Skip to main content

Design of a Middleware and Optimization Algorithms for Light Comfort in an Intelligent Environment

  • Conference paper
  • First Online:
Innovation in Medicine and Healthcare 2016 (InMed 2016)

Abstract

The evolution of technology allows to people with special capabilities of mobility to perform the activities faster and easier. The intelligent environments combined with optimization algorithms and middleware agents could help to this aim. This paper presents the design and the implementation of an architecture of a middleware agent that allows us to make the communication between heterogeneous devices (sensors and actuators of different communication protocols from WiFi to ZigBee). On the other hand, we present a comparison study between micro-algorithms used to get lighting comfort in order to perform an activity in a confined space; this is affect by the light from the outside, which can be blocked by shutters and doors, and lighting of lamps obtained within this space. The micro-algorithm evaluated were: Genetic Algorithm (GA), Artificial Immune System (AIS), Estimation Distribution Algorithm (EDA), Particle Swarm Optimization (PSO), Bee Algorithm (BA) and Bee Swarm Optimization (BSO).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Augusto, J.C.: Ambient intelligence: basic concepts and applications. Commun. Comput. Inf. Sci. 10, 16–26 (2008)

    Google Scholar 

  2. Čongradac, Velimir D., Milosavljevič, Boško B., Veličkovič, Jovan M., Prebiračevič, Bogdan V.: Control of the lighting system using a genetic algorithm. Thermal Sci. 16, 237–250 (2012). doi:10.2298/TSCI120203075C

    Article  Google Scholar 

  3. Romero, W., Zamudio, V., Baltazar, R., Sotelo, M., Soria, J.: Comparative study of BSO and GA for the optimizing energy in ambiente intelligence. In: Advances in Soft Computing, MICAI 2011, Part II, LNAI 7095, pp. 177–188 (2011)

    Google Scholar 

  4. Romero, L.A., Zamudio, V., Sotelo, M., Baltazar, R., Mezura, E.: A comparison between meta-heuristics as strategies for minimizing cyclic instability in ambient intelligence. Sensors (2012)

    Google Scholar 

  5. Herrera-Lozada, J.C., Calvo, H., Taud, H.: A micro artificial immune system. Polibits 43, 107–111 (2011)

    Article  Google Scholar 

  6. Kim, Y.S., Choi, A.S., Jeong, J.W.: Applying microgenetic algorithm to numerical model for luminous intensity distribution of planar prism LED luminaire. Opt. Commun. 293, 22–30 (2013)

    Article  Google Scholar 

  7. Augusto, J.C., Callaghan, V., Cook, D., Kameas, A., Satoh, I.: Intelligent environments: a manifesto. Hum. Centric Comput. Inf. Sci. 3, 12 (2013). Springer

    Google Scholar 

  8. Baquero, R., Rodríguez, J., Mendoza, S., Decouchant, D.: Towards a modular scheme for the integration of ambient intelligence systems. In: 5th International Symposium on Ubiquitous Computing and Ambient Intelligence UCAmI (2011)

    Google Scholar 

  9. Youngblood, G.M.: Middleware, Smart Environments: Technologies, Protocols, and Applications. In: Cook, D.J., Hoboken, S.K. (eds.), pp. 101–127. Wiley, New Jersey (2005)

    Google Scholar 

  10. Araujo, L., Cervigón, C.: Evolutionary Algorithms, A Practical Approach, 1st edn. In: de C.V. (ed.). SA Alfaomega Grupo, Mexico (2009)

    Google Scholar 

  11. Leandro, N.: de Castro y Jonathan Timmis. In: Artificial Immune System: A New Computational Intelligence Approach, 1st edn. Springer, UK (2002)

    Google Scholar 

  12. Larrañaga, P., Lozano, J.A.: Estimation of distribution algorithms (2003). http://www.redheur.org/sites/default/files/metodos/EDA01.pdf

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway, NJ, (1995) (in press)

    Google Scholar 

  14. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bee algorithm—a novel tool for complex optimisation problems. Intell. Prod. Mach. Syst. 2 (2006)

    Google Scholar 

  15. Sotelo-Figueroa, M.A., Baltazar-Flores, M.D.R., Carpio, J.M., Zamudio, V.: A comparison between bee swarm optimization and greedy algorithm for the knapsack problem with bee reallocation. In: 2010 Ninth Mexican International Conference on Articial Intelligence (MICAI), pp. 22–27, 8–13 Nov 2010

    Google Scholar 

  16. Tam, V., Cheng, K.Y., Lui, K.S.: Using micro-genetic algorithms to improve localization in wireless sensor networks. J. Commun. 1(4) (2006). Academy Publisher

    Google Scholar 

  17. Beaubrun, Ronald, Ruiz, Jhon Fredy Llano, Poirier, Benoit, Quintero, Alejandro: A middleware architecture for disseminating delay-constrained information in wireless sensor networks. J. Netw. Comput. Appl. 35, 403–411 (2012). doi:10.1016/j.jnca.2011.09.002

    Article  Google Scholar 

  18. Technological Arts, Fundada in 1995 (2014). http://www.nanocore12.com/

  19. Oracle (2013). http://www.sunspotworld.com/Tutorial/index.html

  20. MEMSIC Inc (2013). http://www.memsic.com/userfiles/files/datasheets/wsn/iris_datasheet.pdf

  21. Texas Instruments (2013). http://www.ti.com/tool/msp-exp430g2

  22. Derrac, J., García, S., Molina, D., Francisco, F.: A practical tutorial on the use of nonparametric statistical test as a methodology for comparing evolutionary and swarm intelligence algorithms. In: Swarm and Evolutionary Computation. Elsevier (2011)

    Google Scholar 

Download references

Acknowledgments

We would like to thank CONACYT, ITL and DGEST for supporting this project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rosario Baltazar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Llamas, T.B., Baltazar, R., Casillas, M.A., Lemus, L., Alanis, A., Zamudio, V. (2016). Design of a Middleware and Optimization Algorithms for Light Comfort in an Intelligent Environment. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2016. InMed 2016. Smart Innovation, Systems and Technologies, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-319-39687-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39687-3_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39686-6

  • Online ISBN: 978-3-319-39687-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics