June 7, 2026 Journal article Open Access

Chimdesa Gedefa

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{
  "DOI": "10.20372/nadre:25014", 
  "language": "eng", 
  "title": "Intelligent Energy-Aware Routing: A Reinforcement Learning Approach for Non-Cooperative Node Detection and Path Optimization in MANETs", 
  "issued": {
    "date-parts": [
      [
        2026, 
        6, 
        7
      ]
    ]
  }, 
  "abstract": "<p><strong>ABSTRACT</strong></p>\n\n<p>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.</p>", 
  "author": [
    {
      "family": "Chimdesa Gedefa"
    }
  ], 
  "version": "version 1", 
  "type": "article-journal", 
  "id": "25014"
}