WSN Coverage Optimization Based on Artificial Fish Swarm Algorithm

  • Changlin HeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)


To solve the problem of unreasonable random distribution of sensor nodes and low network coverage in sensor networks, a coverage optimization method based on artificial fish swarm algorithm is proposed. Firstly, the current research status of WSN coverage was analyzed, the node coverage and regional coverage in WSN on the basis were analyzed, the corresponding mathematical model was established, the WSN coverage optimization program in view of the artificial fish swarm was obtained. Finally, MATLAB was used for the simulation experiment, and the simulation results showed that the introduction of this method improved the node coverage in WSN effectively, the coverage area was huger at the same amount of nodes. Moreover, the algorithm can get the optimal solution in the global scope, and reach the better network coverage optimization effect with less sensor nodes, and the number of iterations was decreased significantly.


Wireless sensor networks (WSN) Artificial fish swarm algorithm Coverage optimization 


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., et al.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)CrossRefGoogle Scholar
  2. 2.
    Dimple, B.: Maximum coverage heuristics (MCH) for target coverage problem in wireless sensor network. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 300–305. IEEE (2014)Google Scholar
  3. 3.
    Ren, Y., Zhang, S.D., Zhang, H.K.: Theories and algorithms of coverage control for wireless sensor networks. J. Softw. 17(3), 422–433 (2006)CrossRefGoogle Scholar
  4. 4.
    Wang, X.Q., Yang, Y.T., Sun, T., et al.: Research on the gird based coverage problem in wireless sensor networks. Comput. Sci. 33(11), 38–39, 78 (2006)Google Scholar
  5. 5.
    Zeng, Y.L., Chen, J., Zheng, J.H.: Genetic algorithm-based sensor network coverage-enhancing approach. Comput. Eng. Appl. 45(11), 89–91 (2009)Google Scholar
  6. 6.
    Huang, Y.Y., Li, K.Q.: Coverage optimization of wireless sensor networks based on artificial fish swarm algorithm. Appl. Res. Comput. 30(2), 554–556 (2013)MathSciNetGoogle Scholar
  7. 7.
    Liu, W.T., Fan, Z.Y.: Coverage optimization of wireless sensor networks based on chaos particle swarm algorithm. J. Comput. Appl. 31(2), 338–340, 361 (2011)Google Scholar
  8. 8.
    Lin, M.J., Su, C.H., Wang, Y.: Study on optimization algorithms in wireless sensor networks. Comput. Simul. 28(3), 178–181 (2011)Google Scholar
  9. 9.
    Li, X.L., Shao, Z.J., Qian, J.X.: An optimizing method based on autonomous animates: fish-swarm algorithm. Syst. Eng. Theory Pract. 11, 32–38 (2002)Google Scholar
  10. 10.
    Jinag, M.Y., Yuan, D.F.: Artificial fish school algorithm and application, pp. 261–269. Science Press, Beijing (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Information Technology Service CenterHexi UniversityZhangyeChina

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