AODV Routing Optimization in Wireless Mesh Networks Using SDN-Inspired Control and ETX-Driven Machine Learning Weight Adaptation
DOI:
https://doi.org/10.30871/jaic.v10i3.12737Keywords:
Wireless Mesh Network (WMN), AODV, Software-Defined Networking (SDN), Expected Transmission Count (ETX), Mechine Learning, Gradient Boosting, Quality of Service (QoS)Abstract
Wireless Mesh Networks (WMNs) require adaptive routing to sustain Quality of Service (QoS) under dynamic conditions, yet conventional AODV is limited by its reliance on hop count, which does not reflect actual link quality. This study proposes a hybrid framework integrating AODV, SDN-inspired centralized control, and Machine Learning (ML)-based weight optimization using the Expected Transmission Count (ETX). The model was evaluated in NS-3 (v3.45) through a four-scenario ablation study with 30 repetitions per scenario and OLSR as a baseline. A Gradient Boosting model trained on 885 samples generated routing weights based on seven QoS-related features. The results show that the proposed method significantly improves performance compared to standard AODV, with throughput increasing by 32.71%, delay decreasing by 40.19%, and routing overhead reduced by 38.92%, all statistically significant. The model achieved high predictive accuracy (R² = 0.9929) without overfitting, with ETX emerging as the most influential feature. Overall, the integration of SDN control and ML optimization enhances routing efficiency, stability, and adaptability in WMNs, offering strong potential for IoT and smart city applications.
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