انتخاب سرخوشه فازی مبتنی بر اعتماد در شبکه‌های حسگر بی‌سیم

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان

1 دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه سجاد، مشهد، ایران.

2 دانشکده مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران.

چکیده

شبکه‌های حسگر بی‌سیم متشکل از تعداد زیادی گره حسگر می‌باشند که در یک منطقه محدود جغرافیایی پراکنده شده‌اند. چالش اصلی این شبکه‌ها محدودیت انرژی است. خوشه‌بندی، یک راه‌حل شناخته‌شده برای صرفه‌جویی در مصرف انرژی و افزایش طول عمر شبکه است. در اکثر روش‌های ارائه‌شده در هر دوره، آرایش خوشه‌ها و سرخوشه‌ها جهت افزایش طول عمر شبکه تغییر می‌یابد که باعث تحمیل مصرف انرژی بالا به سرخوشه‌ها می‌شود. همچنین برخی از روش‌های خوشه‌بندی منجر به انتخاب سرخوشه‌های مجزا برای خوشه‌های نزدیک به یکدیگر می‌شوند که مصرف انرژی بیشتری در پی دارند. مسئله اساسی دیگر، انتخاب گره‌های نامعتبر و نامطمئن به عنوان سرخوشه است زیرا منجر به عدم اطمینان در تعامل میان گره‌ها و کاهش امنیت کل شبکه می‌شود. هدف از روش پیشنهادی، ارائه یک روش خوشه‌بندی کارا است که علاوه بر برخورداری از مزایای مدیریت مصرف انرژی از طریق راهکارهایی نظیر عدم تغییر آرایش خوشه‌ها و سرخوشه‌ها در هر دور از اجرا و عدم انتخاب سرخوشه‌های مجزا برای خوشه‌های نزدیک، بتواند با تشخیص گره‌های مخرب و عدم انتخاب آن‌ها به عنوان سرخوشه، مسیری امن برای تعامل و ارتباط میان گره‌ها فراهم نماید. در روش پیشنهادی، شانس سرخوشه‌گی هر گره با استفاده از رویکرد فازی محاسبه شده و گرههای با شانس بیشتر، به عنوان گره مجاز و معتبر برای برقراری ارتباط شناخته می‌شوند. همچنین فرایند تشکیل خوشه به روش فازی و با تعریف تابع هدف متشکل از پارامترهای انرژی باقی‌مانده، فاصله تا ایستگاه پایه و میانگین فاصله گره‌های درون یک خوشه تا سرخوشه انجام می‌شود. کارایی روش پیشنهادی با روش‌های پایه و مرز دانش مقایسه شده است. نتایج آزمایش‌ها نشان میدهند روش پیشنهادی به طور میانگین 59.83% افزایش طول عمر و 14.75% کاهش مصرف انرژی را در پی دارد.

کلیدواژه‌ها


[1]       D. Suhag, S. S. Gaur, and A. Mohapatra, "A proposed scheme to achieve node authentication in military applications of wireless sensor network," Journal of Statistics and Management Systems, vol. 22, no. 2, pp. 347-362, 2019.
[2]       K. K. Khedo, Y. Bissessur, and D. S. Goolaub, "An inland Wireless Sensor Network system for monitoring seismic activity," Future Generation Computer Systems, vol. 105, pp. 520-532, 2020.
[3]       N. Munusamy, S. Vijayan, and M. Ezhilarasi, "Role of Clustering, Routing Protocols, MAC protocols and Load Balancing in Wireless Sensor Networks: An Energy-Efficiency Perspective," CYBERNETICS INFORMATION TECHNOLOGIES, vol. 21, no. 2, 2021.
[4]       F. Deniz, H. Bagci, and I. Korpeoglu, "Energy-efficient and fault-tolerant drone-BS placement in heterogeneous wireless sensor networks," Wireless Networks, vol. 27, no. 1, pp. 825-838, 2021.
[5]       N. Faruk et al., "A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction," Biocybernetics Biomedical Engineering, 2021.
[6]       J. Y. Lu Si, Wuyang Wu, Jun Ma, Qingbo Wu, Shasha Li,, "Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks," Wireless Pers Commun, vol. 108, pp. 473–492, 2019.
[7]       M. K. Khan et al., "Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis," Journal of Sensors, vol. 2021, 2021.
[8]       A. Alwan, "Data Quality Management in Large-Scale Cyber-Physical Systems," University of East London, 2021.
[9]       A. H. Abdulwahid, "Power grid surveillance and control based on wireless sensor network technologies: Review and future directions," in Journal of Physics: Conference Series, 2021, vol. 1773, no. 1, p. 012004: IOP Publishing.
[10]     S. Singh, S. Chand, and B. Kumar, "Energy efficient clustering protocol using fuzzy logic for heterogeneous WSNs," Wireless Personal Communications, vol. 86, no. 2, pp. 451-475, 2016.
[11]     H. Jadidoleslamy, M. R. Aref, and H. Bahramgiri, "A fuzzy fully distributed trust management system in wireless sensor networks," AEU-International Journal of Electronics and Communications, vol. 70, no. 1, pp. 40-49, 2016.
[12]     A. Jain and B. Reddy, "Node centrality in wireless sensor networks: Importance, applications and advances," in 2013 3rd IEEE International Advance Computing Conference (IACC), 2013, pp. 127-131: IEEE.
[13]     F. Fanian and M. Kuchaki Rafsanjani, "Cluster-based routing protocols in wireless sensor networks: A survey based on methodology," Journal of Network and Computer Applications, vol. 142, pp. 111-142, 2019/09/15/ 2019.
[14]     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, 2000, p. 10 pp. vol. 2: IEEE.
[15]     F. Song and B. Zhao, "Trust-based LEACH protocol for wireless sensor networks," in 2008 Second International Conference on Future Generation Communication and Networking, 2008, vol. 1, pp. 202-207: IEEE.
[16]     S. Sinha and Z. Chaczko, "T-SNIPER: Trust-aware sensor network information protocol for efficient routing," in 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, 2010, pp. 686-691: IEEE.
[17]     G. Ran, H. Zhang, S. J. J. o. I. Gong, and C. Science, "Improving on LEACH protocol of wireless sensor networks using fuzzy logic," Journal of Information Computational Science, vol. 7, no. 3, pp. 767-775, 2010.
[18]     M. Toloueiashtian and H. Motameni, "A new clustering approach in wireless sensor networks using fuzzy system," The Journal of Supercomputing, vol. 74, no. 2, pp. 717-737, 2018.
[19]     H. Bagci and A. Yazici, "An energy aware fuzzy approach to unequal clustering in wireless sensor networks," Applied Soft Computing, vol. 13, no. 4, pp. 1741-1749, 2013.
[20]     D. Agrawal and S. Pandey, "FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks," International Journal of Communication Systems, vol. 31, no. 2, p. e3448, 2018.
[21]     H. El Alami and A. Najid, "Energy-efficient fuzzy logic cluster head selection in wireless sensor networks," in 2016 International Conference on Information Technology for Organizations Development (IT4OD), 2016, pp. 1-7: IEEE.
[22]     P. S. Mehra, M. Doja, and B. Alam, "Zonal based approach for clustering in heterogeneous WSN," International Journal of Information Technology, vol. 11, no. 3, pp. 507-515, 2019.
[23]     P. Nayak and A. Devulapalli, "A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime," IEEE sensors journal, vol. 16, no. 1, pp. 137-144, 2015.
[24]     S. S. A. Mary and J. B. Gnanadurai, "Enhanced zone stable election protocol based on fuzzy logic for cluster head election in wireless sensor networks," International Journal of Fuzzy Systems, vol. 19, no. 3, pp. 799-812, 2017.
[25]     Y. K. Tamandani, M. U. Bokhari, and Q. M. Shallal, "Two-step fuzzy logic system to achieve energy efficiency and prolonging the lifetime of WSNs," Wireless Networks, vol. 23, no. 6, pp. 1889-1899, 2017.
[26]     A. A. Baradaran and K. Navi, "HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks," Fuzzy Sets and Systems, vol. 389, pp. 114-144, 2020.
[27]     R. Ranganathan, B. Somanathan, and K. Kannan, "Fuzzy-Based Cluster Head Amendment (FCHA) Approach to Prolong the Lifetime of Sensor Networks," Wireless Personal Communications, vol. 110, no. 3, pp. 1533-1549, 2020.
[28]     M. Mirzaie and S. M. Mazinani, "Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network," Computer Communications, vol. 111, pp. 56-67, 2017/10/01/ 2017.
[29]     N. Mazumdar and H. Om, "Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks," vol. 31, no. 12, p. e3709, 2018.
[30]     K. Sundaran, V. Ganapathy, and P. Sudhakara, "Fuzzy logic based Unequal Clustering in wireless sensor network for minimizing Energy consumption," in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), 2017, pp. 304-309.
[31]     A. K. Dwivedi and A. K. Sharma, "EE-LEACH: Energy Enhancement in LEACH using Fuzzy Logic for Homogeneous WSN," Wireless Personal Communications, pp. 1-21, 2021.
[32]     M. Karimi, H. R. Naji, and S. Golestani, "Optimizing cluster-head selection in Wireless Sensor Networks using Genetic Algorithm and Harmony Search Algorithm," in 20th Iranian Conference on Electrical Engineering (ICEE2012), 2012, pp. 706-710.
[33]     M. O. Oladimeji, M. Turkey, and S. Dudley, "HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks," Applied Soft Computing, vol. 55, pp. 452-461, 2017/06/01/ 2017.
[34]     N. Mittal, U. Singh, and B. S. Sohi, "An energy-aware cluster-based stable protocol for wireless sensor networks," Neural Computing and Applications, vol. 31, no. 11, pp. 7269-7286, 2019.
[35]     S. Tabatabaei, A. Rajaei, and A. M. Rigi, "A novel energy-aware clustering method via Lion Pride Optimizer Algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs)," Wireless Personal Communications, vol. 108, no. 3, pp. 1803-1825, 2019.
[36]     S. Lata, S. Mehfuz, S. Urooj, and F. Alrowais, "Fuzzy Clustering Algorithm for Enhancing Reliability and Network Lifetime of Wireless Sensor Networks," IEEE Access, vol. 8, pp. 66013-66024, 2020.
[37]     N. Mittal, S. Singh, U. Singh, and R. Salgotra, "Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks," Wireless Networks, vol. 27, no. 1, pp. 151-174, 2021.
[38]     S. Gajjar, M. Sarkar, and K. Dasgupta, "FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks," Applied Soft Computing, vol. 43, pp. 235-247, 2016.
[39]     Z. M. Zahedi, R. Akbari, M. Shokouhifar, F. Safaei, and A. Jalali, "Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks," Expert Systems with Applications, vol. 55, pp. 313-328, 2016.
[40]     M. Shokouhifar and A. Jalali, "Optimized sugeno fuzzy clustering algorithm for wireless sensor networks," Engineering applications of artificial intelligence, vol. 60, pp. 16-25, 2017.
[41]     F. Fanian and M. K. Rafsanjani, "Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm," Applied Soft Computing, vol. 71, pp. 568-590, 2018.
[42]     L. Kaufman and P. J. Rousseeuw, "Partitioning around medoids (program pam)," Finding groups in data: an introduction to cluster analysis, vol. 344, pp. 68-125, 1990.
[43]     N. R. Roy and P. Chandra, "A note on optimum cluster estimation in leach protocol," IEEE Access, vol. 6, pp. 65690-65696, 2018.
[44]     G. Han, J. Jiang, L. Shu, J. Niu, and H.-C. Chao, "Management and applications of trust in Wireless Sensor Networks: A survey," Journal of Computer and System Sciences, vol. 80, no. 3, pp. 602-617, 2014.