Time Series in Sensor Data Using State-of-the-Art Deep Learning Approaches: A Systematic Literature Review

Internet of things (IoT) and artificial intelligence (AI) are becoming support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time-series data, we have: long short-term memory (LSTM) for its high precision in prediction and the ability to automatically process input sequences; convolutional neural networks (CNN) mainly in human activity recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and recurrent neural networks (RNN) for data fusion from different sensors and their subsequent classification, and stacked LSTM autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing. Finally, well-known technologies in natural language processing are also used in time-series data prediction, such as the attention mechanism and embeddings obtaining promising results.