Research Article
A Machine Learning Based Forwarding Algorithm over Cognitive Radios in Wireless Mesh Networks
@INPROCEEDINGS{10.1007/978-3-319-52730-7_23, author={Jianjun Yang and Ju Shen and Ping Guo and Bryson Payne and Tongquan Wei}, title={A Machine Learning Based Forwarding Algorithm over Cognitive Radios in Wireless Mesh Networks}, proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers}, proceedings_a={MLICOM}, year={2017}, month={2}, keywords={Mesh networks Machine learning Forwarding Highest bandwidth capacity}, doi={10.1007/978-3-319-52730-7_23} }- Jianjun Yang
Ju Shen
Ping Guo
Bryson Payne
Tongquan Wei
Year: 2017
A Machine Learning Based Forwarding Algorithm over Cognitive Radios in Wireless Mesh Networks
MLICOM
Springer
DOI: 10.1007/978-3-319-52730-7_23
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.