Prediction of Network Traffic in Wireless Mesh Networks Using Hybrid Deep Learning Model

Wireless mesh networks are getting adopted in the domain of network communication. Their main benefits include adaptability, configuration, and flexibility, with added efficiency in cost and transmission time. Traffic prediction refers to forecasting the traffic volumes in a network. The traffic volume includes incoming requests and outgoing data transmitted by the network nodes. The previous logs of traffic in the network are used for extracting patterns that help for accurate predictions. In this paper, an analysis of various existing traffic prediction methods is done. Specifically, the analysis of a case study where the performance of the High-Speed Diesel (HSD) pump is predicted by observing its output. A network of sensors form a less mesh network; sensors act as nodes while reading the parameters, namely, three-phase Current, Voltage, Temperature, and Vibration. In this case study, a High-Speed Diesel pumps’ performance is predicted by predicting the vibration parameter as the output parameter. Other parameters affecting the performance of the High-Speed Diesel pump which are causing the change in vibration value are identified. Various algorithms, including Statistical Auto-Regressive Integration and Moving Average, Poisson’s regression, and a few Machine Learning and Deep Learning algorithms like Decision Tree Regressor, Multi-Layer Perceptron, Linear Regression, and Long Short-Term Memory are implemented and evaluated for this purpose. Along with the comparison, a novel architecture using Convolution Neural Network and Long Short-Term Memory is described in this paper. The result and comparison between these give the clear understanding that the suggested novel Convo-LSTM model gives better performance and helps to predict the performance of the High-Speed Diesel pump. The proposed system makes a strong case for the network traffic prediction, where the use of historical data is collected over the wireless mesh network. A similar analogy can be used where this model could be implemented further for network monitoring tasks.