Learning-TCP: a novel learning automata based reliable transport protocol for ad hoc wireless networks
Abstract
The use of traditional TCP, in its present form, for reliable transport over ad hoc wireless networks (AWNs) leads to significant degradation in the network performance, in terms of reduction in average network throughput and increase in packet losses. This is primarily due to the congestion window updation and congestion control mechanisms employed by TCP. TCP follows a deterministic approach for updating the size of the congestion window, which is less suitable for loss-prone AWNs as it leads to very frequent occurrences of congestion in the network. Another reason is TCP invokes the congestion control mechanism for both congestion and wireless losses, as it cannot distinguish between them. Hence, in order to use TCP in AWNs, an efficient mechanism must be provided with TCP for updating the size of the congestion window based on the network conditions and distinguishing the congestion losses from wireless losses. In order to address these problems, we propose Learning TCP, a novel learning automata based reliable transport protocol for AWNs, which efficiently adjusts the size of the congestion window and thus reduces the packet losses. The key idea behind Learning-TCP is that, it dynamically adapts to the changing network conditions by observing the occurrence of events, such as arrival of acknowledgment (ACK) and duplicate ACK (DUPACK) packets and appropriately updates the congestion window size. In addition to this, we use a deterministic approach for packet loss discrimination, in order to take the appropriate action for each type of loss. Learning-TCP, unlike other existing proposals for reliable transport over AWNs, does not require any explicit feedback, such as congestion, link failure, and available bandwidth notifications, from the network. We provide extensive simulation studies of Learning-TCP under varying network conditions that show increased throughput and reduced packet loss compared to that of the traditional TCP.
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Some RL-based approaches for updating CWND are designed for resource-constrained networks including WANETs and IoT, while others address wider network architectures such as satellite, cellular, and data center networks.
“Some of these approaches are particularly designed for resourceconstrained networks, including WANETs [29,219,379,380] and IoT [271], while others address a wider range of network architectures [30,122,477], such as satellite, cellular, and data center networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [380]
Simulations in GloMoSim demonstrate that TCP-FALA experiences lower packet loss and higher throughput than standard TCP-Reno in different network conditions.
“Simulations in GloMoSim demonstrate that TCP-FALA experiences lower packet loss and higher throughput than standard TCP-Reno in different network conditions.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [380]
TCP-FALA is the first RL-based TCP variant that focuses on CWND adaptation in wireless networks, particularly in WANETs.
“To the best of our knowledge, TCP-FALA [380] is the first RL-based TCP variant that focuses on CWND adaptation in wireless networks, particularly in WANETs.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [380]
For multi-hop-wireless networks, UL-TCP compared to TCP-NewReno and TCP-FALA exhibits similar results to Learning-TCP.
“For multi-hop-wireless networks (i.e.WANETs), UL-TCP is compared to TCP-NewReno and TCP-FALA, and exhibit similar results to Learning-TCP.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [380]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
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Self-citation
- Authors
- Venkata Ramana, B., Manoj, B. S., & Siva Ram Murthy, C.
- Affiliations
- Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., India
- ORCID
- Manoj, B.S.
- Venue
- 2nd International Conference on Broadband Networks, 2005.
- Publisher
- IEEE
- Published
- 2005
- Pages
- 484-493 Vol. 1
- OpenAlex
- W2125478902
- Cited by
- 20
- References
- 11
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 4 · 2021: 2 · 2018: 1 · 2014: 2 · 2013: 1
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Broadband Communications, Networks and Systems (BROADNETS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- learning automata, reliable transport protocol, ad hoc networks, congestion control, throughput, TCP over wireless networks
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