Paper Info
Title
A Link-Based Variable Probability Learning Approach for Partially Overlapping Channels Assignment on Multi-Radio Multi-Channel Wireless Mesh Information-Centric IoT Networks.
Abstract
The performance of the wireless mesh information-centric Internet of Thing (IC-IoT) networks can be greatly enhanced by adopting multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). However, the network interference and channel assignment in IC-IoT networks become more complicated while using both MRMC and POCs. In this paper, a logical link-based partially overlapping channels interference model is analyzed to mitigate the inter-channel interference, and a channel selection scheme is formulated as a potential game. Moreover, a variable probability learning algorithm is proposed by selecting a channel in the strategy space based on its probability. The channel usage probability can be changed by its link utility. The channel with a larger link utility is then with a bigger probability in the strategy space. The simulation results show that our proposed algorithm can achieve high system throughput with fast network convergence.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2908872
IEEE ACCESS
Keywords
Field
DocType
Multi-radio multi-channel (MRMC),partially overlapping channels (POCs),potential game,variable probability learning algorithms
Computer science,Internet of Things,Communication channel,Computer network,Multi channel,Wireless mesh network,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xiongwen Zhao117520.36
Liang Li22016.30
Suiyan Geng312111.28
Hui Zhang431.73
Yonghong Ma500.34