Dynamic Routing in Mobile Ad-Hoc Networks using AI-Powered Optical Waveguides

Abstract Mobile Ad-Hoc Networks (MANETs) are decentralized, self-organizing networks with mobile nodes that communicate wirelessly without relying on a fixed infrastructure. Despite their potential, MANETs face numerous challenges, including dynamic network topology, limited bandwidth, and unreliable communication links. The integration of optical waveguides in MANETs can address some of these challenges by enabling high-speed data transfer and improving network capacity. However, the dynamic nature of MANETs requires efficient and adaptive routing protocols to ensure stable and reliable communication. Artificial Intelligence (AI) techniques, particularly machine learning and deep learning, offer promising solutions to optimize path discovery, traffic management, and decision-making processes in these networks. This paper explores the application of AI-driven optical waveguides for dynamic routing in MANETs, highlighting their potential to optimize routing performance, enhance network reliability, and reduce latency in real-time operations. By leveraging AI techniques for intelligent routing decisions, optical waveguides can significantly improve the efficiency and scalability of MANETs, paving the way for more advanced mobile communication systems.