An Analysis of the Effectiveness of MANET Routing Algorithms using Machine Learning

Mobile Ad Hoc Networks (MANETs) are a dynamic and self-configuring wireless communication system that is essential in a wide range of applications, including military operations and disaster recovery situations. The effectiveness and dependability of communication in MANETs are greatly contingent upon the routing algorithms utilized. This study provides a thorough examination of the efficacy of MANET routing algorithms, using the capabilities of machine learning methodologies. The chapter begins by examining current MANET routing protocols, highlighting their fundamental attributes and challenges in dynamic and unpredictable network environments. To adapt to the changing characteristics of Mobile Ad hoc Networks (MANETs), machine learning algorithms are employed to forecast network circumstances, including connection quality, node mobility, and congestion. These factors have a substantial influence on the efficiency of routing. To assess the suggested method, comprehensive simulations are carried out employing well-known MANET routing protocols and diverse machine learning techniques. Performance measures, such as packet delivery ratio, end-to-end latency, and network throughput, are used to evaluate the efficiency of routing algorithms in various situations. The simulations yield valuable insights on the flexibility and robustness of routing protocols when combined with machine learning predictions. Moreover, the research examines the consequences of incorporating machine learning into MANET routing algorithms, taking into account aspects such as the computational burden and resources limitations inherent in mobile devices. Additionally, it examines the possibility of utilizing adaptive learning techniques to modify routing algorithms in response to current network circumstances flexibly. The study findings contribute to the ongoing discussion on enhancing the efficiency of Mobile Ad hoc Networks (MANETs) by providing a detailed understanding of how machine learning can be utilized to improve routing algorithms. The suggested method presents a hopeful path for future investigation and advancement in the field of mobile ad hoc networks, with possible uses in enhancing communication dependability and efficiency in various changing situations.