K. Chakrabarty, B. Farahani, M. Daneshmand, J. Song, and K. Mankodiya, ‘‘Fusion of IoT, AI, Edge–Fog–cloud, and blockchain: Challenges, solutions, and a case study in healthcare and medicine,’’ IEEE Internet Things J., vol. 10, no. 5, pp. 3686–3705, Mar. 2023.
10. A. Hemmati, P. Raoufi, and A. M. Rahmani, ‘‘Edge artificial intelligence for big data: A systematic review,’’ Neural Comput. Appl., vol. 36, no. 19, pp. 11461–11494, Jul. 2024.
11. R. Kumar and N. Agrawal, ‘‘Analysis of multi-dimensional Industrial IoT (IIoT) data in edge-fog-cloud based architectural frameworks: A survey on current state and research challenges,’’ J. Ind. Inf. Integration, vol. 35, Oct. 2023, Art. no. 100504.
12. J.-Q. Li, F. R. Yu, G. Deng, C. Luo, Z. Ming, and Q. Yan, ‘‘Industrial internet: A survey on the enabling technologies, applications, and challenges,’’ IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1504–1526, 3rd Quart., 2017.
13. Z. Li, F. Fei, and G. Zhang, ‘‘Edge-to-cloud IIoT for condition monitoring in manufacturing systems with ubiquitous smart sensors,’’ Sensors, vol. 22, no. 15, p. 5901, Aug. 2022.
14. Microsoft. Azure Internet of Things (IoT). Accessed: Jul. 18, 2024. [Online]. Available: https://learn.microsoft.com/en-us/azure/iot/iotintroduction
15. R. K. Naha, S. Garg, D. Georgakopoulos, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan, ‘‘Fog computing: Survey of trends, architectures, requirements, and research directions,’’ IEEE Access, vol. 6, pp. 47980–48009, 2018.
16. D. Ottolini, I. Zyrianoff, and C. Kamienski, ‘‘Interoperability and scalability trade-offs in open IoT platforms,’’ in Proc. IEEE 19th Annu. Consum. Commun. Netw. Conf. (CCNC), Jan. 2022, pp. 1–6.
17. E. Oyekanlu, ‘‘Distributed osmotic computing approach to implementation of explainable predictive deep learning at industrial IoT network edges with real-time adaptive wavelet graphs,’’ in Proc. IEEE 1st Int. Conf. Artif. Intell. Knowl. Eng. (AIKE), Sep. 2018, pp. 179–188.
18. T. Qiu, J. Chi, X. Zhou, Z. Ning, M. Atiquzzaman, and D. O. Wu, ‘‘Edge computing in industrial Internet of Things: Architecture, advances and challenges,’’ IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2462–2488, 4th Quart., 2020.
19. J. Ren, Y. Pan, A. Goscinski, and R. A. Beyah, ‘‘Edge computing for the Internet of Things,’’ IEEE Netw., vol. 32, no. 1, pp. 6–7, Jan. 2018.
20. D. Rosendo, A. Costan, P. Valduriez, and G. Antoniu, ‘‘Distributed intelligence on the edge-to-cloud continuum: A systematic literature review,’’ J. Parallel Distrib. Comput., vol. 166, pp. 71–94, Aug. 2022.
21. S. K. Sharma and X. Wang, ‘‘Live data analytics with collaborative edge and cloud processing in wireless IoT networks,’’ IEEE Access, vol. 5, pp. 4621–4635, 2017.
22. S. Z. Tajalli, M. Mardaneh, E. Taherian-Fard, A. Izadian, A. Kavousi-Fard, M. Dabbaghjamanesh, and T. Niknam, ‘‘DoS-resilient distributed optimal scheduling in a fog supporting IIoT-based smart microgrid,’’ IEEE Trans. Ind. Appl., vol. 56, no. 3, pp. 2968–2977, May 2020.
23. ThingsBoard. (2024). ThingsBoard—Open-Source IoT Platform. Accessed: Jul. 18, 2024. [Online]. Available: https://thingsboard.io
24. I. Ungurean and N. C. Gaitan, ‘‘A software architecture for the industrial Internet of Things—A conceptual model,’’ Sensors, vol. 20, no. 19, pp. 1–19, 2020.
25. M. Urbina, T. Acosta, J. Lázaro, A. Astarloa, and U. Bidarte, ‘‘Smart sensor: SoC architecture for the industrial Internet of Things,’’ IEEE Internet Things J., vol. 6, no. 4, pp. 6567–6577, Aug. 2019.
26. P. Zheng, X. Xu, and L. Wang, ‘‘Editorial notes: Industrial Internet-ofThings (IIoT)-enabled digital servitisation,’’ Int. J. Prod. Res., vol. 61, no. 12, pp. 3844–3848, 2023.
