Skip to main content

Advertisement

SpringerLink
Log in

Analysis of beamforming for improving the energy efficiency in wireless sensor networks with metaheuristics

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

Distributed beamforming in a wireless sensor network (WSN) allows the sensor nodes to reduce the energy required to perform wireless communications, a critical issue to prolong the lifetime of this kind of systems, in which the node energy is supplied by limited sources such as batteries. This works analyzes the effect of the dimensionality in the problem instances to accomplish an efficient distributed beamforming, taking into account two separate factors: the number of sensor nodes involved in the beamforming and the type of antenna these nodes have installed. By using both classic and state-of-the-art metaheuristics, a thorough set of experiments have been performed, showing that, given the antenna type, the number of nodes makes the beamforming problem become harder because of the required synchronism. The results have also shown that beamforming is an effective technique to increase the WSN lifetime.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ahmed, M., Vorobyov, S.: Collaborative beamforming for wireless sensor networks with gaussian distributed sensor nodes. Wirel. Commun. IEEE Trans. 8(2), 638–643 (2009)

    Article  Google Scholar 

  2. Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. Commun. Mag. IEEE 40(8), 102–114 (2002)

  3. Bäck, T., Fogel, D.B., Michalewicz, Z.: Handbook of evolutionary computation. Oxford University Press, Oxford (1997)

  4. Balanis, C.A.: Antenna theory: analysis and design, 3rd edn. Wiley, New York (2005)

    Google Scholar 

  5. Bejar Haro, B., Zazo, S., Palomar, D.: Energy efficient collaborative beamforming in wireless sensor networks. Signal Process IEEE Trans. 62(2), 496–510 (2014)

  6. Ehsan, S., Hamdaoui, B.: A survey on energy-efficient routing techniques with qos assurances for wireless multimedia sensor networks. Commun. Surv. Tutor. 14(2), 265–278 (2012)

    Article  Google Scholar 

  7. Feng, J., Lu, Y., Jung, B., Peroulis, D.: Energy efficient collaborative beamforming in wireless sensor networks. In: International symposium on circuits and systems, pp. 2161–2164 (2009)

  8. Gershman, A., Sidiropoulos, N., ShahbazPanahi, S., Bengtsson, M., Ottersten, B.: Convex optimization-based beamforming. Signal Process. Mag. IEEE 27(3), 62–75 (2010)

    Article  Google Scholar 

  9. Hansen, N.: The CMA evolution strategy: a comparing review. In: Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pp. 75–102. Springer, New York (2006)

  10. Harrop, P.: Wireless sensor networks 2012–2022. IDTechEx (2012)

  11. Rawat, P., Singh, K.D., Chaouchi, H., Bonnin, J.M.: Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput. 68, 1–48 (2014)

    Article  Google Scholar 

  12. Storn, R., Price, K.: Differential evolution—a simple efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. 95-012, Int. Compt. Sci. Inst., Berkeley, CA (1995)

  13. Valenzuela-Valdés, J.F., Luna, F., Luque-Baena, R.M., Padilla, P.: Saving energy in wsns with beamforming. In: 3rd IEEE international conference on cloud networking, CloudNet 2014, pp. 255–260 (2014)

Download references

Acknowledgments

This work has been partially funded by the Government of Extremadura and the European Regional Development Fund (FEDER) under the IB13113 project. Francisco Luna acknowledges the assistance of the University of Malaga, Andalusia Tech Campus of International Excellence.

Author information

Authors and Affiliations

  1. Dpto. de Ingeniería de Sistemas Informáticos y Telemáticos, Centro Universitario de Mérida, Universidad de Extremadura, Badajoz, Spain

    J. Enrique Agudo & Rafael M. Luque-Baena

  2. Dpto. Teoría de la Señal, Telemática y Comunicaciones, Universidad de Granada, Granada, Spain

    Juan F. Valenzuela-Valdés & Pablo Padilla

  3. Dpto. de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain

    Francisco Luna

Authors
  1. J. Enrique Agudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan F. Valenzuela-Valdés
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Luna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafael M. Luque-Baena
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pablo Padilla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco Luna.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agudo, J.E., Valenzuela-Valdés, J.F., Luna, F. et al. Analysis of beamforming for improving the energy efficiency in wireless sensor networks with metaheuristics. Prog Artif Intell 5, 199–206 (2016). https://doi.org/10.1007/s13748-016-0087-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-016-0087-z

Keywords

Search

Navigation