Router Group selection using BAT Algorithm with Bayesian Filter (BABF): An Efficient Trajectory Error Detection

Abstract

The most common factors of traffic trajectory monitoring in operational networks are Error prediction, computational time and communication overhead. These factors are most applicable to the Wide Area Network (WAN) such as MANETs (Mobile Ad-hoc Networks) including 3G, 4G and 5G networks. This larger network size affects the traffic trajectory monitoring factors, i.e., Larger the network size, Error prediction; computational time and communication overhead will be lesser. BAT Algorithm is a new meta-heuristic Swarm Intelligence based optimization algorithm, which has been developed rapidly and has been applied in different optimization issues in recent years. In this paper, heuristic BAT algorithm is deployed with Bayesian filter (BABF) based on Naive Bayes Algorithm. To prove that our proposal is a promising approximation method, router group selection is applied with techniques such as Trajectory Sampling, PSAMP, and Fatih for the performance analysis and results are obtained. The proposed methodology is compared with the default Heuristic Router Group Selection Algorithm to analyze the efficiency of the new approach. The analysis is implemented in the working platform of MATLAB.