Analysis of Learning Techniques for Performance Prediction in Mobile Adhoc Networks

Abstract

Current advancements in cellular technologies and computing have provided the basis for the unparalleled exponential development of mobile networking and software availability and quality combined with multiple systems or network software. Using wireless technologies and mobile ad-hoc networks, such systems and technology interact and collect information. To achieve the Quality of Service (QoS) criteria, the growing concern in wireless network performance and the availability of mobile users would support a significant rise in wireless applications. Predicting the mobility of wireless users and systems performs an important role in the effective strategic decision making of wireless network bandwidth service providers. Furthermore, related to the defect-proneness, self-organization, and mobility aspect of such networks, new architecture problems occur. This paper proposes to predict and simulate the mobility of specific nodes on a mobile ad-hoc network, gradient boosting devices defined for the system will help. The proposed model not just to outperform previous mobility prediction models using simulated and real-world mobility instances, but provides better predictive accuracy by an enormous margin. The accuracy obtained helps the suggested mobility indicator in Mobile Adhoc Networks to increase the average level of performance.