Advertisement

Wireless Sensor Positioning Using ACO Algorithm

  • Stefka FidanovaEmail author
  • Miroslav Shindarov
  • Pencho Marinov
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

Spatially distributed sensors, which communicate wirelessly form a wireless sensor network (WSN). This network monitors physical or environmental conditions. A central gateway, called high energy communication node, collects data from all sensors and sends them to the central computer where they are processed. We need to minimize the number of sensors and energy consumption of the network, when the terrain is fully covered. We convert the problem from multi-objective to mono-objective. The new objective function is a linear combination between the number of sensors and network energy. We propose ant colony optimization (ACO) algorithm to solve the problem. We compare our results with the state of the art in the literature.

Keywords

Wireless sensor network Ant colony optimization Metaheuristics 

Notes

Acknowledgments

This work has been partially supported by the Bulgarian National Scientific Fund under the grants Modeling Processes with fixed development rules—DID 02/29 and Effective Monte Carlo Methods for large-scale scientific problems—DTK 02/44.

References

  1. 1.
    Akuildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayrci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2001). ElsevierGoogle Scholar
  2. 2.
    Alba, E., Molina, G.: Optimal Wireless Sensor Layout with Metaheuristics: Solving a Large Scale Instance, Large-Scale Scientific Computing, LNMCS 4818, pp. 527–535. Springer (2008)Google Scholar
  3. 3.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999)Google Scholar
  4. 4.
    Cahon, S., Melab, N., Talbi, EI.-G.: Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10(3), 357–380 (2004)Google Scholar
  5. 5.
    Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Fidanova, S., Marinov, P., Alba, E.: ACO for optimal sensor layout. In: Filipe, J., Kacprzyk, J. (eds.) Proceedings of International Conference on Evolutionary Computing, Valencia, Spain, pp. 5–9. SciTePress-Science and Technology Publications Portugal (2010). ISBN 978-989-8425-31-7Google Scholar
  7. 7.
    Hernandez, H., Blum, C.: Minimum energy broadcasting in wireless sensor networks: an ant colony optimization approach for a realistic antenna model. J. Appl. Soft Comput. 11(8), 5684–5694 (2011)CrossRefGoogle Scholar
  8. 8.
    Jourdan, D.B.: Wireless sensor network planing with application to UWB localization in gps-denied environments. Massachusetts Institute of Technology, Ph.D. thesis (2000)Google Scholar
  9. 9.
    Konstantinidis, A., Yang, K., Zhang, Q., Zainalipour-Yazti, D.: A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. J. Comput. Netw. 54(6), 960–976 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Molina, G., Alba, E., Talbi, El.-G.: Optimal sensor network layout using multi-objective metaheuristics. Univ. Comput. Sci. 14(15), 2549–2565 (2008)Google Scholar
  11. 11.
    Nemeroff, J., Garcia, L., Hampel, D., DiPierro, S.: Application of Sensor Network Communications. In: IEEE Military Communication Conference, pp. 336–341 (2011)Google Scholar
  12. 12.
    Paek, J., Kothari, N., Chintalapudi, K., Rangwala, S., Govindan, R.: The performance of a wireless sensor network for structural health monitoring. In: Proceedings of 2nd European Workshop on Wireless Sensor Networks, Istanbul, Turkey, Jan 31–Feb 2 (2005)Google Scholar
  13. 13.
    Pottie, G.J., Kaiser, W.J.: Embedding the internet: wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)CrossRefGoogle Scholar
  14. 14.
    Romer, K., Mattern, F.: The design space of wireless sensor networks. IEEE Wirel. Commun. 11(6), pp. 54–61 (2004). ISSN 1536-1284Google Scholar
  15. 15.
    Stutzle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16, 889–914 (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Werner-Allen, G., Lorinez, K., Welsh, M., Marcillo, O., Jonson, J., Ruiz, M., Lees, J.: Deploying a wireless sensor nnetwork on an active volcano. IEEE Internet Comput. 10(2), 18–25 (2006)CrossRefGoogle Scholar
  17. 17.
    Wolf, S., Mezz, P.: Evolutionary local search for the minimum energy broadcast problem. In: Cotta, C., van Hemezl, J. (eds.) VOCOP 2008. Lecture Notes in Computer Sciences, vol. 4972, pp. 61–72. Springer, Germany (2008)Google Scholar
  18. 18.
    Xu, Y., Heidemann, J., Estrin, D.: Geography informed energy conservation for Ad Hoc routing. In: Proceedings of the 7th ACM/IEEE Annual International Conference on Mobile Computing and Networking, Italy, pp. 70–84, 16–21 July 2001Google Scholar
  19. 19.
    Yuce, M.R., Ng, S.W., Myo, N.L., Khan, J.Y., Liu, W.: Wireless body sensor network using medical implant band. Med. Syst. 31(6), 467–474 (2007)CrossRefGoogle Scholar
  20. 20.
    Zitzler, E., Knzli, S.: Indicator-based selection in multiobjective search. PPSN’04, LNCS 3242, pp. 832–842. Springer (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Stefka Fidanova
    • 1
    Email author
  • Miroslav Shindarov
    • 1
  • Pencho Marinov
    • 1
  1. 1.Institute of Information and Communication Technologies–BASSofiaBulgaria

Personalised recommendations