Resource Aware Long Short-Term Memory Model (RALSTMM) Based On-Device Incremental Learning for Industrial Internet of Things

The interconnection of instruments (i.e., actuators and sensors) networked together for industrial applications brings about the Industrial Internet of Things (IIoT). This connectivity enables the collection, sharing, and analysis of data to enhance the efficiency and productivity of manufacturing. Machine learning models are popular methods for analyzing massive time-series data collected by industrial control systems. Classical Long Short-Term Memory (LSTM), which is a widely used time-series model, learns patterns by feeding the entire dataset at once, and the model remains fixed. However, real-world industrial control system nodes generate new data. This paper proposes Resource Aware Long Short-Term Memory Model (RALSTMM) based incremental learning for edge devices in the IIoT. In RALSTMM edge devices can collect and analyze data for various predictive applications. The proposed RALSTMM can be deployed on these tiny edge devices and can be updated to enhance existing knowledge using newly generated data. The RALSTMM gradually learns from newly collected data by leveraging crucial information from previously analyzed data, thereby minimizing the resources needed for training. Hence, previous data that has been processed earlier would not undergo further analysis as the model has already extracted the necessary knowledge from it. The performance of RALSTMM has been evaluated with Mean Squared Error (MSE), accuracy, recall, precision, information criteria and processing time using three IoT testbed datasets. A comparative experimental demonstration of the RALSTMM with the existing LSTM proves the effectiveness of the RALSTMM by reducing processing time and maintaining its performance.