پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 657 |
| تعداد مقالات | 9,642 |
| تعداد مشاهده مقاله | 68,665,566 |
| تعداد دریافت فایل اصل مقاله | 48,144,278 |
Using Intra-Cluster Elections to Reduce Computational Cost of LEACH-C Clustering Algorithm in Wireless Sensor Networks | ||
| Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 4، شماره 4 - شماره پیاپی 18، اسفند 2024، صفحه 23-31 اصل مقاله (2.28 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2025.36729.1199 | ||
| نویسندگان | ||
| Arman Amjadian؛ Hamid Meghdadi* ؛ Ali Shahzadi | ||
| Department of Telecommunications, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 09 بهمن 1403، تاریخ بازنگری: 25 فروردین 1404، تاریخ پذیرش: 26 شهریور 1404 | ||
| چکیده | ||
| Wireless Sensor Networks (WSNs) play a critical role in diverse applications, ranging from environmental monitoring to military surveillance. Clustering techniques are essential for optimizing energy consumption and improving data transmission efficiency in WSNs. This paper introduces the Intra-Cluster Election (ICE) technique, which enhances centralized clustering protocols within the LEACH-C (Low-Energy Adaptive Clustering Hierarchy-Centralized) framework. ICE improves cluster head selection by optimizing the cost function value and reducing convergence iterations. Although the improvements in energy efficiency and data delivery rates are modest compared to LEACH-C, primarily because its cost function does not account for the distance between cluster heads and the base station, ICE demonstrates significant advantages in optimization. The simulation results show that ICE substantially improves cluster head selection, leading to more efficient network operations across various WSN scenarios. It achieves approximately 12–20% improvement in the cost function compared to LEACH-C and reduces the number of required iterations by a factor ranging from 3 to 100, depending on the network conditions. | ||
| کلیدواژه‌ها | ||
| Wireless Sensor Networks (WSNs)؛ Intra-Cluster Election (ICE)؛ Cluster Head Selection؛ Centralized Clustering Protocols؛ Low-Energy Adaptive Clustering Hierarchy (LEACH)؛ LEACH-Centralized (LEACH-C)؛ Optimization Techniques؛ Network Efficiency | ||
| مراجع | ||
|
[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Comput. Netw., vol. 38, no. 4, pp. 393–422, Mar. 2002, doi: 10.1016/S1389-1286(01)00302-4.
[2] S. Khani and M. Hayati, “An ultra-high sensitive plasmonic refractive index sensor using an elliptical resonator and MIM waveguide,” Superlattices Microstruct., vol. 156, p. 106970, Aug. 2021, doi: 10.1016/j.spmi.2021.106970.
[3] S. Khani and M. Hayati, “Optical biosensors using plasmonic and photonic crystal band-gap structures for the detection of basal cell cancer,” Sci. Rep., vol. 12, no. 1, p. 5246, Mar. 2022, doi: 10.1038/s41598-022-09213-w.
[4] S. Khani and M. Afsahi, “Optical Refractive Index Sensors Based on Plasmon-Induced Transparency phenomenon in a Plasmonic Waveguide Coupled to Stub and Nano-disk Resonators,” Plasmonics, vol. 18, no. 1, pp. 255–270, Feb. 2023, doi: 10.1007/s11468-022-01772-y.
[5] R. Mitra and D. Nandy, “A Survey on Clustering Techniques for Wireless Sensor Network,” Int. J. Res. Comput. Sci., vol. 2, no. 4, pp. 51–57, Jul. 2012, doi: 10.7815/ijorcs.24.2012.040.
[6] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA: IEEE Comput. Soc, 2000, p. 10. doi: 10.1109/HICSS.2000.926982.
[7] 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, Oct. 2004, doi: 10.1109/TMC.2004.41.
[8] A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Comput. Commun., vol. 30, no. 14–15, pp. 2826–2841, Oct. 2007, doi: 10.1016/j.comcom.2007.05.024.
[9] I. Daanoune, B. Abdennaceur, and A. Ballouk, “A comprehensive survey on LEACH-based clustering routing protocols in Wireless Sensor Networks,” Ad Hoc Netw., vol. 114, p. 102409, Apr. 2021, doi: 10.1016/j.adhoc.2020.102409.
[10] 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, Oct. 2002, doi: 10.1109/TWC.2002.804190.
[11] N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey,” IEEE Commun. Surv. Tutor., vol. 15, no. 2, pp. 551–591, 2013, doi: 10.1109/SURV.2012.062612.00084.
[12] Q. Wang, D. Lin, P. Yang, and Z. Zhang, “A Fuzzy-logic Based Energy-efficient Clustering Algorithm for the Wireless Sensor Networks,” in 2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia: IEEE, Sep. 2018, pp. 1–6. doi: 10.23919/SOFTCOM.2018.8555848.
[13] M. Hatamian, S. S. Ahmadpoor, S. Berenjian, B. Razeghi, and H. Barati, “A centralized evolutionary clustering protocol for wireless sensor networks,” in 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Dallas-Fortworth, TX, USA: IEEE, Jul. 2015, pp. 1–6. doi: 10.1109/ICCCNT.2015.7395190.
[14] S. K. Gupta and P. K. Jana, “Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach,” Wirel. Pers. Commun., vol. 83, no. 3, pp. 2403–2423, Aug. 2015, doi: 10.1007/s11277-015-2535-7.
[15] H. Zhang, S. Zhang, and W. Bu, “A Clustering Routing Protocol for Energy Balance of Wireless Sensor Network based on Simulated Annealing and Genetic Algorithm,” Int. J. Hybrid Inf. Technol., vol. 7, no. 2, pp. 71–82, Mar. 2014, doi: 10.14257/ijhit.2014.7.2.08.
[16] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, “Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization,” in 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece: IEEE, 2007, pp. 1–5. doi: 10.1109/PIMRC.2007.4394521.
[17] S. M. Guru, S. K. Halgamuge, and S. Fernando, “Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks,” in 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia: IEEE, 2005, pp. 319–324. doi: 10.1109/ISSNIP.2005.1595599.
[18] B. Singh and D. K. Lobiyal, “A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks,” Hum.-Centric Comput. Inf. Sci., vol. 2, no. 1, p. 13, Dec. 2012, doi: 10.1186/2192-1962-2-13.
[19] P. C. S. Rao, P. K. Jana, and H. Banka, “A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks,” Wirel. Netw., vol. 23, no. 7, pp. 2005–2020, Oct. 2017, doi: 10.1007/s11276-016-1270-7.
[20] H. Ali, U. U. Tariq, M. Hussain, L. Lu, J. Panneerselvam, and X. Zhai, “ARSH-FATI: A Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks,” IEEE Syst. J., vol. 15, no. 2, pp. 2386–2397, Jun. 2021, doi: 10.1109/JSYST.2020.2986811.
[21] V. K. Chawra and G. P. Gupta, “Load Balanced Node Clustering scheme using Improved Memetic Algorithm based Meta-heuristic Technique for Wireless Sensor Network,” Procedia Comput. Sci., vol. 167, pp. 468–476, 2020, doi: 10.1016/j.procs.2020.03.256.
[22] M. Ahmad et al., “Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: memeWSN,” Wirel. Commun. Mob. Comput., vol. 2021, no. 1, p. 8875950, Jan. 2021, doi: 10.1155/2021/8875950.
[23] W. Zhang, Y. Lan, A. Lin, and M. Xiao, “An Adaptive Clustering Routing Protocol for Wireless Sensor Networks Based on a Novel Memetic Algorithm,” IEEE Sens. J., vol. 25, no. 5, pp. 8929–8941, Mar. 2025, doi: 10.1109/JSEN.2025.3526831.
[24] J. C. Bansal, H. Sharma, S. S. Jadon, and M. Clerc, “Spider Monkey Optimization algorithm for numerical optimization,” Memetic Comput., vol. 6, no. 1, pp. 31–47, Mar. 2014, doi: 10.1007/s12293-013-0128-0.
[25] E.-G. Talbi, Metaheuristics: From Design to Implementation. John Wiley & Sons, 2009.
[26] F. Glover, “Tabu Search—Part I,” ORSA J. Comput., vol. 1, no. 3, pp. 190–206, Aug. 1989, doi: 10.1287/ijoc.1.3.190.
[27] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 671–680, May 1983, doi: 10.1126/science.220.4598.671.
[28] R. S. Y. Elhabyan and M. C. E. Yagoub, “Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network,” J. Netw. Comput. Appl., vol. 52, pp. 116–128, Jun. 2015, doi: 10.1016/j.jnca.2015.02.004.
[29] I. H. Osman and G. Laporte, “Metaheuristics: A bibliography,” Ann. Oper. Res., vol. 63, no. 5, pp. 511–623, Oct. 1996, doi: 10.1007/BF02125421.
[30] A. Varga, “OMNeT++,” in Modeling and Tools for Network Simulation, K. Wehrle, M. Güneş, and J. Gross, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 35–59. doi: 10.1007/978-3-642-12331-3_3. | ||
|
آمار تعداد مشاهده مقاله: 30 تعداد دریافت فایل اصل مقاله: 42 |
||