Skip to main content
SpringerLink
Log in

Adaptive configuration of IoT applications in the fog infrastructure

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) applications have invaded several domains (supply chain, healthcare, etc.). To enhance the quality of the provided service in terms of latency, response time, etc., service providers such as Amazon, Google, and Microsoft turned to running IoT tasks near the end user by invoking the fog computing concept. Fog computing extends cloud services to the edge of the network. It provides a variety of computing resources in the form of fog nodes, which offer multiple services known as fog services. These latter are used to store and process the data generated by IoT devices. Fog services are characterized by their high reusability. It enables the construction of a composite service to provide complicated IoT tasks. In this paper, we introduce an adaptive requirements-aware approach for configuring IoT applications in fog computing. The configuration is based on a Composite Fog Service (CFS) model and is restricted by a set of constraints. The proposed approach is implemented in a Smart Car Parking (SCP) scenario. Simulation results reveal the effectiveness of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Available online: http://sourceforge.net/projects/java-galib/.

References

  1. Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Bessis N, Dobre C (eds) Big data and internet of things: a roadmap for smart environments. studies in computational intelligence, vol 546. Springer, Cham, pp 169–186

    Google Scholar 

  2. Chen N, Clarke S, Chen S (2020) Fog-based service enablement architecture. In: Yang Y, Huang J, Zhang T, Weinman J (eds) Challenges and practices of fog computing, communication, networking, strategy, and economics, fog and fogonomics. Wiley, Hoboken, pp 151–177

    Chapter  Google Scholar 

  3. Awaisi KS et al (2019) Towards a fog enabled efficient car parking architecture. IEEE Access 7:159100–159111

    Article  Google Scholar 

  4. Singh M et al (2020) QoS-aware selection of IoT-based service. Arab J Sci Eng 45(12):10033–10050

    Article  Google Scholar 

  5. Pittalà, G. F, et al. (2022) Function-as-a-service orchestration in fog computing environments. In: 2022 18th international conference on network and service management (CNSM). IEEE

  6. Ettazi, W, et al. (2023) Towards a cognitive engineering of transactional services in IoT based systems. J Syst Softw 200: 111634

  7. Brahmi Z, Selmi A (2022) Coordinate system-based trust-aware web services composition in edge and cloud environment. Comput J. https://doi.org/10.1093/comjnl/bxad061

    Article  Google Scholar 

  8. Aoudia I, Benharzallah S, Kahloul L, Kazar O (2021) A multi-population genetic algorithm for adaptive QoS-aware service composition in fog-IoT healthcare environment. Int Arab J Inf Technol 18:464–475

    Google Scholar 

  9. Aoudia I et al. (2020) QoS-aware service composition in Fog-IoT computing using multi-population genetic algorithm. In: 21st international Arab conference on information technology, IEEE, 1-9

  10. Barakat L et al (2018) Adaptive composition in dynamic service environments. Futur Gener Comput Syst 80:215–228

    Article  Google Scholar 

  11. Kouicem A, Chibani A, Tari A, Amirat Y, Tari Z (2014) Dynamic services selection approach for the composition of complex services in the web of objects. In: IEEE world forum on internet of things, IEEE, 298–303

  12. Tari K, et al (2010) Context-aware dynamic service composition in ubiquitous environment. In: IEEE international conference on communications

  13. D’Angelo M et al (2020) Decentralized learning for self-adaptive QoS-aware service assembly. Futur Gener Comput Syst 108:210–227

    Article  Google Scholar 

  14. Akintoye SB et al (2019) Improving quality-of-service in cloud/fog computing through efficient resource allocation. Sensors 19(6):1267

    Article  Google Scholar 

  15. Chouat H, Abbassi I, Graiet M (2021) A genetic-based requirements-aware approach for reliable IoT applications in the Fog. In: IEEE 30th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), 39-44

  16. Mokni M, et al, (2022) Cooperative agents-based approach for workflow scheduling on fog-cloud computing. J Ambient Intell Humaniz Comput 13(10):4719–4738

  17. Thangaraj P, Balasubramanie P (2021) Meta heuristic QoS based service composition for service computing. J Ambient Intell Humaniz Comput 12(5):5619–5625

    Article  Google Scholar 

  18. Huang J et al (2020) A simulation-based optimization approach for reliability-aware service composition in edge computing. IEEE Access 8:50355–50366

    Article  Google Scholar 

  19. Zhang T et al (2019) Rate-adaptive fog service platform for heterogeneous IoT applications. IEEE Internet Things J 7(1):176–188

    Article  Google Scholar 

  20. Donassolo B et al (2021) Online reconfiguration of IoT applications in the fog: the information-coordination trade-off. IEEE Trans Parallel Distrib Syst 33(5):1156–1172

    Article  Google Scholar 

  21. Gupta H et al (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things Edge and Fog computing environments. Softw Pract Exp 47(9):1275–1296

    Article  Google Scholar 

  22. Bhiri S et al (2011) Ensuring customised transactional reliability of composite services. J Database Manag 22(2):64–92

    Article  Google Scholar 

  23. Razian M, Fathian M, Bahsoon R, Toosi AN, Buyya R (2022) Service composition in dynamic environments: a systematic review and future directions. J Syst Softw 188:111290

    Article  Google Scholar 

  24. Eyckerman R et al (2020) Requirements for distributed task placement in the fog. Internet of Things 12:100237

    Article  Google Scholar 

  25. Chen L et al (2018) Adaptive fog configuration for the industrial internet of things. IEEE Trans Industr Inf 14(10):4656–4664

    Article  Google Scholar 

  26. Asghari P et al (2018) Service composition approaches in IoT: a systematic review. J Netw Comput Appl 120:61–77

    Article  Google Scholar 

  27. El Hadad J, Manouvrier M, Rukoz M (2010) TQoS: transactional and QoS-aware selection algorithm for automatic web service composition. IEEE Trans Serv Comput 3(1):73–85

    Article  Google Scholar 

  28. Smolka S, Mann ZÁ (2022) Evaluation of fog application placement algorithms: a survey. Computing 104(6):1397–1423

    Article  Google Scholar 

  29. Stigler M (2018) Understanding serverless computing. In: Stigler M (ed) Beginning serverless computing: developing with Amazon web services, Microsoft Azure, and Google Cloud. Apress Press, Berkeley, CA, pp 1–14

    Chapter  Google Scholar 

  30. Cheng B et al (2019) Fog function: Serverless fog computing for data intensive iot services. In: IEEE international conference on services computing

  31. Cicconetti C, Conti M, Passarella A (2020) A decentralized framework for serverless edge computing in the internet of things. IEEE Trans Netw Serv Manag 18(2):2166–2180

    Article  Google Scholar 

  32. Al-Masri E, Mahmoud QH (2008) Investigating web services on the world wide web. In: Proceedings of the 17th international conference on World Wide Web. 795-804

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. University of Sousse, ISITCom, Hammam Sousse, 4011, Sousse, Tunisia

    Houda Chouat

  2. University of Tunis El Manar, LR-OASIS, Tunis, Tunisia

    Imed Abbassi

  3. STACK Team, IMT Atlantique, Inria, LS2N, Nantes, France

    Mohamed Graiet & Mario Südholt

Authors
  1. Houda Chouat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Imed Abbassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Graiet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Südholt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houda Chouat.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chouat, H., Abbassi, I., Graiet, M. et al. Adaptive configuration of IoT applications in the fog infrastructure. Computing 105, 2747–2772 (2023). https://doi.org/10.1007/s00607-023-01191-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-023-01191-9

Keywords

Mathematics Subject Classification

Search

Navigation