Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs

Gezahiegn Tessema; Shiferaw Sıleshe Getachew; Mahlet Agegnehu Asfaw

doi:10.57020/ject.1865444

Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs

Abstract

Mobile Ad Hoc Networks (MANETs) are characterized by their dynamic and decentralized nature, frequently leading to unstable routing paths and diminished network performance. Among the existing solutions, the Ad hoc On-demand Multi-path Distance Vector (AOMDV) routing protocol provides multi-path fault tolerance; however, it still encounters challenges related to route stability, packet delivery efficiency, and adaptability in response to changing topologies. Various efforts have been undertaken to improve routing in MANETs by applying machine learning techniques. While reinforcement learning and supervised learning methods have demonstrated potential, they often lack temporal context. Long Short-Term Memory (LSTM) networks address this limitation by retaining memory of past network behaviours. Conversely, Convolutional Neural Networks (CNNs) extract hierarchical spatial features, rendering them suitable for analyzing topological patterns. The integration of CNN and LSTM can capitalize on the strengths of both, thereby enhancing route prediction accuracy. This paper proposes and develops a hybrid CNN-LSTM deep learning model with an accuracy of 99.4% to augment the AOMDV protocol by predicting more reliable and efficient routing paths based on historical network metrics. The model is trained using simulated routing data in Python on Google Colab and is integrated into the AOMDV decision-making process. Evaluation across varying node densities reveals the enhanced AOMDV protocol achieved up to a 73% improvement in throughput (from 140 kbps to 242 kbps for 50-node scenarios), a 40% reduction in routing overhead, and a 57% increase in PDR (from 0.07 to 0.11), all without increasing end-to-end delay. The results confirm that the proposed deep learning-enhanced AOMDV protocol surpasses the traditional version, particularly in high-mobility and high-density scenarios.

Keywords

Supporting Institution

This research received funding from Dilla University in Ethiopia and the Science and Technology program for Science Week.

Project Number

0008

Ethical Statement

The study does not involve human participants or animals. In the study, the author/s declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Thanks

We thank Dilla University, Ethiopia, for the generous funding that made this study possible. The authors express their gratitude to the key stakeholders of the Science and Technology Week committee for their support of this paper.

References

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Details

Primary Language

English

Subjects

System and Network Security

Journal Section

Research Article

Authors

Gezahiegn Tessema ^*
0000-0001-9575-3395
Ethiopia

Shiferaw Sıleshe Getachew
0009-0002-1007-5965
Ethiopia

Mahlet Agegnehu Asfaw
0009-0003-4859-5816
Ethiopia

Publication Date

March 25, 2026

Submission Date

January 16, 2026

Acceptance Date

March 24, 2026

Published in Issue

Year 2026 Number: 6

DOI

https://doi.org/10.57020/ject.1865444

IZ

https://izlik.org/JA66WU48JA

Cite

RIS / Bibtex

APA

Tessema, G., Sıleshe Getachew, S., & Agegnehu Asfaw, M. (2026). Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs. Journal of Emerging Computer Technologies, 6, 13-28. https://doi.org/10.57020/ject.1865444

AMA

1.Tessema G, Sıleshe Getachew S, Agegnehu Asfaw M. Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs. JECT. 2026;(6):13-28. doi:10.57020/ject.1865444

Chicago

Tessema, Gezahiegn, Shiferaw Sıleshe Getachew, and Mahlet Agegnehu Asfaw. 2026. “Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs”. Journal of Emerging Computer Technologies, nos. 6: 13-28. https://doi.org/10.57020/ject.1865444.

EndNote

Tessema G, Sıleshe Getachew S, Agegnehu Asfaw M (March 1, 2026) Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs. Journal of Emerging Computer Technologies 6 13–28.

IEEE

[1]G. Tessema, S. Sıleshe Getachew, and M. Agegnehu Asfaw, “Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs”, JECT, no. 6, pp. 13–28, Mar. 2026, doi: 10.57020/ject.1865444.

ISNAD

Tessema, Gezahiegn - Sıleshe Getachew, Shiferaw - Agegnehu Asfaw, Mahlet. “Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs”. Journal of Emerging Computer Technologies. 6 (March 1, 2026): 13-28. https://doi.org/10.57020/ject.1865444.

JAMA

1.Tessema G, Sıleshe Getachew S, Agegnehu Asfaw M. Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs. JECT. 2026;:13–28.

MLA

Tessema, Gezahiegn, et al. “Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs”. Journal of Emerging Computer Technologies, no. 6, Mar. 2026, pp. 13-28, doi:10.57020/ject.1865444.

Vancouver

1.Gezahiegn Tessema, Shiferaw Sıleshe Getachew, Mahlet Agegnehu Asfaw. Hybrid CNN-LSTM-Enhanced AOMDV for Performance Optimization of AOMDV Routing Protocol in MANETs. JECT. 2026 Mar. 1;(6):13-28. doi:10.57020/ject.1865444