Skip to main content
SpringerLink
Log in

Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach

  • Conference paper
  • First Online:
Science and Global Challenges of the 21st Century - Science and Technology (Perm Forum 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 342))

Abstract

Recently, due to the active expansion of the Internet of Things (IoT) and Ubiquitous Computing, the neuro-augmented methods and tools for controlling software systems are on the rapid incline. But despite the existing understanding of the necessity of unified approaches for integration of neural interfaces into IoT ecosystems, those seem to be insufficiently developed. In most cases, the equipment is capable of working exclusively with a narrow range of software supplied by its manufacturer, which greatly hinders the integration process. In this paper, we propose the ontology-driven tools for the brain-computer interface integration into the IoT ecosystem. Unified high-level mechanism is provided that allows diverse software, services, and hardware to interconnect independently of particular IoT platforms. Visual editor is developed to design the integration process pipeline, describing desired devices and their behavior. Ontology-driven generator of corresponding firmware and middleware is created, which automates the software developers work. Some real-world applications based on the suggested approach are presented. Evaluation of the methods used is highlighted.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
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

Institutional subscriptions

References

  1. Calderon, M., Delgadillo, S., Garcia-Macias, A.: A more human-centric internet of things with temporal and spatial context. Proc. Comput. Sci. 83, 553–559 (2016). https://doi.org/10.1016/j.procs.2016.04.263

    Article  Google Scholar 

  2. Cimmino, A., et al.: VICINITY: IoT semantic interoperability based on the web of things. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 241–247 (2019). https://doi.org/10.1109/DCOSS.2019.00061

  3. Bröring, A., et al.: The BIG IoT API – semantically enabling IoT interoperability. IEEE Perv. Comput. 17(4), 41–51 (2018). https://doi.org/10.1109/MPRV.2018.2873566

    Article  Google Scholar 

  4. Ryabinin, K., Chuprina, S.: Ontology-driven edge computing. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12143, pp. 312–325. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50436-6_23

    Chapter  Google Scholar 

  5. Interoperability: The Secret to a Scalable IoT Network (2021). https://behrtech.com/blog/interoperability-the-secret-to-a-scalable-iot-network/. Accessed 31 May 2021

  6. Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel. Commun. Mobile Comput. 2017 (2017). https://doi.org/10.1155/2017/9731806

  7. Honti, G.M., Abonyi, J.: A review of semantic sensor technologies in internet of things architectures. Complexity 2019 (2019). https://doi.org/10.1155/2019/6473160

  8. Widell, N., Keränen, A., Badrinath, R.: What Is Semantic Interoperability in IoT and Why Is It Important? (2020), https://www.ericsson.com/en/blog/2020/7/semantic-interoperability-in-iot, last accessed 31 May 2021

  9. Agarwal, R., et al.: Unified IoT ontology to enable interoperability and federation of testbeds. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 70–75 (2016). https://doi.org/10.1109/WF-IoT.2016.7845470

  10. Jacoby, M., Antonić, A., Kreiner, K., Łapacz, R., Pielorz, J.: Semantic interoperability as key to IoT platform federation. In: Podnar Žarko, I., Broering, A., Soursos, S., Serrano, M. (eds.) InterOSS-IoT 2016. LNCS, vol. 10218, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56877-5_1

    Chapter  Google Scholar 

  11. Juárez, J., Rodríguez-Mondéjar, J.A., García-Castro, R.: An ontology-driven communication architecture for spontaneous interoperability in home automation systems. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), pp. 1–4 (2014). https://doi.org/10.1109/ETFA.2014.7005270

  12. El Kaed, C., Ponnouradjane, A., Shah, D.: A semantic based multi-platform IoT integration approach from sensors to Chatbots. In: 2018 Global Internet of Things Summit (GIoTS), pp. 1–6 (2018). https://doi.org/10.1109/GIOTS.2018.8534520

  13. Sahlmann, K., Schwotzer, T.: Ontology-based virtual IoT devices for edge computing. In: Proceedings of the 8th International Conference on the Internet of Things (2018). https://doi.org/10.1145/3277593.3277597

  14. Abdulrab, H., Babkin, E., Kozyrev, O.: Semantically enriched integration framework for ubiquitous computing environment. In: Babkin, E. (ed.) Ubiquitous Computing, pp. 177–196. IntechOpen (2011). https://doi.org/10.5772/15262

  15. Allison, B.: The I of BCIs: next generation interfaces for brain–computer interface systems that adapt to individual users. In: Human-Computer Interaction. Novel Interaction Methods and Techniques, pp. 558–568 (2009)

    Google Scholar 

  16. Huang, S., Tognoli, E.: Brainware: synergizing software systems and neural inputs. In: Companion Proceedings of the 36th International Conference on Software Engineering, pp. 444–447 (2014). https://doi.org/10.1145/2591062.2591131

  17. Camelo, G.A., Menezes, M.L., Sant’Anna, A.P., Vicari, R.M., Pereira, C.E.: Control of smart environments using brain computer interface based on genetic algorithm. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9622, pp. 773–781. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49390-8_75

    Chapter  Google Scholar 

  18. Quitadamo, L.R., Marciani, M.G., Cardarilli, G.C., Bianchi, L.: Describing different brain computer interface systems through a unique model: a UML implementation. Neuroinformatics 6(2), 81–96 (2008). https://doi.org/10.1007/s12021-008-9015-0

    Article  Google Scholar 

  19. Nishimura, E.M., Rapoport, E.D., Wubbels, P.M., Downs, T.H., Downs, J.H.: Functional Near-Infrared Sensing (fNIR) and Environmental Control Applications, pp. 121–132 (2010). https://doi.org/10.1007/978-1-84996-272-8_8

  20. Méndez, S.J.R., Zao, J.K.: BCI ontology: a context-based sense and actuation model for brain-computer interactions. In: 9th International Semantic Sensor Networks Workshop: 17th International Semantic Web Conference (2018)

    Google Scholar 

  21. José, S., Méndez, R.: Modeling actuations in BCI-O: a context-based integration of SOSA and IoT-O. In: Proceedings of the 8th International Conference on the Internet of Things, pp. 1–6 (2018). https://doi.org/10.1145/3277593.3277914

  22. Zao, J.K., et al.: Augmented brain computer interaction based on fog computing and linked data. In: 2014 International Conference on Intelligent Environments, pp. 374–377 (2014). https://doi.org/10.1109/IE.2014.54

  23. Ryabinin, K., Chuprina, S.: High-level toolset for comprehensive visual data analysis and model validation. Proc. Comput. Sci. 108, 2090–2099 (2017). https://doi.org/10.1016/j.procs.2017.05.050

    Article  Google Scholar 

  24. Ryabinin, K., Chuprina, S., Kolesnik, M.: Calibration and monitoring of IoT devices by means of embedded scientific visualization tools. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 655–668. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_52

    Chapter  Google Scholar 

  25. Ryabinin, K., Chuprina, S., Belousov, K.: Ontology-driven automation of IoT-based human-machine interfaces development. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11540, pp. 110–124. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22750-0_9

    Chapter  Google Scholar 

  26. Chuprina, S., Nasraoui, O.: Using ontology-based adaptable scientific visualization and cognitive graphics tools to transform traditional information systems into intelligent systems. Sci. Visual. 8(1), 23–44 (2016)

    Google Scholar 

  27. Abiri, R., Borhani, S., Sellers, E.W., Jiang, Y., Zhao, X.: A comprehensive review of EEG-based brain-computer interface paradigms. J. Neural. Eng. 16(1), 011001 (2019). https://doi.org/10.1088/1741-2552/aaf12e

    Article  Google Scholar 

  28. Janowicz, K., Compton, M.: The stimulus-sensor-observation ontology design pattern and its integration into the semantic sensor network ontology. In: Proceedings of the 3rd International Conference on Semantic Sensor Networks, vol. 668, pp. 64–78 (2010)

    Google Scholar 

  29. Saba-Sadiya, S., Alhanai, T., Liu, T., Ghassemi, M.M.: EEG channel interpolation using deep encoder-decoder networks. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 2432–2439 (2020). https://doi.org/10.1109/BIBM49941.2020.9312979

  30. Courellis, H.S., Iversen, J.R., Poizner, H., Cauwenberghs, G.: EEG channel interpolation using ellipsoid geodesic length. In: 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 540–543 (2016). https://doi.org/10.1109/BioCAS.2016.7833851

  31. Virtanen, P., et al.: SciPy 1.0: Fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2

    Article  Google Scholar 

  32. Gramfort, A., et al.: MEG and EEG data analysis with MNE-python. Front. Neurosci. 7, 1–13 (2013). https://doi.org/10.3389/fnins.2013.00267

    Article  Google Scholar 

  33. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  34. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25(1), 41–56 (2008). https://doi.org/10.1109/MSP.2008.4408441

    Article  Google Scholar 

  35. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b. Frontiers in Neuroscience 6 (2012). https://doi.org/10.3389/fnins.2012.00039

  36. Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58(2), 355–362 (2011). https://doi.org/10.1109/TBME.2010.2082539

    Article  Google Scholar 

  37. Pfurtscheller, G., et al.: Current trends in Graz brain-computer interface (BCI) research. IEEE Trans. Rehabil. Eng. 8(2), 216–219 (2000). https://doi.org/10.1109/86.847821

    Article  Google Scholar 

  38. Wu, S.L., Wu, C.W., Pal, N.R., Chen, C.Y., Chen, S.A., Lin, C.T.: Common spatial pattern and linear discriminant analysis for motor imagery classification. In: 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp. 146–151 (2013). https://doi.org/10.1109/CCMB.2013.6609178

  39. Kołodziej, M., Majkowski, A., Rak, R.: Linear discriminant analysis as EEG features reduction technique for brain-computer interfaces. Przeglad Elektrotechniczny, pp. 28–30 (2012)

    Google Scholar 

  40. Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., Wolpaw, J.: BCI2000: a General-Purpose Brain-Computer Interface (BCI) System. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004). https://doi.org/10.1109/TBME.2004.827072

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Perm State University, Bukireva Street 15, 614990, Perm, Russia

    Konstantin Ryabinin, Svetlana Chuprina & Ivan Labutin

Authors
  1. Konstantin Ryabinin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Svetlana Chuprina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivan Labutin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Alvaro Rocha

  2. Perm State University, Perm, Russia

    Ekaterina Isaeva

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

Ryabinin, K., Chuprina, S., Labutin, I. (2022). Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach. In: Rocha, A., Isaeva, E. (eds) Science and Global Challenges of the 21st Century - Science and Technology. Perm Forum 2021. Lecture Notes in Networks and Systems, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-89477-1_9

Download citation

Publish with us

Policies and ethics

Search

Navigation