Analysis of Time Series Data Generated From the Internet of Things Using Deep Learning Models

The ever-increasing widespread use of the Internet of Things and its applications has generated massive amounts of data. IoT sensor-generated datasets typically have a time-series structure and relational metadata to describe them. Time series data are typically data that have timestamps and can be obtained from sensors and IoT devices. Data prediction is required to maximize the potential of IoT-generated data, and anomaly detection and correction are needed to preserve data quality and integrity. Traditional machine learning models are incapable of analyzing the gigantic amounts of IoT-generated data. On the other hand, deep learning can properly analyze large data volumes, leading to increased use in the IoT domain. This research has examined the use of deep learning models for prediction, anomaly detection, and correction of data generated by IoT devices. The study found that deep learning is widely used in different fields today to analyze IoT-generated data. The research also outlines some challenges being faced while using deep learning models for IoT data analysis. More research is suggested in this study to expose more challenges and tackle the current challenges to achieve better IoT data analysis using deep learning.