References
- S. Sunassee, A. Mungur, S. Armoogum, and S. Pudaruth, “A comprehensive review on congestion control techniques in networking,” in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021. pp. 305–12, DOI: 10.1109/ICCMC51019.2021.9418329.
- K. Upreti, N. Kumar, M. S. Alam, A. Verma, M. Nandan, and A. K. Gupta, “Machine learning-based Congestion Control Routing strategy for healthcare IoT enabled wireless sensor networks,” in 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2021. pp. 1–6, DOI: 10.1109/ICECCT52121.2021.9616864.
- J. Huang, D. Du, Q. Duan, Y. Zhang, Y. Zhao, H. Luo, Q. Liu, “Modeling and Analysis on congestion control for data transmission in Sensor clouds,” Int. J. Distrib. Sens. Netw., Vol. 2014, pp. 45398, 2014.
- W. Z. Hong, D. R. Li, and Y. Shen, “High-performance congestion control routing mechanism based on ant colony optimization,” J. Quantum Electr, Vol. 33, no. 05, pp. 618–27, 2016.
- H. C. Huang, W. J. Lu, J. X. Liu, and M. Hu, “MPTCP congestion control algorithm based on packet loss differentiation and sharing bottlenecks,” Comput. Eng. Des., Vol. 37, no. 03, pp. 19–24, 2016.
- R. Sukjaimuk, Q. N. Nguyen, and T. Sato, “An efficient congestion control model utilizing IoT wireless sensors in information-centric networks,” in 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021. pp. 210–13, doi:10.1109/ECTIDAMTNCON51128.2021.9425753.
- J. S. Dai, L. Rui, S. Chen, and X. Qiu, “A Distributed congestion control Routing protocol based on traffic classification in LEO satellite networks,” in 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2021, pp. 523–9.
- M. Chen, R. Li, Z. Zhao, and H. Zhang, “RAN Information-assisted TCP congestion control via DRL with reward redistribution,” in 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2021. pp. 1–7, DOI: 10.1109/ICCWorkshops50388.2021.9473523.
- N. Katsumata, K. Miyazawa, and S. Yamaguchi, “Managing TCP congestion control of smartphones toward advanced control,” in 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021. pp. 1–2, DOI: 10.1109/ICCE-TW52618.2021.9603132.
- S. S. Shi, Y. M. Ren, J. Li, L. L. Li, and J. Zhi, “A congestion control algorithm for named data networks based on cache interaction,” Chin. High Technol. Lett., Vol. 26, no. 04, pp. 359–66, 2016.
- G. B. N. Rao, and D. Veeraiah, “Congestion control scheme for efficient energy (CCSEE) in Mobile adhoc networks,” in 2021 3rd International Conference on Signal Processing and Communication (ICPSC), 2021. pp. 661–5, DOI: 10.1109/ICSPC51351.2021.9451721.
- G. Singh, A. K. Sharma, O. S. Bawa, and H. Kaur, “Effective congestion control In MANET,” in 2020 International Conference on Intelligent Engineering and Management (ICIEM), 2020. pp. 86–90, DOI: 10.1109/ICIEM48762.2020.9160130.
- Y. Bai, and Y. Jing, “Event-triggered network congestion control of TCP/AWM systems,” Neural Comput & Applic, Vol. 33, pp. 15877–86, 2021. DOI: 10.1007/s00521-021-06209-x.
- M. Aljubayri, T. Peng, and M. Shikh-Bahaei, “Reduce delay of multipath TCP in IoT networks,” Wireless Netw, Vol. 27, pp. 4189–98, 2021. DOI: 10.1007/s11276-021-02701-3.
- K. Yuan, F. Wang, and Z. Marszalek, “A delay-Tolerant data congestion avoidance algorithm for enterprise Cloud system based on modular computing,” Mobile Netw. Appl. (2021. DOI: 10.1007/s11036-021-01826-1.
- B. Nikmard, N. Movahhedinia, and M. R. Khayyambashi, “Congestion avoidance by dynamically cache placement method in named data networking,” J. Supercomput., Vol. 78, pp. 5779–805, 2022. DOI: 10.1007/s11227-021-04080-0.
- X. Sun, Z. Wang, Y. Wu, Che Hao, Jiang Hong, “A price-aware congestion control protocol for cloud services,” J Cloud Comp, Vol. 10, pp. 55–(1-15), 2021. DOI: 10.1186/s13677-021-00271-5.
- A. Sacco, M. Flocco, F. Esposito, and G. Marchetto, “Owl: congestion control with partially invisible networks via reinforcement learning,” in IEEE INFOCOM 2021 – IEEE Conference on Computer Communications, 2021. pp. 1–10, DOI: 10.1109/INFOCOM42981.2021.9488851.
- B. Mareschal, M. Kaur, V. Kharat, and S. Sakhare, “Convergence of Smart Technologies for Digital transformation,” Tehnički Glasnik - Technical Journal, Vol. 15, pp. II–IV, 2021. DOI: 10.31803/tg-20210225102651.
- X. D. Sun, J. M. Wang, J. X. Wang, et al., “Performance evaluation of mptcp congestion control algorithm in wireless networks,” Comput. Eng., Vol. 43, no. 07, pp. 129–35, 2017.
- W. Zhang, and M. Kaur, “A novel QACS automatic extraction algorithm for extracting information in blockchain-based systems,” IETE. J. Res. 2022. DOI: 10.1080/03772063.2022.2030252.
- Q. Y. Qin, D. D. Liu, and J. Zhang, “Research on adaptive congestion control mechanism in delay tolerant networks,” Comput. Eng. Appl., Vol. 54, no. 011, pp. 109–115, 2018.
- A. Maheshwari, and R. K. Yadav, “Analysis of congestion control mechanism for IOT,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020. pp. 288–93, DOI: 10.1109/Confluence47617.2020.9058058.
- S. Emara, B. Li, and Y. Chen, “Eagle: refining congestion control by Learning from the experts,” in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020. pp. 676–85, DOI: 10.1109/INFOCOM41043.2020.9155250.
- B. Tian, and S. T. Cai, “Multi-rate multicast congestion control mechanism for geo satellite networks,” Acta Electronica. Sinica., Vol. 44, no. 07, pp. 1599–604, 2016.