Published Paper Details:

The emergence of Artificial Intelligence (AI) in networking has transformed the design and operation of modern communication infrastructures. AI routers, enhanced with Machine Learning (ML) algorithms, enable intelligent decision-making, predictive analysis, and dynamic optimization of network resources. Unlike conventional routers that rely on static protocols, AI routers continuously learn from network data to predict congestion, reroute traffic, and ensure optimal performance. Machine learning techniques such as supervised learning, reinforcement learning, and deep neural networks have been effectively applied for traffic prediction, congestion control, anomaly detection, and energy-efficient routing. In Software-Defined Networking (SDN), AI-based routing enhances scalability and adaptability by enabling proactive flow control. Similarly, in Internet of Things (IoT) and Wireless Sensor Networks (WSN), ML-powered routers improve energy efficiency and reliability in dense environments. AI routers are also crucial in data centers, UAV-based communication, and 5G/6G systems, where real-time adaptability and low-latency routing are vital. Reinforcement learning models like Deep Q-Networks (DQN) and actor-critic algorithms are used to learn optimal paths dynamically under changing network conditions. Additionally, AI routers enhance network security by detecting malicious traffic patterns through anomaly-based learning models. Despite their advantages, challenges persist in scalability, computational complexity, and explainability of ML models. Future research aims to integrate explainable AI (XAI), federated learning, and edge intelligence to build autonomous, self-healing, and energy-aware routing systems. AI routers thus represent a pivotal step toward the realization of fully intelligent, adaptive, and resilient communication networks for next-generation systems.