Particle Swarm Optimization for the Deployment of Directional Sensors

  • Pankaj Singh
  • S. MiniEmail author
  • Ketan Sabale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)


Directional sensors are a special class of sensors that have special characteristics, such as the angle of sensing. Hence the techniques or methods that are used to solve problems in traditional disk-based sensing models may not be applicable to directional sensor networks. Random deployment of directional sensor nodes usually fails where the number of sensors are limited or have less sensing capability. This paper addresses coverage enhancement of applications that use directional sensor nodes. We assume that the number of directional sensor nodes are less than the number of objects to be covered in the region. The main aim is to identify the optimal/near optimal deployment locations of the directional sensor nodes such that the coverage is maximized. We use Particle Swarm Optimization (PSO) algorithm to compute the deployment locations of the nodes. The experimental results reveal that PSO is a promising method to solve this problem.


Wireless sensor networks Directional sensor networks Sensor deployment Particle swarm optimization 


  1. 1.
    Ai, J., Alhussein, A.A.: Coverage by directional sensors in randomly deployed wireless sensor networks. J. Comb. Optim. 11(1), 21–41 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  4. 4.
    Guvensan, M.A., Gokhan Yavuz, A.: On coverage issues in directional sensor networks: a survey. Ad Hoc Netw. 9(7), 1238–1255 (2011)CrossRefGoogle Scholar
  5. 5.
    Yanli, C., Lou, W., Li, M., Li, X.-Y.: Target-oriented scheduling in directional sensor networks. In: INFOCOM (2007)Google Scholar
  6. 6.
    Cai, Y., Lou, W., Li, M., Xiang-Yang, L.: Energy efficient target-oriented scheduling in directional sensor networks. IEEE Trans. Comput. 58(9), 1259–1274 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Ma, H., Liu, Y.: On coverage problems of directional sensor networks. In: Jia, X., Wu, J., He, Y. (eds.) MSN 2005. LNCS, vol. 3794, pp. 721–731. Springer, Heidelberg (2005). doi: 10.1007/11599463_70 CrossRefGoogle Scholar
  8. 8.
    Zhao, J., Zeng, J.-C.: An electrostatic field-based coverage-enhancing algorithm for wireless multimedia sensor networks. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing (2009)Google Scholar
  9. 9.
    Cheng, W., Li, S., Liao, X., Changxiang, S., Chen, H.: Maximal coverage scheduling in randomly deployed directional sensor networks. In: International Conference on Parallel Processing Workshops, pp. 10–14 (2007)Google Scholar
  10. 10.
    Yanli, C., Lou, W., Li, M.: Cover set problem in directional sensor networks. In: Future Generation Communication and Networking (2007)Google Scholar
  11. 11.
    Li, J., Wang, R.-C., Huang, H.-P., Sun, L.-J.: Voronoi based area coverage optimization for directional sensor networks. In: Second International Symposium on Electronic Commerce and Security, pp. 488–493 (2009)Google Scholar
  12. 12.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  13. 13.
    Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.National Institute of Technology GoaFarmagudiIndia

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