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Semantic Smart Homes: Towards a Knowledge-Rich Smart Environment

  • Liming ChenEmail author
  • Chris D. Nugent
Chapter

Abstract

This chapter introduces semantic smart homes—a novel concept whose aim is to move from the current state of the art of smart home technologies to the future infrastructure that is needed to support the full richness of the smart home vision in which there are adaptive, personalised and context-aware assistance capabilities. It describes the rationale behind the conception and presents a conceptual system architecture for semantic smart homes. It then elaborates functions and their interplay of constituent components with specific emphasis being placed on the methodology of semantic modeling, content generation and management. The chapter also discusses the semantic-enabled processing capabilities and the potentials of the semantic smart homes metaphor through a number of use scenarios.

References

  1. 1.
    Rochester University: University Creates Medical "Smart Home" To Study Future Health Technology - Newsroom - University of Rochester Medical Center. https://www.urmc.rochester.edu/news/story/-103/university-creates-medical-smart-home-to-study-future-health-technology.aspx
  2. 2.
    Nugent CD, Mulvenna MD, Hong X, Devlin S (2009) Experiences in the development of a smart lab. Int J Biomed Eng Technol 2:319–331CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Georgia Institute of Technology: Aware Home Research Initiative. http://www.awarehome.gatech.edu/
  5. 5.
    Espinilla M, Martinez L, Medina J, Nugent C (2018) The experience of developing the UJAmI Smart Lab. IEEE Access 6:34631–34642CrossRefGoogle Scholar
  6. 6.
    OSGi Alliance: OSGiTM Alliance – The Dynamic Module System for Java. https://www.osgi.org/
  7. 7.
  8. 8.
    Salguero AG (2018) Using ontologies for the online recognition of activities of daily living. Sensors (Basel) 18:1–22CrossRefGoogle Scholar
  9. 9.
    Bibi S, Anjum N, Sher M (2018) Automated multi-feature human interaction recognition in complex environment. Comput Ind 99:282–293CrossRefGoogle Scholar
  10. 10.
    Almeida A, Azkune G (2018) Predicting human behaviour with recurrent neural networks. Appl Sci 8:305CrossRefGoogle Scholar
  11. 11.
    Davies J, Studer R, Warren P (2006) Semantic Web technologies: trends and research in ontology-based systems. Wiley, New YorkGoogle Scholar
  12. 12.
    Pollack ME, Pollack ME (2005) Intelligent technology for an aging population: the use of AI to assist elders with cognitive impairment. AI Mag 26(2):9Google Scholar
  13. 13.
    Noor MHM, Salcic Z, Wang KIK (2018) Ontology-based sensor fusion activity recognition. J Ambient Intell Humaniz Comput 1–15 (2018)Google Scholar
  14. 14.
    Meditskos G, Kompatsiaris I (2017) iKnow: ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob Comput 40:17–41CrossRefGoogle Scholar
  15. 15.
    Latfi F, Lefebvre B, Descheneaux C (2007) Ontology-based management of the telehealth smart home, dedicated to elderly in loss of cognitive autonomy. In: CEUR workshop proceedingsGoogle Scholar
  16. 16.
    Klein M, Schmidt A, Lauer R (2007) Ontology-centred design of an ambient middleware for assisted living: the case of SOPRANO. In: Towards ambient intelligence: methods for cooperating ensembles in ubiquitous environments (AIM-CU), 30th annual German conference on artificial intelligenceGoogle Scholar
  17. 17.
    Roussaki I, Strimpakou M, Pils C, Kalatzis N, Anagnostou M (2006) Hybrid context modeling: a location-based scheme using ontologies. In: Proceedings - fourth annual IEEE international conference on pervasive computing and communications workshops, PerCom workshops 2006Google Scholar
  18. 18.
    European Commission - CORDIS: ASK-IT - Ambient intelligence system of agents for knowledge-based and integrated services for mobility impaired users. https://cordis.europa.eu/project/rcn/72134/factsheet/en
  19. 19.
    European Commission - CORDIS: Final Report Summary - SAPPHIRE (System Automation of PEMFCs with Prognostics and Health management for Improved Reliability and Economy). https://cordis.europa.eu/project/rcn/108481/reporting/en
  20. 20.
    W3C: Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language. https://www.w3.org/TR/wsdl20/
  21. 21.
    Johnston WE (2004) Semantic services for grid-based, large-scale science. IEEE Intell Syst 19:34–39CrossRefGoogle Scholar
  22. 22.
    The time ontology in OWL. https://www.w3.org/TR/owl-time/
  23. 23.
    Nugent C, Finlay D, Davies R (2007) The next generation of mobile medication management solutions. Int J Electron Healthc 3(1):7–31CrossRefGoogle Scholar
  24. 24.
    Philipose M, Fishkin KP, Perkowitz M, Patterson DJ, Fox D, Kautz H, Hähnel D (2004) Inferring activities from interactions with objects. IEEE Pervasive Comput 1(4):50–57CrossRefGoogle Scholar
  25. 25.
    Dentler K, Cornet R, Ten Teije A, De Keizer N (2011) Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semant Web 2:71–87Google Scholar
  26. 26.
    Harris S, Gibbins N (2003) 3store: efficient bulk RDF storage. In: Proceedings of the 1st international workshop on practical and scalable semantic systems (PSSS’03)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsDe Montfort UniversityLeicesterUK
  2. 2.School of ComputingUlster UniversityBelfastUK

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