A Novel Framework For Adaptation Routing In Dynamic Network Using Advance Machine Learning and Deep Learning Techniques

Abstract

In the face of fast growth in network demands, efficient, adaptive, and energy-aware routing becomes necessary for maintaining network performance and lifetime. Traditional protocols can hardly deal with dynamic networks in terms of latency, energy consumption, or resilience against congestion. This work presents an integrated framework of machine learning, deep learning, time-series analysis, and reinforcement learning models to enhance routing adaptability, energy efficiency, and predictive capabilities. The proposed approach involves four models that address certain specific limitations of the current routing protocols. Firstly, GBDT will be able to learn complex interactions among nodes and traffic to facilitate adaptive routing with greatly reduced latency and packet loss. Secondly, a hybrid model of Graph Convolutional Network and Long Short-Term Memory allows for context-aware routing, using the spatial-temporal pattern for stability and energy efficiency in path selection. The load balancing is done by the congestion-anticipatory predictive routing model using ARIMA combined with the anomaly detection using Isolation Forest. Finally, this paper proposes a multi-agent reinforcement learning framework using Deep Q-Networks to enable distributed nodes to learn collaboratively from each other about the best routes to extend network lifetime through energy-efficient decision-making processes. These experimental results prove that this multi-layered approach, which had increased latency by 20-30%, reduced routing instability up to 40%, and increased energy efficiency up to 40%. This framework overcomes not only the deficiencies of existing adaptive routing, but also provides a scalable, intelligent, and self-optimizing routing solution that ensures robust performance in complex and variable network environments.