June 7, 2026 Journal article Open Access

Chimdesa Gedefa

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  <dc:creator>Chimdesa Gedefa</dc:creator>
  <dc:date>2026-06-07</dc:date>
  <dc:description>ABSTRACT

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.</dc:description>
  <dc:identifier>https://zenodo.org/record/25014</dc:identifier>
  <dc:identifier>10.20372/nadre:25014</dc:identifier>
  <dc:identifier>oai:zenodo.org:25014</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.20372/nadre:25013</dc:relation>
  <dc:relation>url:https://nadre.ethernet.edu.et/communities/001</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:subject>Mobile Ad Hoc Networks (MANETs), Reinforcement Learning, Q-Learning, Energy-Aware Routing, Non-Cooperative Nodes, Trust Management, Path Optimization, Network Lifetime.</dc:subject>
  <dc:title>Intelligent Energy-Aware Routing: A Reinforcement Learning Approach for Non-Cooperative Node Detection and Path Optimization in MANETs</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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