Adaptive access-point and channel selection method using Markov approximation

This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely...

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Bibliographic Details
Main Authors: Tomotaka Kimura, Kouji Hirata, Masahiro Muraguchi
Format: Article
Language:English
Published: SAGE Publishing 2018-02-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/1550147718761584
Description
Summary:This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely placed wireless access-points. This article introduces a joint optimization problem of access-point and channel selection for such an environment. The proposed method deals with the optimization problem, using Markov approximation which adapts to dynamic changes in network conditions. Markov approximation is a distributed optimization framework, where a network is optimized by individual behavior of users forming a time-reversible continuous-time Markov chain. The proposed method searches optimal solution for the access-point and channel selection problem on the time-reversible continuous-time Markov chain. Simulation experiments demonstrate the effectiveness of the proposed method.
ISSN:1550-1477