Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Over the recent years, deep learning has been considered as one of the primary choices for handling huge amounts of data. Having deeper hidden layers, it surpasses classical methods for detection of outliers in wireless sensor networks. The convolutional neural network (CNN) is a biologically-inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as electroencephalography is a tool used for investigation of brain function, and EEG signal gives time-series data as output. In this paper, the authors propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames, and these frames are projected onto 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy.