Improved Low-Power Coverage with Variable Radius in Voronoi-Based Wireless Sensor Networks, Firefly Swarm Optimization-K-Means Algorithm

yari, hamid; esmaeilpour, mansour; SHirmohammadi, Mohammad Mehdi

[Home ] [Archive]

[ فارسی ]

Main Menu

Home

Journal Information

Articles archive

For Authors

For Reviewers

Registration

Contact us

Site Facilities

Search in website

Receive site information

Volume 3, Issue 6 (1-2025)

3 2025, 3(6): 11-20

Back to browse issues page

Improved Low-Power Coverage with Variable Radius in Voronoi-Based Wireless Sensor Networks, Firefly Swarm Optimization-K-Means Algorithm

Hamid Yari

, Mansour Esmaeilpour

, Mohammad Mehdi SHirmohammadi

Computer Engineering Department, Hamedan Branch, Islamic Azad Universit, Hamedan, Iran

Abstract: (180 Views)

This research aims to optimize low-power coverage with variable radius in Voronoi-based wireless sensor networks. The Firefly Swarm Optimization-K-Means algorithm is investigated in this study. Several experiments will be conducted and compared with others, and the performance of the proposed approach will be analyzed based on several experiments. We apply the GSO algorithm to converge the mobile sensor nodes at the center of a Voronoi cell to reduce the extra area of the sensor networks. After the initial deployment of the mobile sensor nodes, the rearrangement of those sensors may improve the coverage of mobile wireless sensor networks and eliminate coverage holes in the region of interest. In the proposed mechanism, the Firefly Swarm Optimization algorithm, K-Means algorithm, and Voronoi cell structure are used for better trade-off between coverage area and energy consumption in the context of wireless sensor networks. The simulation of the present research has been done using Matlab simulation software version 17. For this purpose, a variety of sensor nodes from 100 to 1000 are randomly distributed in the region of interest (150×150) square meters. It is assumed that each sensor has an initial energy of (1-5) Joules, the energy threshold of each sensor is considered 0.02 Joules, the coverage percentage is determined according to different population sizes from 100 sensor nodes to 1000 nodes, and different cases of the number of iterations from 5000 to 80000 cycles of experiments have been performed. The results obtained are as follows: At the beginning of the work, the number of holes is 35, for the code execution, 5 iterations with different number of repetitions were performed. For example, iteration period 3 and repetition number 3 times, with a total distance of 16359.5 was obtained. By continuing the process, the best total distance was obtained in the amount of 11682.3. The number of holes was reduced to 7, and due to the use of the multi-hop mechanism, the amount of energy efficiency increased by about 0.50.

Article number: 2

Full-Text [PDF 1206 kb] (136 Downloads)

Type of Study: Research | Subject: General
Received: 2024/10/16 | Accepted: 2024/12/21 | Published: 2025/02/8

References

1. H.T.T. Binh, N.T. Hanh, L.V. Quan, N. Dey, Improved Cuckoo search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in WSNs, Neural Comput. Appl. 30 (7) (2018) 2305–2317.

2. A. Chowdhury, D. De, MSLG-RGSO: movement score based limited grid-mobility approach using reverse Glowworm Swarm Optimization algorithm for mobile wireless sensor networks, Ad Hoc Netw. 106 (2020), 102191.

3. A.Chowdhury, D. De, FIS-RGSO: dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks, Comput. Commun. 163 (2020) 12–34.

4. S.K. Gupta, P. Kuila, P.K. Jana, Genetic algorithm approach for k-coverage and m-connected node placement in target-based wireless sensor networks, Comput. Electr. Eng. 56 (2016) 544–556.

5. G. Sharma, A. Kumar, Improved range-free localization for three-dimensional wireless sensor networks using genetic algorithm, Comput. Electr. Eng. 72 (2018) 808–827.

6. Y. Meng, W. Aimin, G. Sun, Y. Zhang, Deploying charging nodes in wireless rechargeable sensor networks based on improved firefly algorithm, Comput. Electr. Eng. 72 (2018) 719–731.

7. M. Fahad, F. Aadil, Z. Rehman, S. Khan, P.A. Shah, K. Muhammad, J. Lloret, H. Wang, J.W. Lee, I. Mehmood, Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks, Comput. Electr. Eng. 70 (2018) 853–870

8. M. M. Shirmohammadi, M. Chahardoli, Wireless Sensor Networks. Hamadan: Islamic Azad University, Hamadan Branch, 2012. ISBN: 978-964-543-103-5.

9. M. M. Shirmohammadi, M. Chhardoli, and K. Faez, “CHEFC: Cluster Head Election with Full Coverage in Wireless Sensor Networks,” in *2009 IEEE 9th Malaysia International Conference on Communications (MICC)*, Kuala Lumpur, Malaysia, Dec. 2009, pp. doi: 10.1109/MICC.2009.5431389.

10. S GUPTA, P SINHA. (2014), “Overview of Wireless Sensor Network: A Survey”. International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 1, ISSN (Print) : 2319-5940. ISSN (Online) : 2278-1021

11. H. Zargari, M. Chahardoli, and M. M. Shirmohammadi, “Balanced Clustering with Full Coverage in Heterogeneous Wireless Sensor Networks,” *Advanced Materials Research*, vol. 433, pp. 3458-3462, Feb. 2012. doi: 10.4028/www.scientific.net/AMR.433-440.3458.

12. M. M. Shirmohammadi and M. Chahhardi, “ESEP: A Stable Cluster Head Selection Protocol in Heterogeneous Wireless Sensor Networks with Sleep and Wake Modes,” in *Proceedings of the Computer, Electrical and Information Technology Conference*, Islamic Azad University, Hamadan Branch, Hamadan, vol. 1, 2007.

13. H. Alemdar and C. Ersoy, "Wireless sensor networks for healthcare: a survey," J. of Computer Networks, vol. 54, no. 15, pp. 2688-2710,Oct. 2010.

14. V. L. Boginski, C. W. Commander, P. M. Pardalos, and Y. Ye, Sensors: Theory, Algorithms, and Applications, Springer Optimization and Its Applications, Springer-Verlag, New York,2011.

15. S. K. Das, G. Ghidini, A. Navarra, and C. M. Pinotti, "Localization and scheduling protocols for actor-centric sensor networks," J. of Networks, vol. 59, no. 3, pp. 299-319, May 2012.

16. M. K. Rafsanjani, M. Mirhoseini, and R. Nourizadeh, "A multiobjective evolutionary algorithm for improving energy consumption in wireless sensor networks," Bull. Transilv. Univ. Brasov, vol. 6, no. 2, pp. 107-116, Jan. 2013.

17. J. Park, S. Lee, and S. Yoo, "Time slot assignment for converge cast in wireless sensor networks," J. of Parallel Distrib. Comput, vol. 83, pp. 70-82, Sept. 2015.

18. F. Carrabs, R. Cerulli, C. D'Ambrosio, M. Gentili, and A. Raiconi, "Maximizing lifetime in wireless sensor networks with multiple sensor families," Computers & Operations Research, vol. 60, pp. 121-137, Aug. 2015.

19. M. Rebaia, M. Leberreb, H. Snoussic, F. Hnaiend, and L. Khoukhie, "Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks," Computers & Operations Research, vol. 59, pp. 11-21, Jul. 2015.

20. K. P. Ferentinos and T. A. Tsiligiridis, "Adaptive design optimization of wireless sensor networks using genetic algorithms," J. of Computer Networks, vol. 51, no. 4, pp. 1031-1051, Mar. 2007.

21. K. P. Ferentinos and T. A. Tsiligiridis, "A memetic algorithm for optimal dynamic design of wireless sensor networks," J. of Computer Communications, vol. 33, no. 2, pp. 250-258, Feb. 2010.

22. M. Borouomand zadeh, M. Hashemi and M. Mohmedi )2013(, “Target Tracking Techniques for Wireless Sensor Networks” International Research Journal of Applied and Basic Sciences, Vol. 5, No. 7, pp. 820-823.

23. M. M. Shirmohammadi, K. Faez, and M. Chhardoli, “Leader election with load balancing energy in wireless sensor network,” in *2009 WRI International Conference on Communications and Mobile Computing*, vol. 2, Jan. 2009, pp. 106-110. doi: 10.1109/CMC.2009.227

24. S. Z. Majidian and M. M. Shirmohammadi, “Clustering and Routing in Wireless Sensor Networks Using Multi-Objective Cuckoo Search and Game Theory,” *Electronic and Cyber Defense*, vol. 10, no. 3, pp. 11-20, Dec. 2022.

Add your comments about this article

Mendeley

Zotero

RefWorks

yari H, esmaeilpour M, SHirmohammadi M M. Improved Low-Power Coverage with Variable Radius in Voronoi-Based Wireless Sensor Networks, Firefly Swarm Optimization-K-Means Algorithm. 3 2025; 3 (6) : 2
URL: http://jiis.iauh.ac.ir/article-1-36-en.html

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

Volume 3, Issue 6 (1-2025)

Back to browse issues page

Persian site map - English site map - Created in 0.05 seconds with 36 queries by YEKTAWEB 4704