Advertisement

Sensorization and Intelligent Systems in Energetic Sustainable Environments

  • Fábio Silva
  • David Cuevas
  • Cesar Analide
  • José Neves
  • José Marques
Part of the Studies in Computational Intelligence book series (SCI, volume 446)

Abstract

Sustainability is an important topic of discussion in our world. However, measuring sustainability and assessing behaviors is not always easy. Indeed, and in order to fulfill this goal, in this work it will be proposed a multi-agent based architecture to measure and assess sustainable indicators taken from a given environment. These evaluations will be based on past and present behaviors of the users and the particularities of the setting, leading to the evaluation of workable indicators such as gas emissions, energetic consumption and the users fitting with respect to the milieu. Special attention is given to user interaction and user attributes to calculate sustainable indicators for each type of structure, i.e., the aim of this scheme is to promote sustainability awareness and sustainable actions through the use of sustainable markers calculated in terms of the information gathered from the environment.

Keywords

Recommender System Workable Indicator Sustainability Assessment Ambient Intelligence Sustainability Indicator 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer US (2011)Google Scholar
  2. 2.
    Al-Waer, H., Clements-Croome, D.J.: Key performance indicators (kpis) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings. Building and Environment 45(4), 799–807 (2009)CrossRefGoogle Scholar
  3. 3.
    Aztiria, A., Izaguirre, A., Augusto, J.C.: Learning patterns in ambient intelligence environments: a survey. Artif. Intell. Rev. 34, 35–51 (2010)CrossRefGoogle Scholar
  4. 4.
    Chetty, M., Tran, D., Grinter, R.E.: Getting to green: understanding resource consumption in the home. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 242–251. ACM, New York (2008)CrossRefGoogle Scholar
  5. 5.
    Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., Burgelman, J.C.: Scenarios for Ambient Intelligence in 2010. Tech. rep., IST Advisory Group (2001), ftp://ftp.cordis.lu/pub/ist/docs/istagscenarios2010.pdf
  6. 6.
    Hagras, H., Doctor, F., Callaghan, V., Lopez, A.: An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments. IEEE Transactions on Fuzzy Systems 15(1), 41–55 (2007)CrossRefGoogle Scholar
  7. 7.
    Lyon, A., Dahl: Achievements and gaps in indicators for sustainability. Ecological Indicators 17(0), 14–19 (2012), doi:10.1016/j.ecolind.2011.04.032; Indicators of environmental sustainability: From concept to applicationsGoogle Scholar
  8. 8.
    Neves, J., Ribeiro, J., Pereira, P., Alves, V., Machado, J., Abelha, A., Novais, P., Analide, C., Santos, M., Fernndez-Delgado, M.: Evolutionary intelligence in asphalt pavement modeling and quality-of-information. Progress in Artificial Intelligence 1, 119–135 (2012), doi:10.1007/s13748-011-0003-5CrossRefGoogle Scholar
  9. 9.
    Rui, C., Yi-bin, H., Zhang-qin, H., Jian, H.: Modeling the ambient intelligence application system: Concept, software, data, and network. IEEE Trans. on SMCC 39(3), 299–314 (2009), doi:10.1109/TSMCC.2009.2014390Google Scholar
  10. 10.
    Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: First Workshop on Mobile Computing Systems and Applications, WMCSA 1994, pp. 85–90 (1994), doi:10.1109/WMCSA.1994.16 Google Scholar
  11. 11.
    Singh, R., Murty, H., Gupta, S., Dikshit, A.: An overview of sustainability assessment methodologies. Ecological Indicators 9(2), 189–212 (2009)CrossRefGoogle Scholar
  12. 12.
    Wang, K.I.K., Abdulla, W.H., Salcic, Z.: Ambient intelligence platform using multi-agent system and mobile ubiquitous hardware. Pervasive and Mobile Computing, 558–573 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fábio Silva
    • 1
  • David Cuevas
    • 1
  • Cesar Analide
    • 1
  • José Neves
    • 1
  • José Marques
    • 1
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations