Design of an optimized sensor fault identification within the limited computing, memory, and energy capabilities

Abstract

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors’ limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though the diagnosis does not have to be made quickly. This project aims to provide an enhanced sensor-fault detection and diagnostic system for the IoT and CPS with constrained energy, memory, and computation resources. The system’s goals are to promptly detect defects, lessen the computing burden on sensors, and enhance the recall and accuracy of fault detection. This study used a hybrid approach that combined principal component analysis, autoencoder, and gated recurrent unit to create an optimal sensor-fault detection and diagnostic system. There were 1001 sensor readings in the dataset; 112 were defective, while the remaining 888 were normal. The investigation showed that the suggested method, which detected faults with an accuracy of 95% and recall of 92%, achieved high accuracy and recall in recognizing defects in IoT and CPS. With significantly shorter processing times, the system’s potential to reduce computational strain on sensors was also proved. The findings of this study indicate that the suggested optimized sensor-fault detection and diagnosis system successfully detects faults in IoT and CPS with limited computation, memory, and energy resources. The system’s ability to reduce the computing burden on sensors while improving accuracy and recall makes it an appealing choice for industrial and commercial applications.