Edge-Based Real-Time Sensor Data Processing for Anomaly Detection in Industrial IoT Applications

Abstract

The Industrial Internet of Things (IIoT), which uses devices with sensors to provide real-time insights into crucial processes, has completely changed how industries function. However, there are many problems associated with the sheer volume and speed of data created in industrial environments, particularly when it comes to anomaly detection. The development of edge-based real-time sensor data processing techniques was required because traditional cloud-based solutions frequently experience latency problems and privacy issues. This study suggests a novel method for IIoT applications that focuses on processing sensor data at the edge, close to the data source, for anomaly identification. We offer real-time analysis of sensor data without the need for continuous data transfer to the cloud by utilising the processing capabilities of edge devices, such as industrial gateways and embedded systems. To find anomalies in streams of real-time sensor data, our methodology integrates data pre-processing, feature engineering, and machine learning algorithms. This strategy not only lessens the strain on the network's bandwidth but also ensures quick reaction to urgent situations, cutting downtime and boosting operational effectiveness. Proposed system has adaptive learning features that enable it to continuously adjust to altering ambient factors and sensor properties, enhancing the precision of anomaly detection over time. We provide experimental findings that show how our edge-based anomaly detection system performs well in diverse industrial situations. The results show that, while protecting data privacy and minimising latency, our methodology outperforms conventional cloud-based methods in terms of anomaly detection performance.