June 7, 2026 Journal article Open Access

Chimdesa Gedefa

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  <identifier identifierType="DOI">10.20372/nadre:25014</identifier>
  <creators>
    <creator>
      <creatorName>Chimdesa Gedefa</creatorName>
      <affiliation>Dilla University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Intelligent Energy-Aware Routing: A Reinforcement Learning Approach for Non-Cooperative Node Detection and Path Optimization in MANETs</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject>Mobile Ad Hoc Networks (MANETs), Reinforcement Learning, Q-Learning, Energy-Aware Routing, Non-Cooperative Nodes, Trust Management, Path Optimization, Network Lifetime.</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Supervisor">
      <contributorName>Prassad Kumar</contributorName>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2026-06-07</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/25014</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre:25013</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/001</relatedIdentifier>
  </relatedIdentifiers>
  <version>version 1</version>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;ABSTRACT&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Mobile Ad Hoc Networks (MANETs) are inherently susceptible to energy depletion and non-cooperative node behavior, both of which critically degrade routing performance and network lifetime. Existing routing protocols fail to simultaneously address dynamic topology changes, selfish node detection, and energy-balanced path selection. This paper presents REAR-RL (Reinforcement Energy-Aware Routing via Reinforcement Learning), a novel adaptive routing framework that integrates a Q-learning-based decision engine with a multi-criteria reward function encapsulating residual energy, link quality, node cooperation history, and hop count. REAR-RL employs a lightweight trust model derived from packet forwarding behavior to identify and isolate non-cooperative nodes without requiring centralized infrastructure. The reward shaping strategy prioritizes routes that balance energy consumption across participating nodes while maximizing packet delivery. Extensive simulations conducted in NS-3 with 50 to 200 mobile nodes reveal that REAR-RL achieves up to 34.7% improvement in network lifetime, a 28.3% increase in packet delivery ratio, and reduces end-to-end delay by 19.6% compared to AODV, DSR, and OLSR under varying node mobility and traffic loads. These results demonstrate the viability of model-free reinforcement learning as a scalable, infrastructure-free solution for intelligent routing in adversarial mobile environments.&lt;/p&gt;</description>
  </descriptions>
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