Adaptive Routing Protocol to Optimize the Quality of Service of MANET Using Neural Network

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]29/09/2025 at 10:58 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 01 09

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    Awadhesh Kumar Rai, Akhilesh A. Waoo,

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  1. Assistant Professor, Professor (Dean), Department of Computer Science and Engineering, Amicable Knowledge Solution University, Satna, Department of Computer Science and Engineering, Amicable Knowledge Solution University, Satna, Madhya Pradesh, Madhya Pradesh, India, India

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Abstract

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nA MANET is a group of mobile nodes that create a temporary network without relying on centralized administration or standard supporting devices, often functioning as a conventional network. These dynamic environments present significant challenges for traditional routing and switching protocols, particularly in delivering Quality of Service (QoS) benchmarks such as bandwidth, latency, packet delivery ratio, and robustness. This study proposes an Adaptive Routing Protocol (ARP) leveraging Neural Network (NN) techniques to enhance QoS in MANETs. The framework incorporates a feed-forward neural network trained on network-specific parameters such as node density, signal strength, mobility patterns, and historical performance metrics. The NN is a decision-making engine that dynamically adjusts routing paths and switching criteria based on real-time network conditions, optimizing data transmission efficiency. Simulations conducted using standard QoS metrics demonstrate that the proposed ARSP significantly outperforms existing protocols like AODV, DSR, and OLSR in terms of latency reduction, increased packet delivery ratios, and improved overall network throughput. The NN’s ability to learn and adapt to changing network conditions provides a flexible solution that can be applied to different MANET scenarios. This research underscores the potential of machine learning techniques, especially neural networks, in addressing the complex routing challenges in MANETs while ensuring optimal quality of service for diverse applications.nn

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Keywords: Adaptive routing protocol, DDPG, MANET, neural network, quality of service

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Mobile Computing, Communications & Mobile Networks (jomccmn)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nAwadhesh Kumar Rai, Akhilesh A. Waoo. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Adaptive Routing Protocol to Optimize the Quality of Service of MANET Using Neural Network[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):01-09.

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How to cite this URL:
nAwadhesh Kumar Rai, Akhilesh A. Waoo. [if 2584 equals=”][226 striphtml=1][else]Adaptive Routing Protocol to Optimize the Quality of Service of MANET Using Neural Network[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):01-09. Available from: https://journals.stmjournals.com/jomccmn/article=29/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 15/05/2025
Accepted 03/09/2025
Published 29/09/2025
Retracted
Publication Time 137 Days

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