Skip to main content
SpringerLink
Log in

AI-based model driven approach for adaptive wireless sensor networks design

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

The development of IoT systems based WSN denotes a significant issue on providing intelligent capabilities to verify nodes behaviors and battery constraints. Existing AI-based works have been recently emerged for the analysis of dynamic WSN systems. Unfortunately, they failed to capture the design of dynamic intelligent WSN requirements at a high abstraction level. They provide AI solutions which are related to the target system and focus on specific problems without supporting reusability and interoperability. The Model Driven Engineering (MDE) and in particular the UML/MARTE profile become promising solutions for high-level abstraction to ease the design of WSN. We propose an AI-based model driven approach for the analysis and the prediction of WSN nodes behaviors and its interaction. It starts with a high-level specification based on the UML/MARTE profile, which describes the adaptation of WSN nodes and their interaction. Then, Model-to-Text (M2T) transformations are used to generate simulation scripts for analysis of WSN on a target AI-based platform. This later focuses on the prediction of WSN nodes behaviors, network clusters interaction and analysis of battery constraints. The prediction is based on training dataset which are collected from the German Weather Service (DWD) and measured within Measurement and Sensor Technology (MST) professorship, in the Technology University of Chemnitz.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. El HD, Sabrine K, Waseem MM, Faouzi D, Kamel B, Olfa Kanoun (2020) A secure and efficient login and data exchange scheme for an iot laboratory management system. IEEE World Forum on Internet of Things (WF-IoT). IEEE

    Google Scholar 

  2. Sonia B, Ghada B, Slim N, Olfa K (2020) Electromagnetic energy harvester for battery-free iot solutions. IEEE World Forum on Internet of Things (WF-IoT). IEEE

    Google Scholar 

  3. Ghosh A, Chakraborty D, Law A (2015) Artificial intelligence in internet of things. IET Res J 3:208–218

  4. Zander S, Merkle N, Frank M (2016) Enhancing the utilization of iot devices using ontological semantics and reasoning. Procedia Computer Sci 98:87–90

    Article  Google Scholar 

  5. Yahyaouy A, Boumhidi J, El Bourakadi D, Ramadan H (2022) A novel solar power prediction model based on stacked bilstm deep learning and improved extreme learning machine. Int J Inform Technol 15:587–594

  6. Schmidt Douglas C (2006) Model-driven engineering. IEEE Comput 39(2):25

    Article  Google Scholar 

  7. Federico C, Ivica C, Di RD, Ivano M, Patrizio P, Romina S (2017) Model-driven engineering for mission-critical iot systems. IEEE Softw 34(1):46–53

    Article  Google Scholar 

  8. OMG Object Management Group (2011) A UML profile for MARTE: modeling and analysis of real-time embedded systems, ptc/2011-06-02. Object Management Group

  9. Fredj N, Khriji S, Kanoun O, Yessine HK, Mohamed A (2021) A review on intelligent iot systems design methodologies. XXIII World congress of Measurement and Confederations, IMEKO

  10. Fredj N, Kanoun O, Kacem Yessine H, Mohamed H (2021) Component ensemble-based uml/marte extensions for the design of dynamic cyber-physical systems. International Conference on Software Technologies, ICSOFT, 2021

  11. Raoudha S, Yessine Hadj K, BenSaleh MS, Mohamed A (2017) Network reconfiguration for wireless sensor networks using uml/marte profile. In Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,, pages 203–209. INSTICC, SciTePress

  12. Raoudha S, Hadj KY, BenSaleh MS, Mohamed A (2016) A uml/marte extension for designing energy harvesting in wireless sensor networks. Intelligent interactive multimedia systems and services 2016. Springer

    Google Scholar 

  13. Al-Omary M (2019) Accuracy improvement of predictive neural networks for managing energy in solar powered wireless sensor nodes. Chemnitz University of Technology, Germany

    Google Scholar 

  14. Shahid Thekiya Mohammed, Dindayal Nikose Mangesh (2022) Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for wireless sensor network. International Journal of Information Technology. 14:2815–2824

    Article  Google Scholar 

  15. Arif M, Sadiq M, Mohammad CW (2022) Uml and nfr-framework based method for the analysis of the requirements of an information system. Int J Inform Technol 15:411–422

  16. Kiran MV, Nithya B (2022) Stable and energy-efficient next-hop router selection (SE-NRS) for wireless body area networks. International Journal of Information Technology

  17. Sagun S, Sangil L, Jaehee L (2018) A new leach algorithm for the data aggregation to improve the energy efficiency in WSN. Int J Internet Broadcasting Commun 10(2):68–73

    Google Scholar 

  18. Chang JY, Ju PH (2013) An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks. Future Gener Comput Syst 35:128–140

    Article  MathSciNet  Google Scholar 

  19. Ahmed G, Khan NM, Khalid Z (2008) Cluster head selection using decision trees for wireless sensor networks. International conference on intelligent sensors, sensor networks and information processing ISSNIP

  20. Khriji S, Houssaini DE, Kammoun I, Kanoun O (2018) A fuzzy based energy aware unequal clustering for wireless sensor networks. In International conference on Ad-Hoc networks and wireless, pages 126–131. Springer

  21. Salem JB, Khriji S, Baklouti M, Kammoun I, Kanoun O (2019) Testbed implementation of a fuzzy based energy efficient clustering algorithm for wireless sensor networks. 2019 16th International multi-conference on systems, signals and devices (SSD). IEEE, pp 351–356

    Chapter  Google Scholar 

  22. da Rocha AR, dos Santos IL, Pirmez L, Delicato FC, Gomes DG, de Souza JN (2010) Semantic clustering in wireless sensor networks. In: Pont A, Pujolle G, Raghavan SV (eds) WCITD/NF. IFIP international federation for information processing, vol. 327. Springer, Heidelberg, pp 3–14. https://doi.org/10.1007/978-3-642-15476-8

  23. Lee JS, Cheng WL (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensor J 12(9):2891–2897

    Article  Google Scholar 

  24. Azad P, Sharma V (2013) Cluster head selection in wireless sensor networks under fuzzy environment. ISRN Sensor Networks

  25. Li W, Yue W, Li P, Yi Ping, Li J (2014) A new intrusion detection system based on knn classification algorithm in wireless sensor network. Hindawi Publishing Corporation J Electr Comput Eng 5:1–8

  26. Li XH, Fang KL, Zhang L, He J (2009) A clustering algorithm based on k-means for wireless indoor monitoring system. International conference on information technology and computer science

  27. Ahmed MM, Taha A, Hassanien AE, Hassanien E (2018) An optimized k-nearest neighbor algorithm for extending wireless sensor network lifetime. Springer International Publishing, New York

    Book  Google Scholar 

  28. Jafarizadeh V, Keshavarzi A, Derikvand T (2016) Efficient cluster head selection using naive bayes classifier for wireless sensor networks. Wireless Netw 23:779–785

    Article  Google Scholar 

  29. Ren Q, Yao G (2020) An energy-efficient cluster head selection scheme for energy-harvesting wireless sensor networks. Sensors 20:187

    Article  Google Scholar 

  30. BenSaleh MS, Saida R, Kacem YH, Abid M (2020) Wireless sensor network design methodologies: a survey. Journal Sens 2020:1–13

    Article  Google Scholar 

  31. Fredj N, Kacem YH, Abid M (2020) Runtime model-based framework for specifying and verifying adaptive real time systems. Int J Coput Appl Technol 63:309–326

    Article  Google Scholar 

  32. Fredj N, Kacem YH, Abid M (2020) An event-based approach for formally verifying runtime adaptive real-time systems. Int J Supercomput 76(6):3110–3143

    Google Scholar 

  33. Fredj N, Kacem YH, Abid M (2018) Runtime uml marte extensions for the design of adaptive rte systems. International Conference on Intelligent Systems Design and Applications (ISDA)Vellore India. Springer, Heidelberg

  34. Said MB, Kacem YH, Amor NB, Abid M (2013) High level design of adaptive real-time embedded systems A survey. pages 341–350,

  35. Krichen F, Hamid B, Zalila B, Jmaiel M, Coulette B (2015) Development of reconfigurable distributed embedded systems with a model driven approach. Concurr Computation 27(6):1391–1411

    Article  Google Scholar 

  36. Corsaro A, Schmidt DC, Klefstad R, ORyan C (2002) Virtual component - a design pattern for memory-constrained embedded applications. In In proceedings of the ninth conference on pattern language of programs (PLoP

  37. Iglesia DGDL, Weyns D (2015) Mape-k formal templates to rigorously design behaviors for self adaptive systems. ACM Transac Auton Adapt Syst 10(3):1–15

    Article  Google Scholar 

  38. Saida R, Kacem YH, BenSaleh MS, Abid M (2018) A model-based transformation framework for designing and analyzing wireless sensor networks. Turkish J Electr Eng Comput Sci 26:3274–3286

    Google Scholar 

  39. Bouyssounouse B, Sifakis J (2005) Embedded systems design: the artist roadmap for research and development. Lecture Notes in Computer Science, vol.3436

  40. Crnkovic I, Sentilles S, Vulgarakis A, Chaudron MR (2011) A classification framework for software component models. IEEE Transac Softw Eng 37(5):593–615

    Article  Google Scholar 

  41. Bures T, Nicola De R, Gerostathopoulos I, Hoch N, Kit M, Koch, Giacom VM, Montanari U, Pugliese R, Serbedzija N, Wirsing M, Zambonelli F (2013) A life cycle for the development of autonomic systems: The e mobility showcase. 2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops, pages 359–381

  42. Shahzad F (2016) Pymote 2.0: Development of an interactive python framework for wireless network simulations. IEEE Internet Things J 3(6):1182–1188

  43. https://www.dwd.de/DE/klimaumwelt/cdc/cdc

Download references

Funding

None.

Author information

Author notes

  1. Yessine Hadj Kacem, Sabrine Khriji, Olfa Kanoun, Slim Hamdi and Mohamed Abid contributed equally to this work.

Authors and Affiliations

  1. Measurement and Sensor Technology, Technische Universität Chemnitz, Chemnitz, Germany

    Nissaf Fredj, Sabrine Khriji & Olfa Kanoun

  2. CES Laboratory, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia

    Nissaf Fredj, Yessine Hadj Kacem, Slim Hamdi & Mohamed Abid

  3. ICD-LM2S, CNRS, University of Technology, Troyes, France

    Slim Hamdi

Authors
  1. Nissaf Fredj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yessine Hadj Kacem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabrine Khriji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olfa Kanoun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Slim Hamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed Abid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yessine Hadj Kacem.

Ethics declarations

Conflict of interest

None.

Data availability

This research used a dataset available online and the URL is: https://www.dwd.de/DE/klimaumwelt/cdc/cdc.html

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

Fredj, N., Hadj Kacem, Y., Khriji, S. et al. AI-based model driven approach for adaptive wireless sensor networks design. Int. j. inf. tecnol. 15, 1871–1883 (2023). https://doi.org/10.1007/s41870-023-01208-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01208-8

Keywords

Search

Navigation