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Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud

机译：基于移动边缘计算的大数据清洁工业传感器云

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摘要

With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial sensor-cloud system (SCS) has also received widespread attention. In the future, a large number of integrated sensors that simultaneously collect multifeature data will be added to industrial SCS. However, the collected big data are not trustworthy due to the harsh environment of the sensor. If the data collected at the bottom networks are directly uploaded to the cloud for processing, the query and data mining results will be inaccurate, which will seriously affect the judgment and feedback of the cloud. The traditional method of relying on sensor nodes for data cleaning is insufficient to deal with big data, whereas edge computing provides a good solution. In this article, a new data cleaning method is proposed based on the mobile edge node during data collection. An angle-based outlier detection method is applied at the edge node to obtain the training data of the cleaning model, which is then established through support vector machine. Besides, online learning is adopted for model optimization. Experimental results show that multidimensional data cleaning based on mobile edge nodes improves the efficiency of data cleaning while maintaining data reliability and integrity, and greatly reduces the bandwidth and energy consumption of the industrial SCS.
机译：随着5G的出现，工业的东西互联网已经发展迅速。工业传感器 - 云系统（SCS）也受到广泛的关注。将来，将添加大量集成传感器，即同时收集多分电数据的数据将被添加到工业SCS中。然而，由于传感器的恶劣环境，所收集的大数据不值得信赖。如果在底部网络收集的数据直接上传到云以进行处理，则查询和数据挖掘结果将不准确，这将严重影响云的判断和反馈。传统的依赖于传感器节点进行数据清洁的方法不足以处理大数据，而边缘计算提供了良好的解决方案。在本文中，基于数据收集期间基于移动边缘节点提出了一种新的数据清洁方法。在边缘节点处应用基于角度的异常检测方法，以获得清洁模型的训练数据，然后通过支持向量机建立。此外，在线学习采用模型优化。实验结果表明，基于移动边缘节点的多维数据清洁提高了数据清洁的效率，同时保持数据可靠性和完整性，大大降低了工业SC的带宽和能量消耗。

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