Hybrid Lightweight Deep Learning-Based Error Detection Model on Edge Computing Devices
Arman Aghaei Attar, Tagir Fabarisov, Andrey S. Morozov, Maurice Artelt, Ilshat Mamaev · 2023
The cyber-physical systems (CPS) are characterized by a high degree of complexity due to the presence of networked heterogeneous components. This complexity makes it crucial to prevent error propagation in the system. Therefore, error detection and mitigation are necessary requirements in CPS. Recently, DL-based techniques have emerged as popular solutions for error detection in CPS. However, the main concern of DL-based error detection models in power constrained CPS is the trade-off between accuracy and speed. This leads to the necessity of designing optimized, accurate, and lightweight models.This paper proposes an optimized lightweight error detection model based on prediction approach. The paper addresses the limitations of conventional DL-based approaches in error detection for hardware-constrained CPS, particularly an exoskeleton system. The model adopted state-of-the-art efficient architecture that comprises in parallel CNN and LSTM layers, which is then transformed into a lightweight network through data quantization and network pruning techniques. The effectiveness of the proposed method is demonstrated through its application in error detection of the exoskeleton system’s data.