Skip to main content

Advertisement

SpringerLink
Log in

Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

  • S.I. : AI-Driven smart networking and communication for Personal IoT
  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes “external dispersion method” to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes “free-particle swarm optimization” to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.

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
Fig. 8
Fig. 9
Fig. 10

References

  1. S. Sakamoto, K. Ozera, A. Barolli, et al., Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods, Soft Computing, Vol. 4, pp. 1–7, 2017.

    Google Scholar 

  2. Y. H. Lin, The simulation of east-bound transoceanic voyages according to ocean-current sailing based on particle swarm optimization in the weather routing system, Marine Structures, Vol. 59, pp. 219–236, 2018.

    Article  Google Scholar 

  3. X. Yang, X. Chen, R. Xia, et al., Wireless sensor network congestion control based on standard particle swarm optimization and single neuron PID, Sensors., Vol. 18, No. 4, p. 1265, 2018.

    Article  Google Scholar 

  4. X. Tong, J. Lin, Y. Ji, et al., Global optimization of wireless seismic sensor network based on the kriging model and improved particle swarm optimization algorithm, Wireless Personal Communications, Vol. 95, No. 3, pp. 1–20, 2017.

    Article  Google Scholar 

  5. S. Phoemphon, C. So-In, D. Niyato. a hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization, Applied Soft Computing, Vol. 65, 2018.

  6. B. F. Gumaida and J. Luo, An efficient algorithm for wireless sensor network localization based on hierarchical structure poly-particle swarm optimization, Wireless Personal Communications, Vol. 97, No. 7, pp. 1–27, 2017.

    Google Scholar 

  7. C. Huang, D. Zhang and G. Song, A novel mapping algorithm for three-dimensional network on chip based on quantum-behaved particle swarm optimization, Frontiers of Computer Science, Vol. 11, No. 4, pp. 1–10, 2017.

    Article  Google Scholar 

  8. L. Guneshwor, T. I. Eldho and A. V. Kumar, Identification of groundwater contamination sources using Meshfree RPCM simulation and particle swarm optimization, Water Resources Management, Vol. 32, No. 4, pp. 1517–1538, 2018.

    Article  Google Scholar 

  9. D. Chao, Z. Yin, Y. Zhang, et al., Research on active disturbance rejection control of induction motors based on adaptive particle swarm optimization algorithm with dynamic inertia weight, IEEE Transactions on Power Electronics., Vol. 99, p. 1, 2018.

    Google Scholar 

  10. G. Sharma and A. Kumar, Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks, Telecommunication Systems, Vol. 67, No. 8, pp. 1–16, 2018.

    Google Scholar 

  11. P. Shen, J. Li, X. Zhan, et al., Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle, Energy, Vol. 123, pp. 89–107, 2017.

    Article  Google Scholar 

  12. M. Van and H. J. Kang, Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization, IEEE Transactions on Industrial Informatics, Vol. 12, No. 1, pp. 124–135, 2017.

    Google Scholar 

  13. S. H. Mousavian and H. R. Koofigar, Identification-based robust motion control of an AUV: optimized by particle swarm optimization algorithm, Journal of Intelligent & Robotic Systems, Vol. 85, No. 2, pp. 331–352, 2017.

    Article  Google Scholar 

  14. L. Hong, D. Yang, W. Su, et al., An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial Shading, IEEE Transactions on Industrial Electronics., Vol. 99, p. 1, 2018.

    Google Scholar 

  15. S. Geng, Y. Liu, Y. Ming, et al., Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm, Computer Networks, Vol. 116, pp. 63–78, 2017.

    Article  Google Scholar 

  16. T. Qasim, M. Zia, Q. A. Minhas, et al., An ant colony optimization based approach for minimum cost coverage on 3-D grid in wireless sensor networks, IEEE Communications Letters., Vol. 99, p. 1, 2018.

    Google Scholar 

  17. A. Thomas, P. Majumdar, T. I. Eldho, et al., Simulation optimization model for aquifer parameter estimation using coupled meshfree point collocation method and cat swarm optimization, Engineering Analysis with Boundary Elements, Vol. 91, pp. 60–72, 2018.

    Article  MathSciNet  MATH  Google Scholar 

  18. G. Tai and Z. Deng, An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing, Applied Acoustics, Vol. 127, pp. 46–62, 2017.

    Article  Google Scholar 

  19. N. Kumar, I. Hussain, B. Singh, et al., Single sensor-Based MPPT of partially shaded pv system for battery charging by using cauchy and Gaussian Sine Cosine optimization, IEEE Transactions on Energy Conversion, Vol. 32, No. 3, pp. 983–992, 2017.

    Article  Google Scholar 

  20. J. Lu, M. Huang and J. J. Yang, A novel spectrum sensing method based on tri-stable stochastic resonance and quantum particle swarm optimization, Wireless Personal Communications, Vol. 95, No. 3, pp. 1–13, 2017.

    Google Scholar 

  21. A. Vatankhah and S. Babaie, An optimized Bidding-based coverage improvement algorithm for hybrid wireless sensor networks, Computers & Electrical Engineering, Vol. 65, No. 1, pp. 1–17, 2018.

    Article  Google Scholar 

  22. A. Kaushik, S. Indu and D. Gupta, A grey wolf optimization approach for improving the performance of wireless sensor networks, Wireless Personal Communications, Vol. 2, No. 2, pp. 1–21, 2019.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Mechanical Engineering, Xinxiang University, Xinxiang, 453000, Henan, China

    Ye Zhang

Authors
  1. Ye Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ye Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y. Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization. Int J Wireless Inf Networks 27, 307–316 (2020). https://doi.org/10.1007/s10776-019-00446-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-019-00446-7

Keywords

Search

Navigation