An Estimation of Distribution Algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks
- 384 Downloads
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.
KeywordsEnergy efficiency Load-balanced clustering Estimation of distribution algorithm Memetic algorithm Minimum lifetime Wireless sensor networks
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.
We promise to comply with ethical standards. All authors have approved the manuscript and have contributed significantly for the paper.
This article does not contain any studies with human participants performed by any of the authors.
- 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.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
- 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.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.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
- 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
- 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.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.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.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
- 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
- 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
- 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
- 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
- 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.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
- 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
- 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
- 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
- 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
- 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
- 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