Hierarchical Edge Computing: A Novel Multi-Source Multi-Dimensional Data Anomaly Detection Scheme for Industrial Internet of Things

Abstract

Every year, many people around the world die because of mining accidents. Industrial Internet of Things (IIoT) can be employed to sense public safety hazards and provide early warning of accidents, thereby ensuring safe operations at underground mining, personnel positioning, and specific items supervision and emergency response. Real-time data anomaly detection can predict the probability of occurrence of the abnormal event. However, massive heterogeneous monitoring data, poor wireless environment and data spatio-temporal association have posed a serious challenge to data anomaly detection for underground mining. Existing methods are mostly concerned about single data or processing at cloud platform, with little regard for the time and space association. Focus on the accuracy and timeliness of data anomaly detection, a novel multi-source multi-dimensional data anomaly detection scheme based on hierarchical edge computing model is presented in this paper. Firstly, a hierarchical edge computing model is proposed to realize load balance and low-latency data processing at the sensor end and base-station end. Then a single-source data anomaly detection algorithm is designed based on fuzzy theory, which can comprehensively analyze the anomaly detection results of multiple consecutive moments. Finally, a multi-source data anomaly detection algorithm executed at the base-station end is designed to consider the sensing data associated attributes of time and space. Experimental results reveal that the proposed scheme has higher detection accuracy and lower processing delay compared with traditional solutions.

Keywords

• Industrial Internet of Things (IIoT)
• underground mining
• anomaly detection
• multi-source multi-dimensional data
• edge computing