AI-Enabled QoS Routing Framework Using Intelligent Route Discovery and Bandwidth Prediction in MANETs
DOI:
https://doi.org/10.31838/NJAP/08.02.15Keywords:
Mobile Ad hoc network Quality of service Routing Protocols Bandwidth EstimationAbstract
Mobile Ad Hoc Networks (MANETs), are characterized by their dynamic topologies, decentralized control, bandwidth constraints, and unpredictable variations in link quality are investigated. Such features significantly impact the provision of Quality of Service (QoS). Conventional routing protocols like AODV and DSR mainly rely on reactive route discovery and shortest path weights, without considering the actual real-time variations in bandwidth, congestion, and link quality, which may cause inefficient QoS performance. This paper proposes an AI-Enabled QoS Routing Framework that integrates smart route discovery with predictive bandwidth estimation to improve the reliability of routing in highly dynamic Mobile Ad Hoc Networks (MANETs). The proposed framework uses machine learning-based modelling to predict available bandwidth and link quality based on the analysis of past traffic patterns, mobility indicators, and congestion information.The route selection module uses weighted multipath routes based on the predicted QoS values, and the adaptive admission control module reduces network congestion and supports efficient resource use. Simulation experiments were performed to test the proposed framework with varying node density and mobility, using important performance metrics such as throughput, Packet Delivery Ratio (PDR), end-to-end delay (EED), routing overhead, and network lifetime. The results show that there are statistically significant improvements over traditional QoS-aware routing methods, thus confirming the effectiveness of AI-assisted predictive routing for QoS and scalability in MANETs.
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