Skip to main content

Adaptive Data-Driven Routing for Edge-to-Cloud Continuum: A Content-Based Publish/Subscribe Approach

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13533)


The concept of Edge-to-Cloud Continuum aims to significantly reduce overall traffic to the cloud by enabling IoT data processing as close as possible to the data sources, either on near- or far-edge devices. In this highly dynamic environment, where IoT devices and edge nodes are constantly changing their state and location, services running on edge nodes have to be scheduled, deployed and managed to ensure high service availability with appropriate Quality of Service (QoS) parameters. However, once services are deployed in the edge-to-cloud continuum, the question arises how to ensure continuous data delivery from IoT devices to the appropriate services for further processing, either on edge devices or in the cloud. In this paper, we propose a general architecture for adaptive data-driven routing in the edge-to-cloud continuum and introduce an implementation of this architecture using the content-based publish/subscribe approach. We evaluate the given implementation against a real-world use case scenario for federated learning in an edge-to-cloud environment hosting digital twins. The performance evaluation of this scenario shows that our implementation efficiently adapts to service failures and reconfigures the edge-to-cloud environment with minimal latency and without data loss, while preserving data privacy and security. In addition, the experiments show that our solution is stable in an environment with IoT data sources generating data at high frequency.


  • Internet of Things
  • Edge computing
  • Data streaming
  • Publish/subscribe
  • Federated learning
  • Digital twin

This work has been supported by Croatian Science Foundation under the project IP-2019–04-1986.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Antonić, A., Marjanović, M., Pripužić, K., Podnar Žarko, I.: A mobile crowd sensing ecosystem enabled by CUPUS: cloud-based publish/subscribe middleware for the Internet of Things. Futur. Gener. Comput. Syst. 56, 607–622 (2016)

    CrossRef  Google Scholar 

  2. Arulraj, J., Chatterjee, A., Daglis, A., Dhekne, A., Ramachandran, U.: eCloud: a vision for the evolution of the edge-cloud continuum. Computer 54(5), 24–33 (2021)

    CrossRef  Google Scholar 

  3. Bormann, C., Ersue, M., Keränen, A.: Terminology for Constrained-Node Networks. RFC 7228 (2014)

    Google Scholar 

  4. Cloud native computing foundation: K3s.

  5. Giouroukis, D., Jestram, J., Zeuch, S., Markl, V.: Streaming data through the IoT via actor-based semantic routing trees. Open J. Internet Things 7(1), 59–70 (2021)

    Google Scholar 

  6. Gupta, A.K., Sahin, O.D., Agrawal, D., Abbadi, A.E.: Meghdoot: content-based publish/subscribe over P2P networks. In: Middleware, pp. 254–273 (2004)

    Google Scholar 

  7. Gupta, D., Kayode, O., Bhatt, S., Gupta, M., Tosun, A.S.: Hierarchical federated learning based anomaly detection using digital twins for smart healthcare (2021)

    Google Scholar 

  8. Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Trans. Cloud Comput. 1–1 (2021)

    Google Scholar 

  9. Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency (2016)

    Google Scholar 

  10. Krivic, P., Kusek, M., Cavrak, I., Skocir, P.: Dynamic scheduling of contextually categorised Internet of Things services in fog computing environment. Sensors 22(2), 465 (2022)

    Google Scholar 

  11. Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)

    CrossRef  Google Scholar 

  12. Openfog Consortium: OpenFog Reference Architecture for Fog Computing (2017)

    Google Scholar 

  13. Pham, V.N., Nguyen, V., Nguyen Tri, T., Huh, E.N.: Efficient edge-cloud publish/subscribe broker overlay networks to support latency-sensitive wide-scale IoT applications. Symmetry 12(1), 3 (2019)

    Google Scholar 

  14. Podnar Žarko, I., Antonić, A., Marjanović, M., Pripužić, K., Skorin-Kapov, L.: The OpenIoT approach to sensor mobility with quality-driven data acquisition management. In: Podnar Žarko, I., Pripužić, K., Serrano, M. (eds.) Interoperability and Open-Source Solutions for the Internet of Things, pp. 46–61. Springer International Publishing, Cham (2015).

  15. Salaht, F.A., Desprez, F., Lebre, A.: An overview of service placement problem in fog and edge computing. ACM Comput. Surv. 53(3), 1–35 (2020)

    Google Scholar 

  16. Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Resource provisioning in fog computing: From theory to practice \(\dagger \). Sensors 19(10), 2238 (2019)

    Google Scholar 

  17. University of Zagreb: IMUNES.

  18. Zhou, Z., Yang, S., Pu, L., Yu, S.: CEFL: online admission control, data scheduling, and accuracy tuning for cost-efficient federated learning across edge nodes. IEEE Internet Things J. 7(10), 9341–9356 (2020)

    CrossRef  Google Scholar 

  19. Čilić, I., Podnar Žarko, I., Kušek, M.: Towards service orchestration for the cloud-to-thing continuum. In: 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 01–07 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Ivan Čilić or Ivana Podnar Žarko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Čilić, I., Žarko, I.P. (2022). Adaptive Data-Driven Routing for Edge-to-Cloud Continuum: A Content-Based Publish/Subscribe Approach. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20935-2

  • Online ISBN: 978-3-031-20936-9

  • eBook Packages: Computer ScienceComputer Science (R0)