Advertisement

Wireless Personal Communications

, Volume 97, Issue 3, pp 4697–4727 | Cite as

An Estimation of Distribution Algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks

  • Dongbin Jiao
  • Liangjun KeEmail author
  • Weibo Yang
  • Jing Li
Article
  • 384 Downloads

Abstract

The design of energy efficiency is a very challenging issue for wireless sensor networks (WSNs). Clustering provides an effective means of tackling the issue. It could reduce energy consumption of the nodes and prolong the network lifetime. However, cluster heads deplete more energy since they bear great load of receiving, aggregation and transmission data than sensor nodes in WSNs. Therefore, the load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. In this paper, we introduce a new model for this problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm framework. A minimum-lifetime-based local search strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.

Keywords

Energy efficiency Load-balanced clustering Estimation of distribution algorithm Memetic algorithm Minimum lifetime Wireless sensor networks 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61573277), the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15201414), the Fundamental Research Funds for the Central Universities, the Open Research Fund of the State Key Laboratory of Astronautic Dynamics under Grant 2016ADL-DW403, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Open Projects Program of National Laboratory of Pattern Recognition. The authors would like to thank Mr. Xuan Liang and Dr. Ke Shang for their kind help and valuable suggestions. The authors are also thankful to the anonymous referees for their insightful comments and helpful suggestions which significantly improve the quality of manuscript.

Compliance with Ethical Standards

Conflict of interest

The authors have no conflicts of interest to declare.

Ethical Standards

We promise to comply with ethical standards. All authors have approved the manuscript and have contributed significantly for the paper.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.CrossRefGoogle Scholar
  2. 2.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  3. 3.
    Azharuddin, M., & Jana, P.K. (2016). PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Computing 1–15.Google Scholar
  4. 4.
    Bagci, H., & Yazici, A. (2010). An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In 2010 IEEE international conference on Fuzzy systems (FUZZ) (pp. 1–8). IEEE.Google Scholar
  5. 5.
    Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.CrossRefGoogle Scholar
  6. 6.
    Baluja, S. (1994). Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Technical Representative, DTIC Document.Google Scholar
  7. 7.
    Baluja, S., & Caruana, R. (1995). Removing the genetics from the standard genetic algorithm. In Machine learning: proceedings of the twelfth international conference (pp. 38–46).Google Scholar
  8. 8.
    Bandyopadhyay, S., Coyle, E.J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003, twenty-second annual joint conference of the IEEE computer and communications (Vol. 3, pp. 1713–1723). IEEE Societies, IEEE.Google Scholar
  9. 9.
    Baranidharan, B., & Santhi, B. (2016). Ducf: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.CrossRefGoogle Scholar
  10. 10.
    Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 31(14), 3451–3459.CrossRefGoogle Scholar
  11. 11.
    Bari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7(4), 665–676.CrossRefGoogle Scholar
  12. 12.
    Baronti, P., Pillai, P., Chook, V. W., Chessa, S., Gotta, A., & Hu, Y. F. (2007). Wireless sensor networks: A survey on the state of the art and the 802.15. 4 and zigbee standards. Computer Communications, 30(7), 1655–1695.CrossRefGoogle Scholar
  13. 13.
    Calhoun, B. H., Daly, D. C., Verma, N., Finchelstein, D. F., Wentzloff, D. D., Wang, A., et al. (2005). Design considerations for ultra-low energy wireless microsensor nodes. IEEE Transactions on Computers, 54(6), 727–740.CrossRefGoogle Scholar
  14. 14.
    Chakraborty, U.K., Das, S.K., Abbott, T.E. (2012). Energy-efficient routing in hierarchical wireless sensor networks using differential-evolution-based memetic algorithm. In 2012 IEEE Congress on Evolutionary Computation (pp. 1–8). IEEE.Google Scholar
  15. 15.
    Chatterjee, M., Das, S. K., & Turgut, D. (2002). Wca: A weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5(2), 193–204.CrossRefGoogle Scholar
  16. 16.
    Chen, X., Ong, Y. S., Lim, M. H., & Tan, K. C. (2011). A multi-facet survey on memetic computation. IEEE Transactions on Evolutionary Computation, 15(5), 591–607.CrossRefGoogle Scholar
  17. 17.
    Chen, X., Lei, G., Yang, G., Shao, K., Guo, Y., Zhu, J., et al. (2012). An improved population-based incremental learning method for objects buried in planar layered media. IEEE Transactions on Magnetics, 48(2), 1027–1030.CrossRefGoogle Scholar
  18. 18.
    Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–39.CrossRefGoogle Scholar
  19. 19.
    Dombo, D.A., & Folly, K. (2015). Multi-machine power system stabilizer design based on population based incremental learning. In 2015 IEEE symposium series on computational intelligence (pp. 1280–1285). IEEE.Google Scholar
  20. 20.
    Gupta, G., & Younis, M. (2003). Load-balanced clustering of wireless sensor networks. In ICC’03, IEEE international conference on communications, 2003 (Vol. 3, pp. 1848–1852). IEEE.Google Scholar
  21. 21.
    Gupta, I., Riordan, D., Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In 3rd Annual communication networks and services research conference (CNSR’05) (pp. 255–260). IEEE.Google Scholar
  22. 22.
    He, Z., Wei, C., Jin, B., Pei, W., & Yang, L. (1999). A new population-based incremental learning method for the traveling salesman problem. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Vol. 2, pp. 1152–1156). IEEE.Google Scholar
  23. 23.
    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRefGoogle Scholar
  24. 24.
    Heinzelman, W.R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000 (pp. 1–10). IEEE.Google Scholar
  25. 25.
    Ho, S., Yang, S., & Fu, W. (2011). A population-based incremental learning vector algorithm for multiobjective optimal designs. IEEE Transactions on Magnetics, 47(5), 1306–1309.CrossRefGoogle Scholar
  26. 26.
    Ho, S. L., Zhu, L., Yang, S., & Huang, J. (2015). A real coded population-based incremental learning for inverse problems in continuous space. IEEE Transactions on Magnetics, 51(3), 1–4.Google Scholar
  27. 27.
    Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks, 2(5), 87–97.CrossRefGoogle Scholar
  28. 28.
    Kim, J.M., Park, S.H., Han, Y.J., & Chung, T.M. (2008). Chef: cluster head election mechanism using fuzzy logic in wireless sensor networks. In ICACT 2008. 10th international conference on advanced communication technology, 2008 (Vol. 1, pp. 654–659). IEEE.Google Scholar
  29. 29.
    Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.CrossRefGoogle Scholar
  30. 30.
    Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.CrossRefGoogle Scholar
  31. 31.
    Kulkarni, R. V., Forster, A., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 13(1), 68–96.CrossRefGoogle Scholar
  32. 32.
    Larranaga, P., & Lozano, J. A. (2002). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). New York: Springer Science & Business Media.zbMATHGoogle Scholar
  33. 33.
    Lindsey, S., & Raghavendra, C.S. (2002). Pegasis: Power-efficient gathering in sensor information systems. In Aerospace conference proceedings, 2002 (Vol. 3, pp. 1125–1130). IEEE.Google Scholar
  34. 34.
    Liu, J. S., & Lin, C. H. R. (2005). Energy-efficiency clustering protocol in wireless sensor networks. Ad Hoc Networks, 3(3), 371–388.CrossRefGoogle Scholar
  35. 35.
    Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.CrossRefGoogle Scholar
  36. 36.
    Lozano, J. A. (2000). Analyzing the population based incremental learning algorithm by means of discrete dynamical systems. Complex Systems, 12, 465–479.MathSciNetzbMATHGoogle Scholar
  37. 37.
    Lozano, J. A. (2006). Towards a new evolutionary computation: Advances on estimation of distribution algorithms (Vol. 192). New York: Springer Science & Business Media.zbMATHCrossRefGoogle Scholar
  38. 38.
    Martins, F. V., Carrano, E. G., Wanner, E. F., Takahashi, R. H., & Mateus, G. R. (2011). A hybrid multiobjective evolutionary approach for improving the performance of wireless sensor networks. IEEE Sensors Journal, 11(3), 545–554.CrossRefGoogle Scholar
  39. 39.
    Meng, X., Li, J., Zhou, M., Dai, X., & Dou, J. (2015). Population-based incremental learning algorithm for a serial colored traveling salesman problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems PP(99), 1–12.Google Scholar
  40. 40.
    Mühlenbein, H., & Paass, G. (1996). From recombination of genes to the estimation of distributions i. binary parameters. In International conference on parallel problem solving from nature (pp. 178–187). Springer.Google Scholar
  41. 41.
    Neri, F., & Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2, 1–14.CrossRefGoogle Scholar
  42. 42.
    Nguyen, Q. H., Ong, Y. S., & Lim, M. H. (2009). A probabilistic memetic framework. IEEE Transactions on Evolutionary Computation, 13(3), 604–623.CrossRefGoogle Scholar
  43. 43.
    Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.CrossRefGoogle Scholar
  44. 44.
    Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top–down survey. Computer Networks, 67, 104–122.CrossRefGoogle Scholar
  45. 45.
    Sabor, N., Abo-Zahhad, M., Sasaki, S., & Ahmed, S. M. (2016). An unequal multi-hop balanced immune clustering protocol for wireless sensor networks. Applied Soft Computing, 43, 372–389.CrossRefGoogle Scholar
  46. 46.
    Saleem, M., Di Caro, G. A., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181(20), 4597–4624.CrossRefGoogle Scholar
  47. 47.
    Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1093–1102.CrossRefGoogle Scholar
  48. 48.
    Sert, S. A., Bagci, H., & Yazici, A. (2015). Mofca: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.CrossRefGoogle Scholar
  49. 49.
    Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 33(5), 560–572.CrossRefGoogle Scholar
  50. 50.
    Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences, 2(1), 2–13.CrossRefGoogle Scholar
  51. 51.
    Smaragdakis, G., Bestavros, A., & Matta, I. (2004). Sep: A stable election protocol for clustered heterogeneous wireless sensor networks. Technical Representative, Boston University Computer Science Department.Google Scholar
  52. 52.
    Taheri, H., Neamatollahi, P., Younis, O. M., Naghibzadeh, S., & Yaghmaee, M. H. (2012). An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10(7), 1469–1481.CrossRefGoogle Scholar
  53. 53.
    Wang, G., Wang, Y., & Tao, X. (2009). An ant colony clustering routing algorithm for wireless sensor networks. In 3rd International conference on genetic and evolutionary computing, 2009. WGEC’09 (pp. 670–673). IEEE.Google Scholar
  54. 54.
    Wang, S. Y., & Wang, L. (2016). An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(1), 139–149.CrossRefGoogle Scholar
  55. 55.
    Wei, D., Jin, Y., Vural, S., Moessner, K., & Tafazolli, R. (2011). An energy-efficient clustering solution for wireless sensor networks. IEEE Transactions on Wireless Communications, 10(11), 3973–3983.CrossRefGoogle Scholar
  56. 56.
    Wu, Y., Fahmy, S., Shroff, N.B. (2008). On the construction of a maximum-lifetime data gathering tree in sensor networks: Np-completeness and approximation algorithm. In INFOCOM 2008. The 27th conference on computer communications (pp. 1013–1021). IEEE.Google Scholar
  57. 57.
    Xing, H., & Qu, R. (2011). A population based incremental learning for network coding resources minimization. IEEE Communications Letters, 15(7), 698–700.CrossRefGoogle Scholar
  58. 58.
    Yang, S., & Yao, X. (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11), 815–834.zbMATHCrossRefGoogle Scholar
  59. 59.
    Yang, S. Y., Ho, S. L., Ni, G. Z., Machado, J. M., & Wong, K. F. (2007). A new implementation of population based incremental learning method for optimizations in electromagnetics. IEEE Transactions on Magnetics, 43(4), 1601–1604.CrossRefGoogle Scholar
  60. 60.
    Ye, M., Li, C., Chen, G., & Wu, J. (2005). Eecs: an energy efficient clustering scheme in wireless sensor networks. In PCCC 2005. 24th IEEE international performance, computing, and communications conference, 2005 (pp. 535–540). IEEE.Google Scholar
  61. 61.
    Yigitel, M. A., Incel, O. D., & Ersoy, C. (2011). Qos-aware mac protocols for wireless sensor networks: A survey. Computer Networks, 55(8), 1982–2004.CrossRefGoogle Scholar
  62. 62.
    Younis, M., Youssef, M., & Arisha, K. (2003). Energy-aware management for cluster-based sensor networks. Computer Networks, 43(5), 649–668.CrossRefGoogle Scholar
  63. 63.
    Younis, O., & Fahmy, S. (2004). Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.CrossRefGoogle Scholar
  64. 64.
    Zungeru, A. M., Ang, L. M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35(5), 1508–1536.CrossRefGoogle Scholar
  65. 65.
    Jiao, D., Ke, L., Yang, W., & Li, J. (2017). An estimation of distribution algorithm based load-balanced clustering of wireless sensor networks. In Computational science and engineering (CSE) and embedded and ubiquitous computing (EUC), 2017 IEEE international conference on, IEEE (Vol. 1, pp. 151–158).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Dongbin Jiao
    • 1
  • Liangjun Ke
    • 1
    Email author
  • Weibo Yang
    • 1
  • Jing Li
    • 2
  1. 1.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.State Key Laboratory of Astronautic DynamicsXi’an Satellite Control CenterXi’anChina

Personalised recommendations