OPTIMIZING QOS AND CONGESTION IN MANETS USING XGBOOST WITH HYBRID PSO AND BELUGA WHALE STRATEGIES

In mobile ad hoc networks (MANETs), optimizing quality-of-service (QoS) routing is a NP-hard problem that requires effective solutions to improve crucial QoS metrics. Congestion is another major issue that has a significant impact on performance, especially at the node level. This study proposes a novel QoSaware routing framework that integrates machine learning (ml) with bio-inspired optimization to detect and mitigate node congestion in MANETs by assessing node reliability with key metrics such as queue buffer, received signal strength (RSS), residual energy (RE), bandwidth, and latency. To address the data sparsity and improve the model training, Synthetic Minority Oversampling Technique (SMOTE) has been used to expand the dataset, assuring a fair representation of the classes. Furthermore, K-means clustering has been used to generate labelled data in instances when labels were not easily available, allowing for more precise predictions. The prediction engine is based on an optimized XGBoost model, which is augmented by a synergistic mix of Particle Swarm Optimization (PSO) and the Beluga Whale Optimization Algorithm (BWOA). The results demonstrate that the suggested technique produces a higher PDR, outperforming AODV by 22% and IIWGSO-DRestNet-AODV by 2%. The throughput is increased by 60% over the AODV and 10% over the IIWGSO-DRestNet-AODV by varying pause time. Results are also proved better in terms of number of flows and number of nodes. Effectiveness of the proposed protocol has been established by comparing the results with ACO, PSO, CSO-AODV, IIWGSO-DResNetAODV, and normal AODV protocols.