Оппортунистическая маршрутизация на основе гибридной оптимизации с учетом спектра для самоорганизующихся сетей когнитивной радиосвязи
Ключевые слова:
самоорганизующиеся сети когнитивной радиосвязи, оппортунистическая маршрутизация, оппортунистическая маршрутизация на базе карты спектра, оптимизация Firefly, оптимизация Grey-Wolf, порог пропускной способностиАннотация
Оппортунистическая маршрутизация повысила эффективность и надежность в самоорганизующихся сетях когнитивной радиосвязи (CRAHN). Многие исследователи разработали модели оппортунистической маршрутизации, в том числе модель оппортунистической маршрутизации на базе карты спектра (SMOR), которая считается более эффективной моделью в этой области. Однако в SMOR существуют определенные ограничения, которые требуют внимания и устранения. В данной статье рассматривается проблема задержки и ухудшения коэффициента доставки пакетов из-за неучета пропускной способности сети. Чтобы решить эти проблемы, в базовой модели маршрутизации SMOR используется гибридный алгоритм оптимизации, состоящий из алгоритмов оптимизации Firefly и Grey Wolf. Разработанная таким образом гибридная модель маршрутизации SMOR на основе оптимизации Firefly и Grey-Wolf (HFGWOSMOR) повышает производительность за счет высокой локальной и глобальной поисковой оптимизации. Первоначально анализируется взаимосвязь между задержкой и пропускной способностью, а затем устанавливается совместная многолучевая связь. Предлагаемая модель маршрутизации также вычисляет значения энергии принимаемых сигналов в пределах порога полосы пропускания и периода времени, и, следовательно, проблемы с производительностью, обнаруженные в SMOR, решаются. Чтобы оценить её эффективность, предложенная модель сравнивается со SMOR и другими существующими моделями оппортунистической маршрутизации, которые показывают, что предлагаемая модель HFGWOSMOR работает лучше, чем другие модели.
Литература
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2. Peha J.M. Approaches to spectrum sharing. IEEE Communications magazine. 2005. vol. 43(2). pp. 10–12.
3. Cesana M., Cuomo F., Ekici E. Routing in cognitive radio networks: Challenges and solutions. Ad Hoc Networks. 2011. vol. 9(3). pp. 228–248.
4. Chowdhury K.R. Communication protocols for wireless cognitive radio ad-hoc networks. Georgia Institute of Technology, 2009. 153 p.
5. Biswas S., Morris R. Opportunistic routing in multi-hop wireless networks. ACM SIGCOMM Computer Communication Review. 2004. vol. 34(1). pp. 69–74.
6. Biswas S., Morris R. ExOR: opportunistic multi-hop routing for wireless networks. ACM SIGCOMM Computer Communication Review. 2005. vol. 35(4). pp. 133–144.
7. Badarneh O.S., Salameh H.B. Opportunistic routing in cognitive radio networks: exploiting spectrum availability and rich channel diversity. IEEE Global Telecommunications Conference (GLOBECOM 2011). 2011. pp. 1–5. DOI: 10.1109/GLOCOM.2011.6134241.
8. Abdullah H.M.A., Kumar A.S. A Survey on Spectrum-Map Based on Normal Opportunistic Routing Methods for Cognitive Radio Ad Hoc Networks. International Journal of Advanced Networking and Applications. 2015. vol. 7(3), pp. 2761–2770.
9. Lin S.C., Chen K.C. Spectrum-map-empowered opportunistic routing for cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology. 2014. vol. 63(6). pp. 2848–2861.
10. Lin S.-C., Chen K.-C. Spectrum-Map-Empowered Opportunistic Routing for Cognitive Radio Ad Hoc Networks, IEEE Transactions on Vehicular Technology. 2014. vol. 63(6). pp. 2848–2861.
11. Abdullah H.M.A., Kumar A.S. A Hybrid Artificial Bee Colony Based Spectrum Opportunistic Routing Algorithm for Cognitive Radio Ad Hoc Networks. International Journal of Scientific and Engineering Research. 2016. vol. 7(6). pp. 294–303.
12. Abdullah H.M.A., Kumar A.S. HB-SOR: Hybrid Bat Spectrum Map Empowered Opportunistic Routing and Energy Reduction for Cognitive Radio Ad Hoc Networks (CRAHNs). International Journal of Scientific and Research Publications (IJSRP). 2017. vol. 7(5). pp. 284–297.
13. Abdullah H.M.A., Kumar A.V.S. HFSA-SORA: Hybrid firefly simulated annealing based spectrum opportunistic routing algorithm for Cognitive Radio Ad hoc Networks (CRAHN). Conference on Intelligent Computing and Control (I2C2). 2017. DOI: 10.1109/I2C2.2017.8321943.
14. Abdullah H.M.A., Kumar A.V.S. Modified SMOR Using Sparsity Aware Distributed Spectrum Map for Enhanced Opportunistic Routing in Cognitive Radio Adhoc Networks. Journal of Advanced Research in Dynamical and Control Systems. 2017. vol. 9(6). pp. 184–196.
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16. Abdullah H.M.A., Kumar A.V.S. Proficient Opportunistic Routing by Queuing Based Optimal Channel Selection for the Primary Users in CRAHN. ARPN Journal of Engineering and Applied Sciences. 2018. vol. 13(5). pp. 1649–1657.
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19. Lee H.W., Modiano E., Le L.B. Distributed throughput maximization in wireless networks via random power allocation. IEEE transactions on mobile computing. 2012. vol. 11(4). pp. 577–590.
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22. Ju H., Zhang R. Throughput maximization in wireless powered communication networks. IEEE Transactions on Wireless Communications. 2014. vol. 13(1). pp. 418–428.
23. Ping S., Aijaz A., Holland O., Aghvami A.H. Energy and interference aware cooperative routing in cognitive radio ad-hoc networks. IEEE Wireless Communications and Networking Conference (WCNC). 2014. pp. 87–92. DOI: 10.1109/WCNC.2014.6951927.
24. Meghanathan N., Fanuel M. A minimum channel switch routing protocol for cognitive radio ad hoc networks. 12th International Conference on Information Technology-New Generations (ITNG). 2015. pp. 280–285.
25. Mirjalili S., Saremi S., Mirjalili S.M., Coelho L.D.S. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications. 2016. vol. 47. pp. 106–119.
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27. Yang Qin, Xiaoxiong Zhong, Yuanyuan Yang, Yanlin Li, Li Li. Joint channel assignment and opportunistic routing for maximizing throughput in cognitive radio networks. IEEE, 2014. DOI: 10.1109/GLOCOM.2014.7037532.
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30. Abdullah H.M.A., Kumar A.V.S. Selective Cooperative Jamming Based Relay Selection and Blowfish Encryption for Enhancing Channel and Data Security in CRAHN Routing (Ed.: Elkhodr M.). Enabling Technologies and Architectures for Next-Generation Networking Capabilities. 2019. pp. 105–124. DOI: 10.4018/978-1-5225-6023-4.ch005.
31. Mirjalili S., Mirjalili S.M., Lewis A. Grey Wolf Optimizer. Advances in Engineering Software. 2014. vol. 69. pp. 46–61. DOI: 10.1016/j.advengsoft.2013.12.007.
32. Li J., Zhang L. Analytical Model for Dynamic Spectrum Decision in Cognitive Radio Ad Hoc Networks: A Stochastic Framework. Future Intelligent Information Systems. 2011. pp. 325–332.
33. Keskin R., Aliskan I. MultiObjective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator. Elektronika ir Elektrotechnika. 2023. vol. 29(1). pp. 4–14.
34. Yang X.-S. Cuckoo Search and Firefly Algorithm. Springer Science and Business Media LLC. 2014. vol. 516. 366 p. DOI: 10.1007/978-3-319-02141-6.
35. Emary E., Zawbaa H.M., Grosan C. Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 2018. vol. 29(3). pp. 681–694. DOI: 10.1109/TNNLS.2016.2634548.
36. Zhang Q., Li H., Liu C., Hu W. A New Extreme Learning Machine Optimized by Firefly Algorithm. Sixth International Symposium on Computational Intelligence and Design. 2013. vol. 1. pp. 133–136. DOI: 10.1109/ISCID.2013.147.
37. Raj R.N., Nayak A., Kumar M.S. QoS-aware routing protocol for Cognitive Radio Ad Hoc Networks. Ad Hoc Networks. 2021. vol. 113. DOI: 10.1016/j.adhoc.2020.102386.
Опубликован
2023-07-06
Как цитировать
Абдулла, Х. М. А., Кумар, А. С., Касем Ахмед, А. А., & Саид Мослех, М. А. (2023). Оппортунистическая маршрутизация на основе гибридной оптимизации с учетом спектра для самоорганизующихся сетей когнитивной радиосвязи. Информатика и автоматизация, 22(4), 880-905. https://doi.org/10.15622/ia.22.4.7
Раздел
Цифровые информационно-телекоммуникационные технологии
Copyright (c) Хишам Мохамед Али Абдулла, Unknown, Unknown, Unknown
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