Toward Web Enhanced Building Automation Systems

  • Gérôme Bovet
  • Antonio Ridi
  • Jean HennebertEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 546)


The emerging concept of Smart Building relies on an intensive use of sensors and actuators and therefore appears, at first glance, to be a domain of predilection for the IoT. However, technology providers of building automation systems have been functioning, for a long time, with dedicated networks, communication protocols and APIs. Eventually, a mix of different technologies can even be present in a given building. IoT principles are now appearing in buildings as a way to simplify and standardise application development. Nevertheless, many issues remain due to this heterogeneity between existing installations and native IP devices that induces complexity and maintenance efforts of building management systems. A key success factor for the IoT adoption in Smart Buildings is to provide a loosely-coupled Web protocol stack allowing interoperation between all devices present in a building. We review in this chapter different strategies that are going in this direction. More specifically, we emphasise on several aspects issued from pervasive and ubiquitous computing like service discovery. Finally, making the assumption of seamless access to sensor data through IoT paradigms, we provide an overview of some of the most exciting enabling applications that rely on intelligent data analysis and machine learning for energy saving in buildings.


Machine Learning Technique Application Layer Adaptation Level Service Discovery SPARQL Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Perez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40, 394–398 (2008)CrossRefGoogle Scholar
  2. 2.
    Knx association.
  3. 3.
  4. 4.
    Granzer, W., Kastner, W., Reinisch, C.: Gateway-free integration of bacnet and knx using multi-protocol devices. In: Proceedings of the 6th IEEE International Conference on Industrial Informatics (INDIN ’08) (2008)Google Scholar
  5. 5.
    The universal device gateway.
  6. 6.
    oBIX 1.1 draft committe specification. The universal device gateway.
  7. 7.
    Neugschwandtner, M., Neugschwandtner, G., Kastner, W.: Web services in building automation: Mapping knx to obix. In: Proceedings of the 5th IEEE International Conference on Industrial Informatics (INDIN ’07) (2007)Google Scholar
  8. 8.
    Bovet, G., Hennebert, J.: Web-of-things gateway for knx networks. In: Proceedings of the IEEE European Conference on Smart Objects, Systems and Technologies (Smart SysTech) (2013)Google Scholar
  9. 9.
    Bovet, G., Hennebert, J.: Offering web-of-things connectivity to building networks. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (2013)Google Scholar
  10. 10.
    Jung, M., Weidinger, J., Reinisch, C., Kastner, W., Crettaz, C., Olivieri, A., Bocchi, Y.: A transparent ipv6 multi-protocol gateway to integrate building automation systems in the internet of things. In: Proceedings of the IEEE International Conference on Green Computing and Communications (2012)Google Scholar
  11. 11.
    Jung, M., Reinisch, C., Kastner, W.: Integrating building automation systems and ipv6 in the internet of things. In: Proceedings of the 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous, Computing (2012)Google Scholar
  12. 12.
    Guinard, D.: A web of things application architecture—integrating the real world into the web. PhD thesis, ETHZ (2011)Google Scholar
  13. 13.
    Nurseitov, N., Paulson, M., Reynolds, R., Izurieta, C.: Comparison of JSON and XML data interchange formats: A case study. In: CAINE (2009)Google Scholar
  14. 14.
    W3C Semantic Sensor Network Incubator Group: Review of sensor and observation ontologies.
  15. 15.
    W3C. Resource description framework.
  16. 16.
    W3C Semantic Sensor Network Incubator Group: Semantic sensor network ontology.
  17. 17.
  18. 18.
    W3C. Sparql query language for rdf.
  19. 19.
    Goland, Y., Cai, T., Leach, P., Gu, Y., Albright, S.: Simple service discovery protocol/1.0 operating without an arbiter. Technical report, IETF (2000)Google Scholar
  20. 20.
    Guttman, E., Perkins, C., Veizades, J., Day, M.: Service location protocol, version 2. Technical report, IETF (1999)Google Scholar
  21. 21.
    Cheshire, S., Krochmal, M.: Dns-based service discovery. Technical report (2013)Google Scholar
  22. 22.
    Kovacevic, A., Ansari, J., Mahonen, P.: Nanosd: A flexible service discovery protocol for dynamic and heterogeneous wireless sensor networks. In: Proceedings of the 6th International Conference on Mobile Ad-hoc and Sensor, Networks, pp. 14–19 (2010)Google Scholar
  23. 23.
    Butt, T., Phillips, I., Guan, L., Oikonomou G.: Trendy: An adaptive and context-aware service discovery protocol for 6 lowpans. In: Proceedings of the 3rd International Workshop on the Web of Things (2012)Google Scholar
  24. 24.
    Mayer, S., Guinard, D.: An extensible discovery service for smart things. In: Proceedings of the 2nd International Workshop on the Web of Things (WoT) (2011)Google Scholar
  25. 25.
    Rahman, A., Dijk, E.: Group communication for coap. Technical report, IETF (2013)Google Scholar
  26. 26.
    Iso 16484-2:2004 building automation and control systems (bacs). Technical report, International Organization for Standardization (2004)Google Scholar
  27. 27.
    Huang, W., Lam, H.N.: Using genetic algorithms to optimize controller using genetic algorithms to optimize controller parameters for hvac systems. In: Energy and Buildings, pp. 277–282 (1997)Google Scholar
  28. 28.
    Kanarachos, A., Geramanis, K.: Multivariable control of single zone hydronic heating systems with neural networks. In: Energy Conversion and Management, pp. 1317–1336 (1998)Google Scholar
  29. 29.
    Yao, Y., Lian, Z., Hou, Z., Zhou, X.: Optimal operation of a large cooling system based on an empirical model. Appl. Therm. Energy 24, 2303–2321 (2004)CrossRefGoogle Scholar
  30. 30.
    Gisler, C., Ridi, A., Zufferey, D., Khaled, O.A., Hennebert, J.: Appliance consumption signature database and recognition test protocols. In: Proceedings of the 8th International Workshop on Systems, Signal Processing and their Applications (Wosspa’13), pp. 336–341 (2013)Google Scholar
  31. 31.
    Ridi, A., Gisler, C., Hennebert, J.: Automatic identification of electrical appliances using smart plugs. In: Proceedings of the 8th Internation Workshop on Systems, Signal Processing and their Applications (Wosspa ’13), pp. 301–305 (2013)Google Scholar
  32. 32.
    Ridi, A., Gisler, C., Hennebert, J.: Unseen appliances identification. In: Proceedings of the 18th Iberoamerican Congress on Pattern Recognition (Ciarp ’13), to appear (2013)Google Scholar
  33. 33.
    Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27, 32–38 (2012)CrossRefGoogle Scholar
  34. 34.
    Ridi, A., Zakaridis, N., Bovet, G., Morel, N., Hennebert, J.: Towards reliable stochastic data-driven models applied to the energy saving in buildings. In: Proceedings of the International Conference on Cleantech for Smart Cities and Buildings (Cisbat ’13) (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gérôme Bovet
    • 1
    • 2
  • Antonio Ridi
    • 2
    • 3
  • Jean Hennebert
    • 2
    • 3
    Email author
  1. 1.Telecom Paris TechParisFrance
  2. 2.University of Applied Sciences Western SwitzerlandFribourgSwitzerland
  3. 3.University of FribourgFribourgSwitzerland

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