Browse > Article
http://dx.doi.org/10.3837/tiis.2020.02.003

A many-objective optimization WSN energy balance model  

Wu, Di (Taiyuan University of Science and Technology, Complex System and Computation Intelligent Laboratory)
Geng, Shaojin (Taiyuan University of Science and Technology, Complex System and Computation Intelligent Laboratory)
Cai, Xingjuan (Taiyuan University of Science and Technology, Complex System and Computation Intelligent Laboratory)
Zhang, Guoyou (Taiyuan University of Science and Technology, Complex System and Computation Intelligent Laboratory)
Xue, Fei (Beijing Wuzi University, School of Information)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.2, 2020 , pp. 514-537 More about this Journal
Abstract
Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.
Keywords
WSN; LEACH protocol; many-objective optimization; energy consumption;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 X. J. Cai, Y. Niu, S. J. Geng, J. J. Zhang, Z. H. Cui, J. W. Li and J. J. Chen, "An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search," Concurrency and Computation: Practice and Experience, 2019.
2 X. J. Cai, P. H. Wang, L. Du, Z. H. Cui, W. S. Zhang and J. J. Chen, "Multi-objective 3-Dimensional DV-Hop Localization Algorithm with NSGA-II," IEEE Sensors Journal, vol.19, no.21, pp..10003-10015, 2019.   DOI
3 G. G. Wang, X. J. Cai, Z. H. Cui, G. Y. Min and J. J. Chen, "High Performance Computing for Cyber Physical Social Systems by Using Evolutionary Multi-Objective Optimization Algorithm," IEEE Transactions on Emerging Topics in Computing, p.1-1, 2017.
4 W. Arloff, K. R. B. Schmitt and L. J. Venstrom, "A parameter estimation method for stiff ordinary differential equations using particle swarm optimisation," International Journal of Computing Science and Mathematics, vol. 9, no. 5, pp. 419-432, 2018.   DOI
5 X. Zhang, X. T. Li and M. H. Yin, "Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem," International Journal of Bio-Inspired Computation, vol. 13, no. 2, pp. 102-110, 2019.   DOI
6 Z. H. Cui, Y. Cao, X. J. Cai, J. H. Cai and J. J. Chen, "Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things," Journal of Parallel and Distributed Computing, vol. 132, pp. 217-229, 2019.   DOI
7 X. J. Cai, X. Z. Gao and Y. Xue, "Improved bat algorithm with optimal forage strategy and random disturbance strategy," International Journal of Bio-inspired Computation, vol. 8, no. 4, pp. 205-214, 2016.   DOI
8 B. P. Zhao, Y. Xue, B. Xu, T. H. Ma and J. F. Liu, "Multi-objective classification based on NSGA-II," International Journal of Computing Science and Mathematics, vol. 9, no. 6, pp. 539-546, 2018.   DOI
9 H. Kamel, K. Nadjet and D. Habiba, "Multi-objective bat algorithm for mining numerical association rules," International Journal of Bio-Inspired Computation, vol. 11, no. 4, pp. 239- 248, 2018.   DOI
10 X. J. Cai, J. J. Zhang, H. Liang, L. Wang and Q. D. Wu., "An Ensemble Bat Algorithm for Large-scale Optimization," International Journal of Machine Learning and Cybernetics, vol.10, no.11, pp.3099-3113, 2019.   DOI
11 W. R. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, "Energy-efficient communication protocol for wireless sensor networks," in Proc. of the 33rd Hawaii International Conference on System Sciences, January 7, 2000.
12 P. Shunmuga Perumal, V. Rhymend Uthariaraj and V. R. Elgin Christo, "WSN Lifetime Analysis: Intelligent UAV and Arc Selection Algorithm for Energy Conservation in Isolated Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 9, no. 3, pp. 901-920, 2015.   DOI
13 K. Daehee, K. Dongwan and A. Sunshin, "Communication Pattern Based Key Establishment Scheme in Heterogeneous Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 10, no. 3, pp. 1249-1272, 2016.   DOI
14 Z. Q. Qin, B. X. Lu, M. Zhu, L. Sun and L. Shu, "Optimized Charging in Large-Scale Deployed WSNs with Mobile Charger," KSII Transactions on Internet and Information Systems, vol. 10, no. 12, pp. 5307-5327, 2016.   DOI
15 T. A. Z. Raza, "A bacterial foraging-based batch scheduling model for distributed systems," International Journal of Bio-Inspired Computation, vol. 11, no. 1, pp. 16-26, 2018.   DOI
16 M. N. Omidvar, X. Li, Y. Mei and X. Yao, "Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization," IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 378-393, 2014.   DOI
17 W. Heinzelman, "Adaptive Protocols for Information Dissemination in Wireless Sensor Networks," in Proc. of the 5th annual ACM/IEEE international conference on Mobile computing and networking, pp.174-185, August 15 - 19, 1999.
18 O. Younis and S. Fahmy, "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks," IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 366-379, 2004.   DOI
19 X. J. Cai, Y. Q. Sun, Z. H. Cui, W. S. Zhang and J. J. Chen, "Optimal LEACH Protocol with Improved Bat Algorithm in Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 13, no. 5, pp. 2469-2490, 2019.   DOI
20 N. G. Kumar and D. Chetna, "ESO-LEACH: PSO Based Energy Efficient Clustering in LEACH," Journal of King Saud University - Computer and Information Sciences, 2018.
21 D. B. Jourdan and O. L. D. Weck, "Layout optimization for a wireless sensor network using a multi-objective genetic algorithm," in Proc. of 59th IEEE Vehicular Technology Conference, pp. 2466-2470, May 17-19, 2004.
22 K. P. Ferentinos and T. A. Tsiligiridis, "Adaptive design optimization of wireless sensor networks using genetic algorithms," Computer Networks, vol. 51, no. 4, pp. 1031-1051, 2007.   DOI
23 J. Wang and J. J. Yuan, "A high-efficient multi-deme genetic algorithm with better load-balance," International Journal of Computing Science and Mathematics, vol. 9, no. 3, pp. 240-246, 2018.   DOI
24 P. H. Wang, J. R. Huang, Z. H. Cui, L. P. Xie and J. J. Chen, "A Gaussian Error Correction Multi-Objective Positioning Model with NSGA-II," Concurrency and Computation Practice and Experience, 2019.
25 W. Shi, S. Y. Liu and Z. H. Zhang, "A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network," KSII Transactions on Internet and Information Systems, vol. 9, no. 9, 2015.
26 Z. H. Cui, Y. Chang, J. J. Zhang, X. J. Cai and W. S. Zhang, "Improved NSGA-III with selection-and-elimination operator," Swarm and Evolutionary Computataion, vol. 49, pp. 23-33, 2019.   DOI
27 X. J. Cai, H. Wang, Z. H. Cui, J. H. Cai, Y. Xue and L. Wang, "Bat algorithm with triangle-flipping strategy for numerical optimization," International Journal of Machine Learning and Cybernetics, vol. 9, no. 2, pp. 199-215, 2018.   DOI
28 Asaduzzaman and H. Y. Kong, "Code Combining Cooperative Diversity in Long-haul Transmission of Cluster based Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 5, no. 7, pp. 1293-1310, 2011.   DOI
29 J. Zhang, H. Yan, Y. Cui, H. Rong and J. P. Wang, "LEACH-EO: A More Energy-Efficient LEACH Protocol for WSN," Applied Mechanics and Materials, vol. 644-650, pp. 3108-3111, 2014.   DOI
30 B. a. Attea and E. A. Khalil, "A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks," Applied Soft Computing Journal, vol. 12, no. 7, pp. 1950-1957, 2012.   DOI
31 Y. P. Liu, D. W. Gong, J. Sun and Y. C. Jin, "A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy," IEEE Transactions on Cybernetics, vol. 47, no. 9, pp. 2689-2702, 2017.   DOI
32 J. J. Zhang, F. Xue, X. J. Cai, Z. H. Cui, Y. Chang, W. S. Zhang and W. Z. Li, "Privacy protection based on many-objective optimization algorithm," Concurrency and Computation Practice and Experience, vol. 31, no. 20, 2019.
33 Z. H. Cui, J. J. Zhang, Y. C. Wang, Y. Cao, X. J. Cai, W. S. Zhang and J. J. Chen, "A pigeon-inspired optimization algorithm for many-objective optimization problems," SCIENCE CHINA Information Sciences, vol. 62, no. 7, p. 070212, 2019.   DOI
34 M. Abdel-Baset, Y. Q. Zhou and M. Ismail, "An improved cuckoo search algorithm for integer programming problems," International Journal of Computing Science and Mathematics, vol. 9, no. 1, pp. 66-81, 2018.   DOI
35 W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on Wireless Communication, vol. 1, no. 4, pp. 660-670, 2002.   DOI
36 Q. F. Zhang and L. Hui, "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition," IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, 2007.   DOI
37 Q. Z. Lin, S. B. Liu, Q. L. Zhu, C. Y. Tang, R. Z. Song, J. Y. Chen, C. A. C. Coello, K. C. Wong and J. Zhang, "Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems," IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 32-46, 2018.   DOI
38 M. Li, S. Yang and X. Liu, "Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization," IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 348-365, 2014.   DOI
39 H. Z. Wu, Y. Q. Zhou and Q. F. Luo, "Hybrid symbiotic organisms search algorithm for solving 0-1 knapsack problem," International Journal of Bio-Inspired Computation, vol. 12, no. 1, pp. 25-53, 2018.
40 K. Deb, L. Thiele, M. Laumanns and E. Zitzler, "Scalable Test Problems for Evolutionary Multiobjective Optimization," Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, pp. 105-145, 2005.
41 K. Li, K. Deb, Q. Zhang and S. Kwong, "An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition," IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 694-716, 2015.   DOI
42 Z. H. Cui, L. Du, P. H. Wang, X. J. Cai and W. S. Zhang, "Malicious code detection based on CNNs and multi-objective algorithm," Journal of Parallel and Distributed Computing, vol. 129, pp. 50-58, 2019.   DOI
43 K. Deb and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints," IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, 2014.   DOI
44 X. J. Bi and C. Wang, "A niche-elimination operation based NSGA-III algorithm for many-objective optimization," Applied Intelligence, vol. 48, no. 1, pp. 118-141, 2018.   DOI
45 Y. Yuan, H. Xu, B. Wang, B. Zhang and X. Yao, "Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers," IEEE Transactions on Evolutionary Computation, vol. 20, no. 2, pp. 180-198, 2016.   DOI
46 J. Bader and E. Zitzler, "HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization," Evolutionary Computation, vol. 19, no. 1, pp. 45-76, 2011.   DOI
47 E. Zitzler and S. Kunzli, "Indicator-Based Selection in Multiobjective Search," Lecture Notes in Computer Science, vol. 3242, pp. 832-842, 2004.