Intelligent Routing Protocols Using Machine Learning

Abstract

Routing serves as a cornerstone of network management, pivotal in determining the optimal path for data transmission. This research endeavor endeavors to scrutinize and assess improved routing algorithms customized for both wired and wireless networks. The inquiry encompasses a meticulous examination of contemporary routing algorithms deployed in these networks, aimed at gauging their effectiveness and operational efficiency. Specifically, the investigation delves into the OSPF (Open Shortest Path First) protocol tailored for wired networks and the AODV (Ad hoc On-Demand Distance Vector) protocol designed for wireless networks. These protocols are acknowledged as the most fitting for their respective network domains. The core thrust of this inquiry resides in a comprehensive analysis of routing algorithms employed in both wired and wireless networks, with particular attention directed towards OSPF and AODV. Elaborate elucidations regarding the foundational characteristics of these protocols are provided in ensuing sections. Additionally, the research explores shared traits observed in wired and wireless routing algorithms, alongside deliberating on the potential advantages of integrating machine learning methodologies to further enrich and optimize these algorithms. Through this investigative journey, the research endeavors to contribute to the progression of routing protocols, with the overarching goal of fostering more streamlined and dependable network communication.