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Enhance QoS by Learning Data flow rates in Wireless Networks Using Hierarchical Docition

Publication Type : Journal Article

Publisher : Elsevier

Source : Procedia Computer Science, 70, 708–714. 2015

Url :

Keywords : Reinforcement learning, Wireless network

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Verified : No

Year : 2015

Abstract : Wireless Wireless network finds application in military environments, emergency, rescue operations and medical monitoring due to its self-configuring nature. As the availability of resources such as processing power, buffer capacity and energy are limited in wireless networks; it is required to devise efficient algorithms for packet forwarding. Due to the dynamic nature of the wireless environment, the traditional packet forwarding strategies cannot guarantee good network performance every time. This paper proposes a method for learning data flow rates in wireless network to improve quality of service in the network. Each node in the network learns the environment using reinforcement learning approach and selects appropriate neighbours for packet forwarding. In order to improve the learning capacity of nodes, the hierarchical docition technique is employed. Docition applied to each layer of network, which selects a set of special nodes which has more information about the environment and share this information with less informative nodes. The algorithm is tested in a geographical routing protocol and the results indicate improved network performance.

Cite this Research Publication : Simi Surendran and Varghese, S. Ann, “Enhance QoS by Learning Data flow rates in Wireless Networks Using Hierarchical Docition”, Procedia Computer Science, 70, 708 - 714. 2015

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