A Machine Learning-Based Forwarding Algorithm over Cognitive Radio in Wireless Mesh Networks

Document Type

Conference Paper

Publication Date

2016

Publication Source

International Conference on Machine Learning and Intelligent Communications: MLICOM 2016: Machine Learning and Intelligent Communications

Abstract

Wireless Mesh Networks improve their capacities by equipping mesh nodes with multi-radios tuned to non-overlapping channels. Hence the data forwarding between two nodes has multiple selections of links and the bandwidth between the pair of nodes varies dynamically. Under this condition, a mesh node adopts machine learning mechanisms to choose the possible best next hop which has maximum bandwidth when it intends to forward data. In this paper, we present a machine learning based forwarding algorithm to let a forwarding node dynamically select the next hop with highest potential bandwidth capacity to resume communication based on learning algorithm. Key to this strategy is that a node only maintains three past status, and then it is able to learn and predict the potential bandwidth capacities of its links. Then, the node selects the next hop with potential maximal link bandwidth. Moreover, a geometrical based algorithm is developed to let the source node figure out the forwarding region in order to avoid flooding. Simulations demonstrate that our approach significantly speeds up the transmission and outperforms other peer algorithms.

Inclusive pages

228-234

ISBN/ISSN

9783319527291

Comments

The International Conference on Machine Learning and Intelligent Communications: MLICOM 2016: Machine Learning and Intelligent Communications is part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series

Permission documentation on file.

Publisher

Springer

Peer Reviewed

yes

Link to Full Text

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