Learning automata: an introduction
Learning automata: an introduction
Analysis of TCP performance over mobile ad hoc networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
ATP: a reliable transport protocol for ad-hoc networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Ad Hoc Wireless Networks: Architectures and Protocols
Ad Hoc Wireless Networks: Architectures and Protocols
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
ATCP: TCP for mobile ad hoc networks
IEEE Journal on Selected Areas in Communications
Data aggregation in sensor networks using learning automata
Wireless Networks
Clustering the wireless Ad Hoc networks: A distributed learning automata approach
Journal of Parallel and Distributed Computing
Clustering the wireless Ad Hoc networks: A distributed learning automata approach
Journal of Parallel and Distributed Computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Wireless Personal Communications: An International Journal
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The use of traditional TCP, in its present form, for reliable transport over Ad hoc Wireless Networks (AWNs) leads to a significant degradation in the network performance. This is primarily due to the congestion window (cwnd) updation and congestion control mechanisms employed by TCP and its inability to distinguish congestion losses from wireless losses. In order to provide an efficient reliable transport over AWNs, we propose Learning-TCP, a novel learning automata based reliable transport protocol, which efficiently adjusts the cwnd size and thus reduces the packet losses. The key idea behind Learning-TCP is that, it dynamically adapts to the changing network conditions and appropriately updates the cwnd size by observing the arrival of acknowledgment (ACK) and duplicate ACK (DUPACK) packets. Learning-TCP, unlike other existing proposals for reliable transport over AWNs, does not require any explicit feedback, such as congestion and link failure notifications, from the network. We provide extensive simulation studies of Learning-TCP under varying network conditions, that show increased throughput (9-18%) and reduced packet loss (42-55%) compared to that of TCP.