-ارائه روشی به‌منظور کاهش مصرف انرژی در شبکه‌های حسگر بی‌سیم با استفاده از الگوریتم خوشه‌بندی DBSCAN

نوع مقاله : مقاله پژوهشی

نویسنده

کارشناسی ارشد کامپیوتر- نرم افزار، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران. رایانامه: rezamolae4@gmail.com

چکیده

در این تحقیق، به ارزیابی روش‌های خوشه‌بندی انرژی کارآمد در شبکه‌های حسگر بی‌سیم می‌پردازیم. نتایج حاصل از این پژوهش حاکی از آن است که خوشه‌بندی توسط DBSCAN، امتیاز کارایی بالاتری نسبت به سایر روش‌های خوشه‌بندی به دست می‌آورد. به‌طوری‌که DBSCAN امتیاز کارایی ۹۹٪ را به دست آورد اما الگوریتم K-Means، امتیاز کارایی ۷۶٪ را به دست آورد. همچنین انرژی باقیمانده در شبکه پس از اتمام شبیه‌سازی در مسیریابی با پروتکل جدید حدود ۱۱٪ بیشتر از مسیریابی EEHC و حدود ۹٪ بیشتر از مسیریابی با پروتکل LCA است. اگر طول عمر شبکه را در زمان خاموش شدن اولین گره در شبکه در نظر بگیریم در پروتکل جدید اولین گره ۶ ثانیه دیرتر از پروتکل EEHC و ۱۲ ثانیه دیرتر از پروتکل LCA‌ خاموش می‌شود. این بدین معناست که به‌طور میانگین حدود ۱۰٪ طول عمر شبکه با پروتکل جدید افزایش‌یافته است. پروتکل I-LEACH راندمان انرژی و طول عمر را باکار بیشتر در همان ساختارها در مقایسه با پروتکل معمولی LEACH بهبود می‌بخشد.

کلیدواژه‌ها

عنوان مقاله [English]

provide a method to reduce energy consumption in wireless sensor networks using DBSCAN clustering algorithm

نویسنده [English]

  • reza molaee fard
Master of Computer-Software, Dezful Branch, Islamic Azad University, Dezful, Iran. Email: rezamolae4@gmail.com
چکیده [English]

In this research, we evaluate energy efficient clustering methods in wireless sensor networks. The results of this study indicate that clustering by DBSCAN has a higher efficiency score than other clustering methods. The DBSCAN scored 99%, but the K-Means algorithm scored 76%. Also, the energy remaining in the network after the simulation is completed in routing with the new protocol is about 11% more than routing with EEHC and about 9% more than routing with LCA protocol. If we consider the lifespan of the network when the first node in the network is turned off, in the new protocol, the first node shuts down 6 seconds later than the EEHC protocol and 12 seconds later than the LCA‌ protocol. This means that on average, about 10% of network life has been increased with the new protocol. The I-LEACH protocol improves energy efficiency and longevity by working more in the same structures than the conventional LEACH protocol.

کلیدواژه‌ها [English]

  • Wireless sensor network
  • clustering
  • energy reduction
  • I-LEACH algorithm

مراجع

Al-Baz, A., & El-Sayed, A. (2015, October). Energy-aware enhancement of leach protocol in wireless sensor network. In 5th International Conference on Computer Theory and Applications (ICCTA 2015) (Vol. 10). DOI: https://doi.org/10.1109/ICCTA37466.2015.9513448
AL-BAZ, A., & EL-SAYED, A. (2017). Cluster head selection enhancement of LEACH protocol in wireless sensor network. Menoufia Journal of Electronic Engineering Research, 26(1), 153-170. DOI: https://doi.org/10.21608/mjeer.2017.63438
Al‐Baz, A., & El‐Sayed, A. (2018). A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks. International journal of communication systems, 31(1), e3407. DOI: https://doi.org/10.1002/dac.3407
Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 30(2), 259-267. DOI: https://doi.org/10.1016/j.jksuci.2017.04.002
Gautam, N., Lee, W. I., & Pyun, J. Y. (2009, October). Track-sector clustering for energy efficient routing in wireless sensor networks. In 2009 Ninth IEEE international conference on computer and information technology (Vol. 2, pp. 116-121). IEEE. DOI: https://doi.org/10.1109/CIT.2009.130
Ge, Y., Wang, S., & Ma, J. (2018). Optimization on TEEN routing protocol in cognitive wireless sensor network. EURASIP Journal on Wireless Communications and Networking, 2018(1), 27. DOI: https://doi.org/10.1186/s13638-018-1039-z
Gupta, P., & Sharma, A. K. (2019). Clustering-based Optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system. Soft Computing, 23(2), 507-526. DOI: https://doi.org/10.1007/s00500-017-2837-7
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 1(4), 660-670. DOI: https://doi.org/10.1109/TWC.2002.804190
Ienco, D., & Bordogna, G. (2018). Fuzzy extensions of the DBScan clustering algorithm. Soft Computing, 22(5), 1719-1730. DOI: https://doi.org/10.1007/s00500-016-2435-0
Kumar, V., Jain, S., & Tiwari, S. (2011). Energy efficient clustering algorithms in wireless sensor networks: A survey. International Journal of Computer Science Issues (IJCSI), 8(5), 259. DOI: https://doi.org/10.5815/ijwmt.2018.04.03
Lattanzi, E., Capellacci, P., & Freschi, V. (2020). Experimental evaluation of the impact of packet length on wireless sensor networks subject to interference. Computer Networks, 167, 106986. DOI: https://doi.org/10.1016/j.comnet.2019.106986
Mann, P. S., & Singh, S. (2017). Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. Journal of Network and Computer Applications, 83, 40-52. DOI: https://doi.org/10.1016/j.jnca.2017.01.031
Rajput, M., Sharma, S. K., & Khatri, P. (2017, August). Performance analysis of leach based approaches for large area coverage in wireless sensor network. In 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC) (pp. 1-5). IEEE. DOI: https://doi.org/10.3390/s18061926
Reddy, M. J., Prakash, P. S., & Reddy, P. C. (2013). Homogeneous and heterogeneous energy schemes for hierarchical cluster based routing protocols in WSN: a survey. In Proceedings of the third international conference on trends in information, telecommunication and computing (pp. 591- 595). Springer, New York, NY. DOI: https://doi.org/10.1007/978-1-4614-3363-7_70
Rostami, A. S., Badkoobe, M., Mohanna, F., Hosseinabadi, A. A. R., & Sangaiah, A. K. (2018). Survey on clustering in heterogeneous and homogeneous wireless sensor networks. The Journal of Supercomputing, 74(1), 277-323. DOI: https://doi.org/10.1007/s11227-017-2128-1
Sarvottam, K. & Yadav, R. K. (2015). Obesity-related inflammation & cardiovascular disease: Efficacy of a yoga-based lifestyle intervention. The Indian journal of medical research, 139(6), 822. DOI: https://doi.org/10.1016/j.mvr.2020.104023
Shen, X., Shahidehpour, M., Han, Y., Zhu, S., & Zheng, J. (2016). Expansion planning of active distribution networks with centralized and distributed energy storage systems. IEEE Transactions on Sustainable Energy, 8(1), 126-134. DOI: https://doi.org/10.1109/TSTE.2016.2586027
Singh, R., & Verma, A. K. (2017). Energy efficient cross layer based adaptive threshold routing protocol for WSN. AEU-International Journal of Electronics and Communications, 72, 166-173. DOI: https://doi.org/10.1016/j.aeue.2016.12.001
Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 146, 196-214. DOI: https://doi.org/10.1016/j.jss.2018.09.067
Xu, D., & Gao, J. (2011). Comparison study to hierarchical routing protocols in wireless sensor networks. Procedia Environmental Sciences, 10, 595-600. DOI: https://doi.org/10.1016/j.proenv.2011.09.096
Zanjireh, M. M., & Larijani, H. (2015, May). A survey on centralised and distributed clustering routing algorithms for WSNs. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) (pp. 1- 6). IEEE. DOI: https://doi.org/10.1109/VTCSpring.2015.7145650
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