Logo PTI Logo FedCSIS

Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Data Interoperability Using Smart Data Models and NGSI-LD for the Norwegian Agrifood Sector

, , , , ,

DOI: http://dx.doi.org/10.15439/2025F5425

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 297–302 ()

Full text

Abstract. The growing adoption of digital technologies in agriculture has led to a proliferation of heterogeneous data from sources such as drones, robotic platforms, and IoT sensors. However, the lack of interoperability across these data streams poses major challenges for integration into decision support systems. This paper presents an approach to harmonising such data using NGSI-LD and Smart Data Models, developed within the Norwegian research project SMARAGD. We demonstrate how domain-specific semantic models and linked data principles can be applied to standardise and enrich geospatial and temporal metadata across three key agritech domains: aerial imagery, robotic sensing, and environmental monitoring. The resulting information assets are integrated into a shared, FIWARE-compatible data space, enabling cross-platform visualisation, querying, and reuse. This work contributes to the development of an open, standards-based digital infrastructure for interoperable, data-driven agriculture in Norway and beyond.

References

  1. R. Dautov, S. Tverdal, A. S. Bondevik, S. A. Frøshaug, V. Szabo, J. R. Fiksdal, and M. F. Stølen, “SMARAGD: Data Interoperability for Decision Support in the Norwegian Agrifood Sector,” in Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning (RuleML+RR-Companion 2024), 2024.
  2. O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, “An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges,” IEEE Internet of things Journal, vol. 5, no. 5, pp. 3758–3773, 2018. https://dx.doi.org/10.1109/JIOT.2018.2844296
  3. V. Pesce, G.-W. Kayumbi, J. Tennison, L. Mey, and P. Zervas, “Agrifood data standards: a gap exploration report,” F1000Research, vol. 7, no. 176, p. 176, 2018. https://dx.doi.org/10.7490/f1000research.1115261.1
  4. S. Mishra and S. Jain, “Ontologies as a semantic model in IoT,” International Journal of Computers and Applications, vol. 42, no. 3, pp. 233–243, 2020. https://dx.doi.org/10.1080/1206212X.2018.1504461
  5. I. Szilagyi and P. Wira, “Ontologies and Semantic Web for the Internet of Things-a survey,” in IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. https://dx.doi.org/10.1109/IECON.2016.7793744 pp. 6949–6954.
  6. A. Raj, J. Bosch, H. H. Olsson, and T. J. Wang, “Modelling data pipelines,” in 2020 46th Euromicro conference on software engineering and advanced applications (SEAA). IEEE, 2020. https://dx.doi.org/10.1109/SEAA51224.2020.00014 pp. 13–20.
  7. J. López-Riquelme, N. Pavón-Pulido, H. Navarro-Hellín, F. Soto-Valles, and R. Torres-Sánchez, “A software architecture based on FIWARE cloud for Precision Agriculture,” Agricultural water management, vol. 183, pp. 123–135, 2017. https://dx.doi.org/10.1016/j.agwat.2016.10.020
  8. P. Corista, D. Ferreira, J. Gião, J. Sarraipa, and R. J. Gonçalves, “An IoT agriculture system using FIWARE,” in 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, 2018. https://dx.doi.org/10.1109/ICE.2018.8436381 pp. 1–6.
  9. M. A. Rodriguez, L. Cuenca, and A. Ortiz, “FIWARE open source standard platform in smart farming-a review,” in 19th Working Conference on Virtual Enterprises (PRO-VE 2018). Springer, 2018. https://dx.doi.org/10.1007/978-3-319-99127-6_50 pp. 581–589.
  10. I. Roussaki, K. Doolin, A. Skarmeta, G. Routis, J. A. Lopez-Morales, E. Claffey, M. Mora, and J. A. Martinez, “Building an interoperable space for smart agriculture,” Digital Communications and Networks, vol. 9, no. 1, pp. 183–193, 2023. https://dx.doi.org/10.1016/j.dcan.2022.02.004
  11. D. Vasisht, Z. Kapetanovic, J. Won, X. Jin, R. Chandra, S. Sinha, A. Kapoor, M. Sudarshan, and S. Stratman, “FarmBeats: an IoT platform for Data-Driven agriculture,” in 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, 2017, pp. 515–529.
  12. S. Malvar, A. Badam, and R. Chandra, “FarmBeats: Digital Water for Agriculture,” Resource Magazine, vol. 29, no. 4, pp. 40–42, 2022.
  13. Z. Kapetanovic, D. Vasisht, J. Won, R. Chandra, and M. Kimball, “Experiences deploying an always-on farm network,” GetMobile: Mobile Computing and Communications, vol. 21, no. 2, pp. 16–21, 2017. https://dx.doi.org/10.1145/3131214.3131220
  14. D. Jaramillo, D. V. Nguyen, and R. Smart, “Leveraging microservices architecture by using Docker technology,” in SoutheastCon 2016. IEEE, 2016. https://dx.doi.org/10.1109/SECON.2016.7506647 pp. 1–5.
  15. ETSI Industry Specification Group (ISG), “Context Information Management (CIM); NGSI-LD API,” ETSI, Tech. Rep. RGS/CIM-009v161, 2019.
  16. R. Dautov and S. Distefano, “Distributed data fusion for the Internet of Things,” in Proceedings of 14th International Conference on Parallel Computing Technologies (PaCT 2017). Springer, 2017. https://dx.doi.org/10.1007/978-3-319-62932-2_41 pp. 427–432.
  17. R. Dautov and S. Distefano, “Three-level hierarchical data fusion through the IoT, edge, and cloud computing,” in Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017. https://dx.doi.org/10.1145/3109761.3158388 pp. 1–5.
  18. European Commission and Directorate-General for Communication, Data Act – The path to the digital decade. Publications Office of the European Union, 2022.
  19. D. Firmani, F. Leotta, J. G. Mathew, J. Rossi, L. Balzotti, H. Song, D. Roman, R. Dautov, E. J. Husom, S. Sen, V. Balionyte-Merle, A. Morichetta, S. Dustdar, T. Metsch, V. Frascolla, A. Khalid, G. Landi, J. Brenes, I. Toma, R. Szabó, C. Schaefer, C. Udroiu, A. Ulisses, V. Pietsch, S. Akselsen, A. Munch-Ellingsen, I. Pavlova, H.-G. Kim, C. Kim, B. Allen, S. Kim, and E. Paulson, “INTEND: Intent-Based Data Operation in the Computing Continuum,” in CEUR Workshop Proceedings, vol. 3692, 2024, pp. 43–50.