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Wireless Personal Communications

, Volume 82, Issue 1, pp 61–80 | Cite as

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

  • Mohammad Shojafar
  • Saeid Abolfazli
  • Habib Mostafaei
  • Mukesh Singhal
Article

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.

Keywords

Wireless mesh network (WMN) Multi-radio Channel assignment (CA) Multi-channel Learning automata (LA) Energy usage reduction 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Mohammad Shojafar
    • 1
  • Saeid Abolfazli
    • 2
  • Habib Mostafaei
    • 3
  • Mukesh Singhal
    • 4
  1. 1.Department of Information Engineering, Electronics and Telecommunications (DIET)Sapienza University of RomeRomeItaly
  2. 2.Department of Computer System and TechnologyThe University of MalayaKuala LumpurMalaysia
  3. 3.Department of Computer Engineering, Urmia BranchIslamic Azad UniversityUrmiaIran
  4. 4.Computer Science and EngineeringUniversity of CaliforniaMercedUSA

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