Optimal selection of network in heterogeneous environment based on fuzzy approach

Abstract

Since quality of service of the wireless network is changing over time, there is need of systematic and periodic analyzing of network traffic to get connected with the optimal network among the heterogeneous technologies. To cope with this scenario, this work has proposed a stream data mining based on ST-DR (Dynamic relaxation) fuzzy c means clustering to classify the network traffic effectively. Subsequently classified data would be sent to the web usage mining based on Probabilistic Latent Semantic Analyzer which analyzes the traffic information. Even though being classified and analyzed the network traffic information itself can’t get connected to the optimal network due to its dynamic nature so to handle this situation this work has incorporated an ensemble machine learning algorithm applying sequential AdaBoost which predicts the quality of service of the each network during network traffic and enables the user to get connected with an optimal network which have superior Quality of Service (QoS).