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Intelligent Models of Random Walk

  • Ali Mohammad Saghiri
  • M. Daliri Khomami
  • Mohammad Reza Meybodi
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Intelligent models of random walk are obtained from random walk algorithms and a learning element. In this chapter, the learning element is a type of learning automata which is called as variable-structure learning automata. In this chapter, this type of learning automata is used to design three intelligent models of random walk. The first model is called Intelligent K-Random Walk based on Learning Automata (IKRW-LA). This model is able to predict k promising links from each arbitrary node in the networks. The second model is called Self-Organized Intelligent Random Walk based on Learning Automata (SOIRW-LA). This model is proposed to add the learning capability to k-random walk for path prediction. In this model, we do not need to determine the value for parameter k to find the promising links. The third model is called Intelligent Random Walk based on Learning Automata (IRW-LA). The advantage of this model than the others models is to predict the best path of the network considering the feedback received from the network. This model is equivalent to the first model (IKRW-LA) when the value of parameter k is equal to one. The convergence behavior of the IRW-LA is also studied. All of the mentioned models are described in this chapter.

Keywords

Intelligent models of random walk Path prediction Variable-structure learning automata 

References

  1. 1.
    Narendra KS, Thathachar MA (1989) Learning automata: an introduction. Prentice-HallGoogle Scholar
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    Abolhassani M, Esnaashari M, Meybodi MR (2009) LADIT: A learning automata fault tolerant routing protocol for sensor networks. Yazd, Iran, pp 12–18Google Scholar
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    Rezvanian A, Saghiri AM, Vahidipour M, Esnaashari M, Meybodi MR (2018) Recent advances in learning automata. SpringerGoogle Scholar
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    Akbari Torkestani J, Meybodi MR (2010) Mobility-based multicast routing algorithm for wireless mobile Ad-hoc networks: a learning automata approach. Comput Commun 33:721–735CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Mohammad Saghiri
    • 1
  • M. Daliri Khomami
    • 1
  • Mohammad Reza Meybodi
    • 1
  1. 1.Amirkabir University of TechnologyTehranIran

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