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Improving Channel Assignment in Multi-radio Wireless Mesh Networks with Learning Automata

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Abstract

Wireless mesh networks (WMNs) consist of static nodes that usually have one or more radios or media. Optimal channel assignment (CA) for nodes is a challenging problem in WMNs. CA aims to minimize interference in the overall network and thus increase the total capacity of the network. This paper proposes a new method for solving the CA problem that comparatively performs more efficient than existing methods. The link layer in the TCP/IP model is a descriptive realm of networking protocols that operates on the local network link in routers discovery and neighboring hosts. TCP/IP employs the link-layer protocol (LLP) that is included among the hybrid states in CA methods, and learning automata are used to complete the algorithm with an intelligent method for suitable CA. We call this algorithm LLLA, which are short for LLP and learning automata. Our simulation results show that LLLA performs more efficient than ad hoc on-demand distance vector (AODV) types with respect to parameters such as packet drop, end-to-end delay, average goodput, jitter in special applications, and energy usage.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Rome, Italy

    Mohammad Shojafar

  2. Department of Computer System and Technology, The University of Malaya, Kuala Lumpur, Malaysia

    Saeid Abolfazli

  3. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Habib Mostafaei

  4. Computer Science and Engineering, University of California, Merced, CA, S&E 296, USA

    Mukesh Singhal

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  1. Mohammad Shojafar
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  2. Saeid Abolfazli
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  4. Mukesh Singhal
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Correspondence to Mohammad Shojafar.

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Shojafar, M., Abolfazli, S., Mostafaei, H. et al. Improving Channel Assignment in Multi-radio Wireless Mesh Networks with Learning Automata. Wireless Pers Commun 82, 61–80 (2015). https://doi.org/10.1007/s11277-014-2194-0

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