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Erschienen in: Wireless Personal Communications 3/2017

24.08.2017

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

verfasst von: Dongbin Jiao, Liangjun Ke, Weibo Yang, Jing Li

Erschienen in: Wireless Personal Communications | Ausgabe 3/2017

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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.

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Literatur
1.
Zurück zum Zitat Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.CrossRef Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.CrossRef
2.
Zurück zum Zitat Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef
3.
Zurück zum Zitat 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. 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.
4.
Zurück zum Zitat 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. 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.
5.
Zurück zum Zitat Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.CrossRef Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.CrossRef
6.
Zurück zum Zitat Baluja, S. (1994). Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Technical Representative, DTIC Document. Baluja, S. (1994). Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Technical Representative, DTIC Document.
7.
Zurück zum Zitat 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). 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).
8.
Zurück zum Zitat 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. 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.
9.
Zurück zum Zitat Baranidharan, B., & Santhi, B. (2016). Ducf: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.CrossRef Baranidharan, B., & Santhi, B. (2016). Ducf: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.CrossRef
10.
Zurück zum Zitat Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 31(14), 3451–3459.CrossRef Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 31(14), 3451–3459.CrossRef
11.
Zurück zum Zitat 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.CrossRef 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.CrossRef
12.
Zurück zum Zitat 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.CrossRef 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.CrossRef
13.
Zurück zum Zitat 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.CrossRef 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.CrossRef
14.
Zurück zum Zitat 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. 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.
15.
Zurück zum Zitat Chatterjee, M., Das, S. K., & Turgut, D. (2002). Wca: A weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5(2), 193–204.CrossRef Chatterjee, M., Das, S. K., & Turgut, D. (2002). Wca: A weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5(2), 193–204.CrossRef
16.
Zurück zum Zitat 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.CrossRef 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.CrossRef
17.
Zurück zum Zitat 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.CrossRef 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.CrossRef
18.
Zurück zum Zitat Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–39.CrossRef Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–39.CrossRef
19.
Zurück zum Zitat 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. 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.
20.
Zurück zum Zitat 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. 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.
21.
Zurück zum Zitat 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. 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.
22.
Zurück zum Zitat 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. 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.
23.
Zurück zum Zitat 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.CrossRef 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.CrossRef
24.
Zurück zum Zitat 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. 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.
25.
Zurück zum Zitat 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.CrossRef 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.CrossRef
26.
Zurück zum Zitat 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. 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.
27.
Zurück zum Zitat Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks, 2(5), 87–97.CrossRef Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks, 2(5), 87–97.CrossRef
28.
Zurück zum Zitat 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. 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.
29.
Zurück zum Zitat Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.CrossRef Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.CrossRef
30.
Zurück zum Zitat 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.CrossRef 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.CrossRef
31.
Zurück zum Zitat 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.CrossRef 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.CrossRef
32.
Zurück zum Zitat Larranaga, P., & Lozano, J. A. (2002). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). New York: Springer Science & Business Media.MATH Larranaga, P., & Lozano, J. A. (2002). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). New York: Springer Science & Business Media.MATH
33.
Zurück zum Zitat 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. 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.
34.
Zurück zum Zitat Liu, J. S., & Lin, C. H. R. (2005). Energy-efficiency clustering protocol in wireless sensor networks. Ad Hoc Networks, 3(3), 371–388.CrossRef Liu, J. S., & Lin, C. H. R. (2005). Energy-efficiency clustering protocol in wireless sensor networks. Ad Hoc Networks, 3(3), 371–388.CrossRef
35.
Zurück zum Zitat 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.CrossRef 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.CrossRef
36.
Zurück zum Zitat Lozano, J. A. (2000). Analyzing the population based incremental learning algorithm by means of discrete dynamical systems. Complex Systems, 12, 465–479.MathSciNetMATH Lozano, J. A. (2000). Analyzing the population based incremental learning algorithm by means of discrete dynamical systems. Complex Systems, 12, 465–479.MathSciNetMATH
37.
Zurück zum Zitat Lozano, J. A. (2006). Towards a new evolutionary computation: Advances on estimation of distribution algorithms (Vol. 192). New York: Springer Science & Business Media.MATHCrossRef Lozano, J. A. (2006). Towards a new evolutionary computation: Advances on estimation of distribution algorithms (Vol. 192). New York: Springer Science & Business Media.MATHCrossRef
38.
Zurück zum Zitat 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.CrossRef 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.CrossRef
39.
Zurück zum Zitat 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. 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.
40.
Zurück zum Zitat 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. 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.
41.
Zurück zum Zitat Neri, F., & Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2, 1–14.CrossRef Neri, F., & Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2, 1–14.CrossRef
42.
Zurück zum Zitat Nguyen, Q. H., Ong, Y. S., & Lim, M. H. (2009). A probabilistic memetic framework. IEEE Transactions on Evolutionary Computation, 13(3), 604–623.CrossRef Nguyen, Q. H., Ong, Y. S., & Lim, M. H. (2009). A probabilistic memetic framework. IEEE Transactions on Evolutionary Computation, 13(3), 604–623.CrossRef
43.
Zurück zum Zitat 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.CrossRef 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.CrossRef
44.
Zurück zum Zitat Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top–down survey. Computer Networks, 67, 104–122.CrossRef Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top–down survey. Computer Networks, 67, 104–122.CrossRef
45.
Zurück zum Zitat 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.CrossRef 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.CrossRef
46.
Zurück zum Zitat 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.CrossRef 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.CrossRef
47.
Zurück zum Zitat 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.CrossRef 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.CrossRef
48.
Zurück zum Zitat Sert, S. A., Bagci, H., & Yazici, A. (2015). Mofca: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.CrossRef Sert, S. A., Bagci, H., & Yazici, A. (2015). Mofca: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.CrossRef
49.
Zurück zum Zitat 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.CrossRef 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.CrossRef
50.
Zurück zum Zitat 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.CrossRef 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.CrossRef
51.
Zurück zum Zitat 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. 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.
52.
Zurück zum Zitat 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.CrossRef 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.CrossRef
53.
Zurück zum Zitat 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. 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.
54.
Zurück zum Zitat 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.CrossRef 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.CrossRef
55.
Zurück zum Zitat 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.CrossRef 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.CrossRef
56.
Zurück zum Zitat 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. 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.
57.
Zurück zum Zitat Xing, H., & Qu, R. (2011). A population based incremental learning for network coding resources minimization. IEEE Communications Letters, 15(7), 698–700.CrossRef Xing, H., & Qu, R. (2011). A population based incremental learning for network coding resources minimization. IEEE Communications Letters, 15(7), 698–700.CrossRef
58.
Zurück zum Zitat Yang, S., & Yao, X. (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11), 815–834.MATHCrossRef Yang, S., & Yao, X. (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11), 815–834.MATHCrossRef
59.
Zurück zum Zitat 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.CrossRef 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.CrossRef
60.
Zurück zum Zitat 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. 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.
61.
Zurück zum Zitat 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.CrossRef 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.CrossRef
62.
Zurück zum Zitat Younis, M., Youssef, M., & Arisha, K. (2003). Energy-aware management for cluster-based sensor networks. Computer Networks, 43(5), 649–668.CrossRef Younis, M., Youssef, M., & Arisha, K. (2003). Energy-aware management for cluster-based sensor networks. Computer Networks, 43(5), 649–668.CrossRef
63.
Zurück zum Zitat 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.CrossRef 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.CrossRef
64.
Zurück zum Zitat 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.CrossRef 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.CrossRef
65.
Zurück zum Zitat 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). 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).
Metadaten
Titel
An Estimation of Distribution Algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks
verfasst von
Dongbin Jiao
Liangjun Ke
Weibo Yang
Jing Li
Publikationsdatum
24.08.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4746-6

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