Skip to main content
SpringerLink
Log in

Inter-space Machine Learning in Smart Environments

  • Conference paper
  • First Online:
Machine Learning and Knowledge Extraction (CD-MAKE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12279))

Abstract

Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Alavi, M., Leidner, D.E.: Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 25, 107–136 (2001)

    Article  Google Scholar 

  2. Anjomshoaa, A.: Blending building information with smart city data. In: S4SC@ ISWC, pp. 1–2. Citeseer (2014)

    Google Scholar 

  3. Anjomshoaa, A., Shayeganfar, F., Mahdavi, A., Tjoa, A.: Toward constructive evidence of linked open data in AEC domain. In: Proceedings of the 10th European Conference on Product and Process Modelling (ECPPM2014), Vienna, Austria, 17–19 September 2014, pp. 535–542 (2014)

    Google Scholar 

  4. Arief-Ang, I.B., Hamilton, M., Salim, F.D.: A scalable room occupancy prediction with transferable time series decomposition of co2 sensor data. ACM Trans. Sens. Netw. (TOSN) 14(3–4), 1–28 (2018)

    Google Scholar 

  5. Balaji, B., et al.: Brick: towards a unified metadata schema for buildings. In: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 41–50. ACM (2016)

    Google Scholar 

  6. Bonatti, P.A., Decker, S., Polleres, A., Presutti, V.: Knowledge graphs: new directions for knowledge representation on the semantic web (dagstuhl seminar 18371) (2019)

    Google Scholar 

  7. Curry, E.: Real-Time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29665-0

    Book  Google Scholar 

  8. Curry, E., O’Donnell, J., Corry, E., Hasan, S., Keane, M., O’Riain, S.: Linking building data in the cloud: integrating cross-domain building data using linked data. Adv. Eng. Inform. 27(2), 206–219 (2013)

    Article  Google Scholar 

  9. Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Comput. 22(4), 9889–9904 (2019)

    Article  Google Scholar 

  10. Handzic, M.: Knowledge management: a research framework. In: Proceedings of the European Conference on Knowledge Management, pp. 219–229 (2001)

    Google Scholar 

  11. Hélie, S., Sun, R.: Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychol. Rev. 117(3), 994 (2010)

    Article  Google Scholar 

  12. Hogan, A., et al.: Knowledge graphs (2020)

    Google Scholar 

  13. Holzinger, A.: From machine learning to explainable AI. In: 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), pp. 55–66. IEEE (2018)

    Google Scholar 

  14. Ishii, H., Ullmer, B.: Tangible bits: towards seamless interfaces between people, bits and atoms. In: Proceedings of the ACM SIGCHI Conference on Human factors in computing systems, pp. 234–241 (1997)

    Google Scholar 

  15. Kleinbaum, D.G., Dietz, K., Gail, M., Klein, M., Klein, M.: Logistic Regression. A Self-Learning Text. Springer, New York (2002). https://doi.org/10.1007/b97379

    Book  MATH  Google Scholar 

  16. Publio, G.C., et al.: Ml-schema: exposing the semantics of machine learning with schemas and ontologies (2018)

    Google Scholar 

  17. Sangogboye, F.C., Arendt, K., Singh, A., Veje, C.T., Kjærgaard, M.B., Jørgensen, B.N.: Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control. Build. Simul. 10, 829–843 (2017)

    Article  Google Scholar 

  18. Schwee, J.H., et al.: Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building. Sci. Data 6(1), 1–11 (2019)

    Article  Google Scholar 

  19. Singh, D., et al.: Human activity recognition using recurrent neural networks. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2017. LNCS, vol. 10410, pp. 267–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66808-6_18

    Chapter  Google Scholar 

  20. Törmä, S.: Semantic linking of building information models. In: 2013 IEEE Seventh International Conference on Semantic Computing, pp. 412–419. IEEE (2013)

    Google Scholar 

  21. Wang, W., Chen, J., Hong, T.: Occupancy prediction through machine learning and data fusion of environmental sensing and wi-fi sensing in buildings. Autom. Constr. 94, 233–243 (2018)

    Article  Google Scholar 

  22. Wikipedia: Buildingsmart, industry foundation classes (IFC). https://en.wikipedia.org/wiki/Industry_Foundation_Classes. Accessed 14 Apr 2020

  23. Xing, T., Sandha, S.S., Balaji, B., Chakraborty, S., Srivastava, M.: Enabling edge devices that learn from each other: cross modal training for activity recognition. In: Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking, pp. 37–42 (2018)

    Google Scholar 

  24. Yadav, P., Curry, E.: VidCEP: complex event processing framework to detect spatiotemporal patterns in video streams. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2513–2522. IEEE (2019)

    Google Scholar 

Download references

Acknowledgement

This work was supported with the financial support of the Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie).

Author information

Authors and Affiliations

  1. Lero – the Irish Software Research Centre, National University of Ireland, Galway, Ireland

    Amin Anjomshoaa & Edward Curry

Authors
  1. Amin Anjomshoaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edward Curry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Anjomshoaa .

Editor information

Editors and Affiliations

  1. Human-Centered AI Lab, Institute for Medical Informatics, Statistics and Doumentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. UAS St. Pölten, St. Pölten, Austria

    Peter Kieseberg

  3. Institute of Software Technology and Interactive Systems, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  4. SBA Research, Vienna, Austria

    Edgar Weippl

Rights and permissions

Reprints and permissions

Copyright information

© 2020 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anjomshoaa, A., Curry, E. (2020). Inter-space Machine Learning in Smart Environments. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57321-8_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57320-1

  • Online ISBN: 978-3-030-57321-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation