June 7, 2026 Journal article Open Access

Chimdesa Gedefa

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        <foaf:name>Chimdesa Gedefa</foaf:name>
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            <foaf:name>Dilla University</foaf:name>
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    <dct:title>Intelligent Energy-Aware Routing: A Reinforcement Learning Approach for Non-Cooperative Node Detection and Path Optimization in MANETs</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2026</dct:issued>
    <dcat:keyword>Mobile Ad Hoc Networks (MANETs), Reinforcement Learning, Q-Learning, Energy-Aware Routing, Non-Cooperative Nodes, Trust Management, Path Optimization, Network Lifetime.</dcat:keyword>
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        <foaf:name>Prassad Kumar</foaf:name>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2026-06-07</dct:issued>
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    <dct:description>&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;</dct:description>
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