Pigeon-Inspired Optimization for Node Location in Wireless Sensor Network

  • Trong-The Nguyen
  • Jeng-Shyang Pan
  • Thi-Kien DaoEmail author
  • Tien-Wen Sung
  • Truong-Giang Ngo
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 104)


Wireless Sensor Network (WSN) refers to a network of devices that can communicate the information gathered from a monitored field through wireless links. As a critical technology of WSN, the localization algorithm plays a vital role in improving node location accuracy and network efficiency. A hybrid Pigeon Inspired Optimization (PIO) with a typical localization model is proposed to solve the problem of node localization in WSN. The self-learning idea of PIO and speed formula are combined to improve exploring and exploiting agents of PIO. Fitness function for optimization is mathematically modeled based on analysis Pareto distances. The simulation results compared with the other approaches in the literature, e.g., the improved particle swarm optimization (PSO) and the cuckoo search (CS) show that the proposed method effectively improves the location accuracy of nodes and reduces the cumulative error caused by success positioning nodes.


Wireless sensor network Pigeon-inspired Optimization Location accuracy 


  1. 1.
    Gungor, V.C., Lu, B., Hancke, G.P.: Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans. Ind. Electron. (2010). Scholar
  2. 2.
    Nguyen, T.-T., Dao, T.-K., Kao, H.-Y., Horng, M.-F., Shieh, C.-S.: Hybrid particle swarm optimization with artificial bee colony optimization for topology control scheme in wireless sensor networks. J. Internet Technol. 18, 743–752 (2017). Scholar
  3. 3.
    Nguyen, T.-T., Pan, J., Dao, T.: An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network. IEEE Access 7, 75985–75998 (2019). Scholar
  4. 4.
    Pan, J.-S., Nguyen, T.-T., Dao, T.-K., Pan, T.-S., Chu, S.-C.: Clustering formation in wireless sensor networks: a survey. J. Netw. Intell. 02, 287–309 (2017)Google Scholar
  5. 5.
    García-hernández, C.F., Ibargüengoytia-gonzález, P.H., García-hernández, J., Pérez-díaz, J.A.: Wireless sensor networks and applications: a survey. J. Comput. Sci. 7, 264–273 (2007). Scholar
  6. 6.
    Nguyen, T.-T., Pan, J.-S., Dao, T.-K.: A compact bat algorithm for unequal clustering in wireless sensor networks (2019). Scholar
  7. 7.
    Nguyen, T.-T., Pan, J.-S., Chu, S.-C., Roddick, J.F., Dao, T.-K.: Optimization localization in wireless sensor network based on multi-objective firefly algorithm. J. Netw. Intell. 1, 130–138 (2016)Google Scholar
  8. 8.
    Pan, J.-S., Nguyen, T.-T., Chu, S.-C., Dao, T.-K., Ngo, T.-G.: Diversity enhanced ion motion optimization for localization in wireless sensor network. J. Inf. Hiding Multimedia Signal Process. 10, 221–229 (2019)Google Scholar
  9. 9.
    Nguyen, T.-T., Pan, J.-S., Dao, T.-K.: A novel improved bat algorithm based on hybrid parallel and compact for balancing an energy consumption problem (2019). Scholar
  10. 10.
    Peng, B., Li, L.: An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cogn. Neurodyn. 9, 249–256 (2015). Scholar
  11. 11.
    Low, K.S., Nguyen, H.A., Guo, H.: A particle swarm optimization approach for the localization of a wireless sensor network. In: IEEE International Symposium on Industrial Electronics (2008).
  12. 12.
    Goyal, S., Patterh, M.S.: Wireless sensor network localization based on cuckoo search algorithm. Wirel. Pers. Commun. 79, 223–234 (2014). Scholar
  13. 13.
    Chuang, P.J., Wu, C.P.: Employing PSO to enhance RSS range-based node localization for wireless sensor networks. J. Inf. Sci. Eng. 27, 1597–1611 (2011)MathSciNetGoogle Scholar
  14. 14.
    Pan, J.-S., Dao, T.-K., Pan, T.-S., Nguyen, T.-T., Chu, S.-C., Roddick, J.F.: An improvement of flower pollination algorithm for node localization optimization in WSN. J. Inf. Hiding Multimedia Signal Process. 08, 500–509 (2017)Google Scholar
  15. 15.
    Nguyen, T.-T., Thom, H.T.H., Dao, T.-K.: Estimation localization in wireless sensor network based on multi-objective grey wolf optimizer (2017). Scholar
  16. 16.
    Sai, V.-O., Shieh, C.-S., Nguyen, T.-T., Lin, Y.-C., Horng, M.-F., Le, Q.-D.: Parallel firefly algorithm for localization algorithm in wireless sensor network. In: Proceedings - 2015 3rd International Conference on Robot, Vision and Signal Processing, RVSP 2015 (2016).
  17. 17.
    Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7, 24–37 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Trong-The Nguyen
    • 1
    • 2
  • Jeng-Shyang Pan
    • 1
  • Thi-Kien Dao
    • 1
    Email author
  • Tien-Wen Sung
    • 1
  • Truong-Giang Ngo
    • 2
  1. 1.Fujian Provincial Key Lab of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  2. 2.Department of Information TechnologyHaiphong Private UniversityHaiphongVietnam

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