Deep LSTM and Chi-Square Based Feature Selection Model for Traffic Congestion Prediction in Ad-Hoc Network

Abstract

Ad-hoc network is a type of wireless network, but it differs from other wireless networks in that it lacks infrastructure such as access points, routers, and other devices. While a node can communicate with every other node in the same cell in infrastructure networks, routing and the limitations of wireless communication are the main issues in ad hoc networks. But those clarifications are gave not accurate results. In order to overcome these issues, proposed traffic congestion prevention for IoT based traffic management in ad-hoc network using deep learning. This proposed method has five phases like data collection, preprocessing, feature selection, classification and decision making. The input data gathered from IoT devices in the ad-hoc network. After that, IoT features were preprocessed using missing values replacement and SMOTE resampling. Then preprocessed IoT data features to be selected using chi-square, which is used to select optimal features to avoid overfitting problems. Following that, the selected IoT features were classified with the help of deep LSTM, which is used to know whether the network is traffic or not. If the network have traffic, the data transmission is done through the traffic less path. Otherwise, the IoT data should be transmitted easily. The proposed model was designed and the performance was validated by using MATLAB software. Deep learning (DL) performance parameters such as accuracy, precision, recall, and error have values of 98.32, 98.325, 97.87, and 1.9%, respectively. Moreover, this proposed model is effective for detecting traffic congestion and which is used to prevent traffic through an ad-hoc network’s IoT based traffic management system.