Volume 14, Issue 1 (3-2022)                   itrc 2022, 14(1): 1-12 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ghorbanvirdi M, Mazinani S M. Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in Wireless Sensor Network. itrc. 2022; 14 (1) :1-12
URL: http://ijict.itrc.ac.ir/article-1-525-en.html
1- Department of Electrical Engineering Imam Reza International University Mashhad, Iran
2- Department of Electrical Engineering Imam Reza International University Mashhad, Iran , smajidmazinani@imamreza.ac.ir
Abstract:   (163 Views)
The most important challenge in wireless sensor networks is to extend the network lifetime, which is directly related to the energy consumption. Clustering is one of the well-known energy-saving solutions in WSNs.  To put this in perspective, the most studies repeated cluster head selection methods for clustering in each round, which increases the number of sent and received messages. what's more, inappropriate cluster head selection and unbalanced clusters have increased energy dissipation. To create balanced clusters and reduce energy consumption, we used a centralized network and relay nodes, respectively. Besides, we applied a metaheuristic algorithm to select the optimal cluster heads because classical methods are easily trapped in local minimum. In this paper, the Grey Wolf Optimizer(GWO), which is a simple and flexible algorithm that is capable of balancing the two phases of exploration and exploitation is used. To prolong the network lifetime and reduce energy consumption in cluster head nodes, we proposed a centralized multiple clustering based on GWO that uses both energy and distance in cluster head selection. This research is compared with classical and metaheuristic algorithms in three scenarios based on the criteria of "Network Lifetime", "Number of dead nodes in each round" and "Total Remaining Energy(TRE) in the cluster head and relay nodes. The simulation results show that our research performs better than other methods. In addition, to analyze the scalability, it has been evaluated in terms of "number of nodes", "network dimensions" and "BS location". Regarding to the results, by rising 2 and 5 times of these conditions, the network performance is increased by 1.5 and 2 times, respectively.
Full-Text [PDF 1073 kb]   (60 Downloads)    
Type of Study: Research | Subject: Communication Technology

References
1. [1] J. N. Al-Karaki and a E. Kamal, “Routing techniques in wireless sensor networks: a survey,” IEEE Wirel. Commun., vol. 11, no. 6, pp. 6–28, 2004. [2] K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad Hoc Networks, vol. 3, no. 3, pp. 325–349, 2005. [3] V. Kumar, J. K. Chhabra, and D. Kumar, “Grey Wolf Algorithm-Based Clustering Technique,” J. Intell. Syst., vol. 26, no. 1, 2016. [4] M. Mirzaie and S. M. Mazinani, “MCFL: an energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network,” Wirel. Networks, pp. 1–16, 2017. [5] S. Mirjalili, J. S. Dong, and A. Lewis, Nature- Inspired Optimizers. Springer, Cham, 2019. [6] S. K. Shandilya, S. Shandilya, and A. K. Nagar, Advances in Nature-Inspired Computing and Applications, 1st ed. Springer International Publishing, 2019. [7] Y. Zhou, N. Wang, and W. Xiang, “Clustering Hierarchy Protocol in Wireless Sensor Networks Using an Improved PSO Algorithm,” IEEE Access, vol. 5, no. c, pp. 2241–2253, 2017. [8] M. Ramadas and A. Abraham, “Metaheuristics and Data Clustering,” in Metaheuristics for Data Clustering and Image Segmentation, 2019, pp. 7–55. [9] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014. [10] H. Faris, I. Aljarah, M. Azmi, and A. S. Mirjalili, “Grey wolf optimizer : a review of recent variants and applications,” Neural Comput. Appl., vol. 30, no. 2, pp. 413–435, 2017. [11] S. J. Nanda and G. Panda, “A survey on nature inspired metaheuristic algorithms for partitional clustering,” Swarm and Evolutionary Computation, vol. 16. Elsevier, pp. 1–18, 2014. [12] M. Panda and B. Das, “Grey Wolf Optimizer and Its Applications : A Survey,” in Proceedings ofthe Third International Conference on Microelectronics, Computing and Communication Systems, Lecture, 2019. [13] P. Singh and M. Satvir, “Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks,” Soft Comput., vol. 21, no. 22, pp. 6699–6712, 2016. [14] A. R. Pagar and D. C. Mehetre, “A Survey on Energy Efficient Sleep Scheduling in Wireless Sensor Network,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 1. pp. 557–562, 2015. [15] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proceedings of the 33rd Hawaii International Conference on System Sciences, 2000, vol. 1, p. 10. [16] R. Sharma, V. Vashisht, A. V. Singh, and S. Kumar, “Analysis of Existing Clustering Algorithms for Wireless Sensor Networks,” in System Performance and Management Analytics, P. K. Kapur, Y. Klochkov, A. K. Verma, and G. Singh, Eds. Singapore: Springer Singapore, 2019, pp. 259–277. [17] A. Manjeshwar and D. P. Agrawal, “TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Network,” in Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001, 2001. [18] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wirel. Commun., vol. 1, no. 4, pp. 660–670, 2002. [19] S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-efficient gathering in sensor information systems,” in Proceedings, IEEE Aerospace Conference, 2002, vol. 3, pp. 1125–1130. [20] O. Younis and S. Fahmy, “HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks,” IEEE Trans. Mob. Comput., vol. 3, no. 4, pp. 366–379, 2004. [21] N. Al-humidi and G. V Chowdhary, “Energy-Aware Approach for Routing Protocol by Using Centralized Control Clustering Algorithm in Wireless Sensor Networks,” in Computing, Communication and Signal Processing, 2019, pp. 261–274. [22] Y. Zhong, Z. Huang, and Y. Xu, “An Energy Optimal Clustering Algorithm,” J. Phys. Conf. Ser., vol. 1229, 2019. [23] M. S. Azizi and M. L. HASNAOUI, “An energy efficient clustering protocol for homogeneous and heterogeneous wireless sensor network,” 2019. [24] T. Ahmad, M. Haque, and A. M. Khan, An Energy-Efficient Cluster Head Selection Using Artificial Bees Colony Optimization for Wireless Sensor Networks. Springer International Publishing, 2019. [25] S. Z. Selim and K. Alsultan, “A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM,” Pattern Recognit., vol. 24, no. 10, pp. 1003–1008, 1991. [26] M. Dorigo and G. Di Caro, “Ant colony optimization: A new meta-heuristic,” in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999, vol. 2, pp. 1470–1477. [27] R. Eberhart and J. Kennedy, “A New Optimizer Using Particle Swarm Theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43. [28] J. H. Holland, “Genetic Algorithms,” Sci. Am., vol. 267, no. 1, pp. 66–73, 1992. [29] S. Sharma and E. A. Bindle, “Comparison of Routing Algorithm of Optimized and Non-Optimized PSO in Wireless Sensor Networks,” Int. J. Eng. Sci. Comput., vol. 9, no. 3, pp. 1–6, 2019. [30] Y. Shi and R. Eberhart, “A Modified Particle Swarm Optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998, pp. 69–73. [31] P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, “An ant colony approach for clustering,” Anal. Chim. Acta, vol. 509, no. 2, pp. 187–195, 2004. [32] D. KARABOGA, “An idea based on honey bee swarm for numerical optimization,” Tech. report-tr06, Erciyes Univ. Eng. Fac. Comput. Eng. Dep., vol. 200, pp. 1–10, 2005. [33] P. Nayak, “Genetic Algorithm Based Clustering Approach for Wireless Sensor Network to Optimize Routing Techniques,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 2017, pp. 373–380. [34] D. Reddy, E. Mahesh, C. Kongara, and R. Cheruku, “A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs,” Wirel. Pers. Commun., no. 1/2019, 2018. [35] V. K. Arora, V. Sharma, and M. Sachdeva, “ACO optimized self ‑ organized tree ‑ based energy balance algorithm for wireless sensor network,” J. Ambient Intell. Humaniz. Comput., vol. 0, no. 0, p. 0, 2019. [36] P. S. Mann and S. Singh, “Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks,” Netw. Comput. Appl., vol. 83, no. c, pp. 40–52, 2017. [37] S. Mirjalili, “How effective is the Grey Wolf optimizer in training multi-layer perceptrons,” Appl. Intell., vol. 43, no. 1, pp. 150–161, Jul. 2015. [38] A. Chandanse, P. Bharane, S. Anchan, and H. Patil, “Performance Analysis of LEACH Protocol in Wireless Sensor Network,” in 2nd International Conference on Advances in Science & Technology (ICAST-2019), 2019 [39] Garg, A. and Kumar Agarwal, S. (2012). Dynamic Stability Enhancement of Power Transmission System Using Artificial Neural Network Controlled Static Var Compensator. International Journal of Computer Applications, 53(9), pp.21-29. [40] Energy-Efficient Routing Protocol for Wireless Sensor Networks Based on Improved Grey Wolf Optimizer. (2018). KSII Transactions on Internet and Information Systems

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.