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Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks

Global Routing Versus Local Routing

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Machine Learning for Networking (MLN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11407))

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Abstract

Energy consumption and maximize lifetime routing in Mobile Ad hoc Network (MANETs) is one of the most important issues.

In our paper, we compare a global routing approach with a local routing approach both using reinforcement learning to maximize lifetime routing.

We first propose a global routing algorithm based on reinforcement learning algorithm called Q-learning then we compare his results with a local routing algorithm called AODV-SARSA.

Average delivery ratio, End to end delay and Time to Half Energy Depletion are used like metrics to compare both approach.

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Author information

Authors and Affiliations

  1. MISC Laboratory, Constantine 2 – Abdelhamid Mehri University, Constantine, Algeria

    Redha Mili & Salim Chikhi

Authors
  1. Redha Mili
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  2. Salim Chikhi
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Corresponding author

Correspondence to Redha Mili .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Inria, Paris, France

    Paul Mühlethaler

  3. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

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Mili, R., Chikhi, S. (2019). Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks. In: Renault, É., Mühlethaler, P., Boumerdassi, S. (eds) Machine Learning for Networking. MLN 2018. Lecture Notes in Computer Science(), vol 11407. Springer, Cham. https://doi.org/10.1007/978-3-030-19945-6_17

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  • DOI: https://doi.org/10.1007/978-3-030-19945-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19944-9

  • Online ISBN: 978-3-030-19945-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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