Intelligent Route Adaptation in Manets Using AI Techniques for Scalable Network Performance

Abstract—Mobile Ad Hoc Networks (MANETs) are prone to frequent topology changes and scalability issues due to their decentralized and mobile nature. As network size and node mobility increase, traditional routing protocols become inefficient, leading to degraded network performance. This paper introduces a novel AI-driven approach to route optimization that enables MANETs to self-adjust to dynamic conditions. The proposed method leverages machine learning algorithms to analyze real-time mobility patterns and link quality, allowing for predictive route selection and rapid reconfiguration. By dynamically adapting to varying network states, the system significantly enhances scalability, reduces latency, and improves packet delivery. Experimental results demonstrate that the AI-based model consistently outperforms conventional routing protocols under diverse network scenarios, making it a promising solution for future mobile and mission-critical applications.

Dynamic Routing, Machine Learning, Network Scalability, Mobility Prediction, Adaptive Protocols, Intelligent Routing, Real-Time Optimization, Wireless Communication

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