Natural Computing

, Volume 16, Issue 1, pp 5–13 | Cite as

An improved dynamic deployment method for wireless sensor network based on multi-swarm particle swarm optimization

  • Qingjian NiEmail author
  • Huimin Du
  • Qianqian Pan
  • Cen Cao
  • Yuqing Zhai


Dynamic deployment methods for wireless sensor network (WSN) can improve the quality of service (QoS) of the network by adjusting positions of mobile nodes. In the dynamic deployment problem model of this paper, not only the coverage rate of WSN but also the moving distance of mobile nodes is taken into consideration. This kind of model can be abstracted into multi-objective optimization problem, and particle swarm optimization (PSO) is introduced to solve this problem. In this paper, combined with previous work, an improved dynamic deployment method is proposed based on multi-swarm PSO. Specifically, we propose a discrete PSO to calculate the distance of mobile solutions, and a multi-swarm PSO is designed to optimize network performance for enhancing the QoS of deployment which includes higher coverage rate and lower energy consumption of mobile nodes. Experimental results demonstrate that the proposed method has a good performance in solving the WSN deployment problem.


Dynamic deployment Discrete particle swarm optimization Multi-swarm particle swarm optimization 



This paper is supported by National Natural Science Foundation of China (Grant No. 61170164).


  1. Aleksandra M, Gavrilovska L (2011) WSN coverage and connectivity improvement utilizing sensors mobility. In: Proceedings of the 11th European wireless conference on sustainable wireless technologiesGoogle Scholar
  2. Aziz N, Mohemmed AW, Sagar BSD (2007) Particle swarm optimization and voronoi diagram for wireless sensor networks coverage optimization. In: Proceedings of the international conference on intelligent and advanced systems, pp 961–965Google Scholar
  3. Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Applications of evolutionary computing, Springer, Berlin, pp 489–500Google Scholar
  4. Clerc M (2004) Discrete particle swarm optimization, illustrated by the traveling salesman problem. In: New optimization techniques in engineering. Springer, Berlin, pp 219–239Google Scholar
  5. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  6. Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195CrossRefGoogle Scholar
  7. Du HM, Ni QJ, Pan QQ, Yao YY, Lv Q (2014) An improved particle swarm optimization-based coverage control method for wireless sensor network. In: Proceedings of the 5th international conference on swarm intelligence, Springer, Berlin, pp 114–124Google Scholar
  8. Kennedy J (2005) Dynamic-probabilistic particle swarms. In: Proceedings of the 7th annual conference on genetic and evolutionary computation (GECCO) ACM, pp 201–207Google Scholar
  9. Kou XL, Liu SY, Zhang JK, Zheng W (2009) Co-evolutionary particle swarm optimization to solve constrained optimization problems. Comput Math Appl 57(11):1776–1784CrossRefzbMATHGoogle Scholar
  10. Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(2):262–267CrossRefGoogle Scholar
  11. Li SJ, Xu CF, Pan WK, Pan YH (2005) Sensor deployment optimization for detecting maneuvering targets. In: Proceedings of the 8th international conference on information fusion, vol 2Google Scholar
  12. Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings of IEEE swarm intelligence symposium (SIS), pp 124–129Google Scholar
  13. Liu YN, Wang G, Chen HL, Dong H, Zhu XD, Wang SJ (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8(2):191–200CrossRefGoogle Scholar
  14. Ma M, Yang YY (2007) Adaptive triangular deployment algorithm for unattended mobile sensor networks. IEEE Trans Comput 56(7):946–847MathSciNetCrossRefGoogle Scholar
  15. Mukhopadhyay S, Banerjee S (2012) Global optimization of an optical chaotic system by chaotic multi swarm particle swarm optimization. Expert Syst Appl 39(1):917–924CrossRefGoogle Scholar
  16. Ni QJ, Deng JM (2011) Two improvement strategies for logistic dynamic particle swarm optimization. In: Proceedings of the 10th international conference on adaptive and natural computing algorithms. Springer, Berlin, pp 320–329Google Scholar
  17. Shi YH, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE world congress on computational intelligence (WCCI), pp 69–73Google Scholar
  18. Shi XH, Liang YC, Lee HP, Lu C, Wang QX (2007) Particle swarm optimization-based algorithms for TSP and generalized TSP. Inf Process Lett 103(5):169–176MathSciNetCrossRefzbMATHGoogle Scholar
  19. Solomon S, Thulasiraman P, Thulasiram R (2011) Collaborative multi-swarm pso for task matching using graphics processing units. In: Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO), pp 1563–1570Google Scholar
  20. Vanneschi L, Codecasa D, Mauri G (2010) An empirical comparison of parallel and distributed particle swarm optimization methods. In: Proceedings of the 12th annual conference on genetic and evolutionary computation (GECCO), ACM, pp 15–22Google Scholar
  21. Wang K-P, Huang L, Zhou C-G, Pang W (2003) Particle swarm optimization for traveling salesman problem. In: Proceedings of the international conference on machine learning and cybernetics, vol 3, pp 1583–1585Google Scholar
  22. Wang X, Wang S, Ma JJ (2007) An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment. Sensors 7(3):354–370CrossRefGoogle Scholar
  23. Yazdani D, Nasiri B, Sepas-Moghaddam A, Meybodi MR (2013) A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization. Appl Soft Comput 13(4):2144–2158CrossRefGoogle Scholar
  24. Zhao SZ, Suganthan PN, Pan Q-K, Tasgetiren MF (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735–3742CrossRefGoogle Scholar
  25. Zou Y, Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces. In: Proceedings of the 22nd annual joint conference of the IEEE computer and communications (INFOCOM), vol 2, pp 1293–1303Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Qingjian Ni
    • 1
    Email author
  • Huimin Du
    • 2
  • Qianqian Pan
    • 3
  • Cen Cao
    • 1
  • Yuqing Zhai
    • 1
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.College of Software EngineeringSoutheast UniversityNanjingChina
  3. 3.School of Information Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations