پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شمارهها | 834 |
| تعداد مقالات | 8,015 |
| تعداد مشاهده مقاله | 14,852,484 |
| تعداد دریافت فایل اصل مقاله | 9,586,508 |
A Fuzzy Logic-Based Extremal Optimization Approach for Enhancing Energy Efficiency in Wireless Sensor Networks | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 23، شماره 2، خرداد و تیر 2026، صفحه 37-52 اصل مقاله (1.4 M) | ||
| نوع مقاله: Review Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2026.52619.9292 | ||
| نویسنده | ||
| shayesteh tabatabaei* | ||
| Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran. | ||
| چکیده | ||
| A wireless sensor network (WSN) consists of a collection of sensor nodes that collaboratively perform monitoring and data acquisition tasks. Considering the strict resource limitations of sensor nodes, achieving high energy efficiency is a critical requirement. In WSNs, it is essential to minimize data collection delay to ensure that sensed information remains current, while simultaneously maximizing the number of collected data samples to enhance accuracy and reliability. To address these conflicting objectives, this paper proposes a clustering-based routing protocol that simultaneously maximizes packet delivery, minimizes energy consumption, and reduces end-to-end delay. The proposed protocol integrates extremal optimization with fuzzy logic to dynamically form clusters, selecting cluster heads based on two primary criteria: residual energy and distance to the sink. The elected cluster heads then construct a minimum spanning tree (MST) to serve as an efficient multi-hop communication backbone toward the sink. The proposed method, termed the Extremal Optimization Fuzzy-Based Clustering Algorithm (EOFBCA), was implemented and evaluated using the OPNET 11.5 simulation platform. Performance is compared against three state-of-the-art protocols: AFSRP, BFOABMS, and NODIC. Simulation results demonstrate that EOFBCA achieves superior performance across multiple metrics, including energy consumption, end-to-end delay, throughput, packet delivery ratio, and signal-to-noise ratio. | ||
| کلیدواژهها | ||
| Wireless sensor networks؛ energy efficiency؛ clustering؛ extremal optimization؛ fuzzy logic؛ minimum spanning tree | ||
| مراجع | ||
|
[1] I. Abasıkele¸s-Turgut, O. G. Hafif, NODIC: A novel distributed clustering routing protocol in WSNs by using a time-sharing approach for CH election, Wireless Networks, 22(3) (2016), 1023-1034. https://doi.org/10.1007/ s11276-015-1045-6 [2] J. Amutha, S. Sharma, S. K. Sharma, An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for wireless sensor networks, Expert Systems with Applications, 203 (2022), 117334. https://doi.org/10.1016/j.eswa.2022.117334 [3] O. Banimelhem, S. Khasawneh, GMCAR: Grid-based multipath with congestion avoidance routing protocol in wireless sensor networks, Ad Hoc Networks, 10(7) (2012), 1346-1361. https://doi.org/10.1016/j.adhoc.2012.03.015 [4] S. Boettcher, A. G. Percus, Optimization with extremal dynamics, Physical Review Letters, 86 (2001), 5211. https: //doi.org/10.1103/PhysRevLett.86.5211 [5] E. Fasolo, M. Rossi, J. Widmer, M. Zorzi, In-network aggregation techniques for wireless sensor networks: A survey, IEEE Wireless Communications, 14(2) (2007), 70-87. https://doi.org/10.1109/MWC.2007.358967 [6] S. Gorgich, S. Tabatabaei, Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (wireless sensor networks), Wireless Personal Communications, 119(3) (2021), 1935-1955. https://doi. org/10.1007/s11277-021-08312-7 [7] W. B. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications, 1(4) (2002), 660-670. https://doi.org/ 10.1109/TWC.2002.804190 [8] S. Jannu, P. K. Jana, Energy efficient grid based clustering and routing algorithms for wireless sensor networks, in 2014 Fourth International Conference on Communication Systems and Network Technologies, (2014), 63-68. https://doi.org/10.1109/CSNT.2014.245 [9] P. Karthick, C. Palanisamy, Optimized cluster head selection using krill herd algorithm for wireless sensor network, Automatika: ˘Casopis Za Automatiku, Mjerenje, Elektroniku, Ra˘cunarstvo i Komunikacije, 60(3) (2019), 340-348. https://doi.org/10.1080/00051144.2019.1637174 [10] S. Kaviarasan, R. Srinivasan, Developing a novel energy efficient routing protocol in WSN using adaptive remora optimization algorithm, Expert Systems with Applications, 244 (2024), 122873. https://doi.org/10.1016/j. eswa.2023.122873 [11] N. Malisetti, V. K. Pamula, Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm, Microprocessors and Microsystems, 93 (2022), 104593. https://doi.org/10.1016/j.micpro.2022.104593 [12] M. Manoharan, B. Subramani, P. Ramu, An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering, Sustainable Computing: Informatics and Systems, 43 (2024), 101003. https://doi.org/10.1016/j.suscom.2024.101003 [13] R. F. Mansour, et al., Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks, Computer Networks, 212 (2022), 109049. https://doi.org/10.1016/j.comnet.2022. 109049 [14] X. Min, S. Wei-Ren, J. Chang-Jiang, Z. Ying, Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks, AEU-International Journal of Electronics and Communications, 64(4) (2010), 289-298. https://doi.org/10.1016/j.aeue.2009.01.004 [15] A. M. Rahmani, et al., A routing approach based on combination of gray wolf clustering and fuzzy clustering and using multi-criteria decision making approaches for WSN-IoT, Computers and Electrical Engineering, 122 (2025), 109946. https://doi.org/10.1016/j.compeleceng.2024.109946 [16] W. Shafik, Wireless sensor network-assisted fuzzy sink based model, Energy, 333 (2025), 137512. https://doi. org/10.1016/j.energy.2025.137512 [17] K. Sohrabi, J. Gao, V. Ailawadhi, G. J. Pottie, Protocols for self-organization of a wireless sensor network, IEEE Personal Communications, 7(5) (2000), 16-27. https://doi.org/10.1109/98.878532 [18] S. Tabatabaei, Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink, PLOS One, 17(3) (2022), e0265113. https://doi.org/10.1371/journal.pone. 0265113 [19] M. A. Tawfeek, I. Alrashdi, M. Alruwaili, F. M. Talaat, A fuzzy multi-objective framework for energy optimization and reliable routing in wireless sensor networks via particle Swarm Optimization, Computers, Materials and Continua, 83(2) (2025), 2773-2792. https://doi.org/10.32604/cmc.2025.061773 [20] C. Vimalarani, C. T. Selvi, B. Gopinathan, T. Kalaivani, Improving energy efficiency in WSN through adaptive memetic-based clustering and routing for resource management, Sustainable Computing: Informatics and Systems, 45 (2025), 101073. https://doi.org/10.1016/j.suscom.2024.101073 | ||
|
آمار تعداد مشاهده مقاله: 64 تعداد دریافت فایل اصل مقاله: 84 |
||