Internet of Things Based on Situation-Awareness for Energy Efficiency

Autores/as

  • Un Hee Schiefelbein Mestrado em Ciência da Computação - Universidade Federal de Santa Maria http://orcid.org/0000-0003-4628-2800
  • Diovane Soligo Colégio Politécnico - UFSM
  • Vinícius Maran Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC) – Universidade Federal de Santa Maria (UFSM)
  • José Palazzo M. de Oliveira Instituto de Informática – Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre, Rio Grande do Sul – Brazil
  • João Carlos Damasceno Lima PROGRAMA DE PÓS–GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO - Universidade Federal de Santa Maria
  • Alencar Machado PROGRAMA DE PÓS–GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO - Universidade Federal de Santa Maria

Palabras clave:

Context-awareness, nergy efficiency, pervasive application

Resumen

The reduction of electric energy consumption is considered as one of the main challenges in diverse sectors of the economy. To residential customers, the management of energy consumption can bring significant costs reduction and decreased environmental impact. This work presents a solution based on the use of situation-awareness applied in IOT that helps the users to reduce the consumption of electric energy through its own residence. The practical results obtained in the application of this proposal in a real-live scenario confirmed the option of collecting information directly of electrical appliances and inform the user of their energy expenditures in real-time, allowing the knowledge and the management of their expenses.

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Citas

Aarts, E., & Wichert, R. (2009). Ambient intelligence. Technology Guide. doi: 10.1007/978-3-540-88546-7_47.

Agência de energia elétrica do Brasil, Aneel. 2008. Agência Nacional de Energia Elétrica. Brasília. http://www.aneel.gov.br . December 2017.

Al-Daraiseh, A., El-Qawasmeh, E., & Shah, N. (2013). A Framework for Energy Monitoring and Management System for Educational Institutions. In IT Convergence and Security (ICITCS), 2013 International Conference on (pp. 1-4). IEEE. doi: 10.1109/ICITCS.2013.6717774

Atmel, C.L.D.; 1995.Book, A. Atmel Corporation,

Caragliu, A., del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology. doi: 10.1080/10630732.2011.601117

Chagas, J., Ferraz, C., Alves, A. P., & Carvalho, G. Sensibilidade a contexto na gestão eficiente de energia elétrica. 145-158, 2010

Darby, S. Making it obvious: designing feedback into energy consumption. In Energy efficiency in household appliances and lighting; Springer, 2001; pp. 685–696. doi: 10.1007/978-3-642-56531-1_73

Darby, S. (2006). the Effectiveness of Feedback on Energy Consumption a Review for Defra of the Literature on Metering , Billing and. Environmental Change Institute University of Oxford. doi: 10.4236/ojee.2013.21002.

Ehrhardt-martinez, A. K., & Donnelly, K. a. (2010). Advanced Metering Initiatives and Residential Feedback Programs : A Meta-Review for Household Electricity-Saving Opportunities. Energy.

Etzion, O., & Niblett, P. (2010). Event Processing in Action. Online.

Fischer, C. (2008). Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency.doi: 10.1007/s12053-008-9009-7

Güngör, V. C., Sahin, D., Kocak, T., Ergüt, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics. doi: 10.1109/TII.2011.2166794

Jain, R. K., Taylor, J. E., & Peschiera, G. (2012). Assessing eco-feedback interface usage and design to drive energy efficiency in buildings. Energy and Buildings. doi: 10.1016/j.enbuild.2011.12.033

Hermsen, S., Frost, J., Renes, R. J., & Kerkhof, P. (2016). Using feedback through digital technology to disrupt and change habitual behavior: a critical review of current literature. Computers in Human Behavior, 57, 61-74. doi: 10.1016/j.chb.2015.12.023

Houde, S., Todd, A., Sudarshan, A., Flora, J. A., & Armel, K. C. (2013). Real-time feedback and electricity consumption: A field experiment assessing the potential for savings and persistence. The Energy Journal, 87-102. doi: 10.5547/01956574.34.1.4

Lam, C. F., DeRue, D. S., Karam, E. P., & Hollenbeck, J. R. (2011). The impact of feedback frequency on learning and task performance: Challenging the “ more is better” assumption. Organizational Behavior and Human Decision Processes. doi: 10.1016/j.obhdp.2011.05.002

Lyytinen, K.; Yoo, Y. Ubiquitous computing. (2002). Communications of the ACM 2002, 45, 63–96.

Machado, A., Maran, V., Augustin, I., Wives, L. K., & de Oliveira, J. P. M. (2017). Reactive, proactive, and extensible situation-awareness in ambient assisted living. Expert Systems with Applications. doi: 10.1016/j.eswa.2017.01.033

Machado, A., Maran, V., Augustin, I., Lima, J. C., Wives, L. K., & de Oliveira, J. P. M. (2016, April). Reasoning on Uncertainty in Smart Environments. In Proceedings of the 18th International Conference on Enterprise Information Systems (pp. 240-250). SCITEPRESS-Science and Technology Publications, Lda. doi: 10.5220/0005866502400250

Machado, A., Lichtnow, D., Pernas, A. M., Wives, L. K., & de Oliveira, J. P. M. (2014, April). A Reactive and Proactive Approach for Ambient Intelligence. In ICEIS (2) (pp. 501-512). doi: 10.5220/0004884205010512

Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks. doi: 10.1109/JIOT.2015.2498900.

Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE communications surveys & tutorials, 16(1), 414-454. doi: 10.1109/SURV.2013.042313.00197

Sadri, F. Ambient intelligence: A survey. ACM Computing Surveys, 2011, 43. doi: 10.1145/1978802.1978815

Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for internet of things: a survey. IEEE Internet of Things Journal, 3(1), 70-95. doi: 10.1109/JIOT.2015.2498900

Rosa, L. M. F. (2013). Sensorização, fusão sensorial e dispositivos móveis: contribuições para a sustentabilidade de ambientes inteligentes (PhD thesis).

Salber, D., Dey, A. K., & Abowd, G. D. (1999). The Context Toolkit : Aiding the Development of Context-Enabled. CHI ’99 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Doi: 10.1145/302979.303126

Shajahan, A. H., & Anand, A. (2013). Data acquisition and control using Arduino-Android platform: Smart plug. In 2013 International Conference on Energy Efficient Technologies for Sustainability, ICEETS 2013. doi: 10.1109/ICEETS.2013.6533389

Schreurs, W.; Hildebrandt, M.; Gasson, M.; Warwick, K. Report on actual and possible profiling techniques in the field of ambient intelligence. Future of Identity in the Information Society (FIDIS) Project Deliverable 510 2005.

Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things. Cluster of European Research Projects on the Internet of Things, European Commision, 3(3), 34-36. doi: 10.2759/26127

Weiser, M. (1991). The Computer for the 21st Century. Scientific American. Doi: 10.1038/scientificamerican0991-94

Ye, J., Dobson, S., & McKeever, S. (2012). Situation identification techniques in pervasive computing: A review. Pervasive and Mobile Computing. doi: 10.1016/j.pmcj.2011.01.004

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Publicado

2019-04-17

Cómo citar

Schiefelbein, U. H., Soligo, D., Maran, V., de Oliveira, J. P. M., Lima, J. C. D., & Machado, A. (2019). Internet of Things Based on Situation-Awareness for Energy Efficiency. ISys - Brazilian Journal of Information Systems, 12(1), 28–53. Recuperado a partir de https://seer.unirio.br/isys/article/view/7866

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