Abstract
TCP-Transmission Control Protocol provides connection oriented and reliable communication. TCP’s default consideration of any loss as a cause of network congestion degrades the end-to-end performance in wireless networks specially in MANETs-Mobile Adhoc Networks. TCP should identify various non congestion losses such as channel loss and route failure loss to act accordingly. Over the years, researchers have proposed Machine Learning based network protocols for accurate and efficient decision making. Reinforcement learning suits better for the dynamic networks with unpredictable traffic and topology. TCP-RLLD, TCP with Reinforcement Learning based Loss Differentiation is an end-to-end transport layer solution to predict cause of a packet loss. TCP’s default consideration of any loss as a congestion loss is overruled by TCP-RLLD to avoid unnecessary reduction of the transmission rate. TCP-RLLD is evaluated with multiple TCP variants for the Mobile Adhoc Networks. The extensive evaluation is performed with NS-3 simulator. This paper discusses TCP-RLLD architecture along with the detail of performance improvement.
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References
Mast, N., & Owens, T. J. (2011). A survey of performance enhancement of transmission control protocol (TCP) in wireless adhoc networks. EURASIP Journal on Wireless Communications and Networking, 2011, 1–23.
Al-Jubari, A. M., Othman, M., Mohd Ali, B., & Abdul Hamid, N. A. W. (2011). TCP performance in multi-hop wireless ad hoc networks challenges and solution. EURASIP Journal on Wireless Communications and Networking, 2011, 1–25.
Tsaoussidis, V., & Matta, I. (2002). Open issues on TCP for mobile computing. Journal on Wireless Communications and Mobile Computing, 2, 3–20.
Wang, S., Chaovalitwongse, W., & Babuska, R. (2012). Machine learning algorithms in bipedal robot control. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5), 728–743.
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. In Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies) (pp. 3–24).
Woergoetter, F., & Porr, B. (2008). Reinforcement learning. Scholarpedia, 3(3), 1448.
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2018). Machine learning for networking: Workflow, advances and opportunities. IEEE Network, 32(2), 92–99.
Boutaba, Raouf, Salahuddin, Mohammad A., Limam, Noura, Ayoubi, Sara, Shahriar, Nashid, Estrada-Solano, Felipe, & Caicedo, Oscar M. (2018). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications, 9, 1–99.
Molia, H. K., & Kothari, A. D. (2019). TCP with machine learning—advances and opportunities. International Journal of Advanced Trends in Computer Science and Engineering, 8, 3526–3534.
Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2021). Corrauc: A malicious bot-IoT traffic detection method in IoT network using machine-learning techniques. IEEE Internet of Things Journal, 8(5), 3242–3254.
Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2020). IoT malicious traffic identification using wrapper-based feature selection mechanisms. Computers and Security, 94, 101863.
Shafiq, M., Tian, Z., Bashir, A. K., Jolfaei, A., & Yu, X. (2020). Data mining and machine learning methods for sustainable smart cities traffic classification: A survey. Sustainable Cities and Society, 60, 102177.
Shafiq, M., Tian, Z., Sun, Y., Xiaojiang, D., & Guizani, M. (2020). Selection of effective machine learning algorithm and bot-IoT attacks traffic identification for internet of things in smart city. Future Generation Computer Systems, 107, 433–442.
Fonseca, N., & Crovella, M. (2005). Bayesian packet loss detection for TCP. IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 3, 1826–1837.
El Khayat, I., Geurts, P., & Leduc, G. (2010). Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wireless Networks, 16, 273–290.
Winstein, K., & Balakrishnan, H. (2013). Tcp ex machina: Computer-generated congestion control. ACM SIGCOMM Computer Communication Review, 43(4), 123–134.
Kong, Y., Zang, H., & Ma, X. (2018). Improving TCP congestion control with machine intelligence. In Proceedings of the 2018 Workshop on Network Meets AI & ML (pp. 60–66).
Arouche Nunes, B. A., Veenstra, K., Ballenthin, W., Lukin, S., & Obraczka, K. (2014). A machine learning framework for TCP round-trip time estimation. EURASIP Journal on Wireless Communications and Networking, 2014, 1–22.
Alim Al Islam, A. B. M., & Raghunathan, V. (2015). iTCP: An intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks. Wireless Networks, 21, 581–610.
Li, W., Zhou, F., Meleis, W. and Chowdhury, K., (2016). Learning-based and data-driven TCP design for memory-constrained IoT. In 2016 International conference on distributed computing in sensor systems (DCOSS) (pp. 199–205). IEEE.
Jiang, H., Luo, Y., Zhang, Q. Y., Yin, M. Y., & Chun, W. (2017). TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks. Wireless Networks, 23, 1535–1548.
Kumar, P., Tripathi, S., & Pal, P. (2018). Neural network based reliable transport layer protocol for manet. In 4th International conference on recent advances in information technology IIT(ISM), Dhanbad-IEEE Explore (pp. 1–6).
Li, W., Zhou, F., Chowdhury, K. R., & Meleis, W. (2018). QTCP: Adaptive congestion control with reinforcement learning. IEEE Transactions on Network Science and Engineering, 6(3), 445–458.
Molia, H.K. & Kothari, A.D., (2020). Appropriateness of machine learning techniques for TCP with MANETs. In Proceedings of the international conference on communication and intelligent systems, Lecture Notes in Networks and Systems (pp. 503–511). Springer.
Sutton, R. S., & Barto, A. G. (2011). Reinforcement learning: An introduction. MIT Press.
Ana Aguiar. CRAWDAD dataset it/vr2marketbaiaotrial (v. 2019-09-16). Downloaded from https://crawdad.org/it/vr2marketbaiaotrial/20190916, September 2019.
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Molia, H.K., Kothari, A.D. TCP-RLLD: TCP with reinforcement learning based loss differentiation for mobile adhoc networks. Wireless Netw 29, 1937–1948 (2023). https://doi.org/10.1007/s11276-023-03254-3
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DOI: https://doi.org/10.1007/s11276-023-03254-3