Adaptive QoS-Aware Routing in Mobile Ad-Hoc Networks Using Hybrid Machine Learning Techniques
Keywords:
Adaptive routing, Hybrid machine learning, Mobile ad-hoc networks, Network performance, QoS-Aware routing, Reinforcement learningAbstract
The Mobile Ad-Hoc Network (MANETs) use is vitiated by an ongoing issue of providing stable, effective and viable routing due to the extremely dynamic topology, the spatial distribution of nodes, the frequentness of link failures, and the constraints of energy and bandwidth. The above challenges make it problematic in a way that the capability of the traditional routing protocols to fulfill Quality of Service (QoS) requests on a consistent basis is difficult to achieve particularly in a high mobility environment with dense node distributions. To address these deficiencies, this paper proposes a hybrid machine learning-based routing framework that dynamically selects the most appropriate routing path as well as offers QoS-aware network performance. The proposed framework integrates supervised learning techniques to recreate the stability of the links and forecast the traffic patterns, and reinforcement learning techniques that can be used to generate proactive and intelligent routing decisions. Based on the information on past and current network states, the hybrid approach adapts itself to changes of topology, congestion prediction and dynamically selected routings that optimize different QoS parameters are selected. This collaborative learning system allows the network to compute routing decisions in a self-adaptable and intelligent manner, and remove any reliance on fixed or reactive routing protocols. The usefulness of the suggested methodology is experimented with the aid of a great number of simulations with the NS-3 network simulator, a set of diverse mobility models, node densities, and traffic scenarios. The key performance indicators, which are the end-to-end delay, ratio of delivered packets, throughput, and energy consumption, are acquired and compared with the traditional QoS-aware routing protocols. The hybrid learning based solution, as pointed out in the simulation results, has a major impact on increasing the degree of reliability of the network, latency, and energy efficiency, particularly in a highly mobile and densely populated MANET. This paper, by and large, proposes a scalable and sound routing structure, which bridges the gap between intelligent decision-making, and practical support of QoS of next-generation MANETs.
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