Predictive edge computing for time series of industrial IoT and large scale critical infrastructure based on open-source software analytic of big data
Predictive edge computing for time series of industrial IoT and large scale critical infrastructure based on open-source software analytic of big data
复制标题
基于开源软件大数据分析的工业物联网和大规模关键基础设施时间序列的预测边缘计算
DOI：
10.1109/bigdata.2017.8258103
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Emmanuel A. Oyekanlu
中科院分区：
文献类型：
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作者：
Emmanuel A. Oyekanlu
工业物联网(IIoT)在时延、带宽、成本、安全和连接等方面与一般物联网有很大不同。大多数现有的物联网平台都是针对一般的物联网需求而设计的，因此无法处理IIoT的特殊性。随着IIoT预期的大数据生成，迫切需要一个开源平台，能够最大限度地减少从边缘发送的数据量，同时可以通过执行高效的实时边缘分析来有效地监控和沟通大型工程系统的状况。在这项工作中，创建了一个工业机械状态监测开源软件数据库，该数据库配备了一本词典，小到足以放入边缘数据分析设备的内存中。数据库-词典系统将防止过多的工业和智能电网机器数据被发送到云中，因为只会发送来自EDGE词典和数据库的故障报告和必要的建议。使用基于Linux操作系统的开源软件(PythonSQLite)创建EDGE数据库和词典，以实现跨平台的可移植性，使大多数IIoT机器都能够使用该平台。在网络边缘使用众所周知的工业方法(例如峰度和偏度)进行的统计分析表明，所产生的机器信号和参考信号之间存在显著差异。这种数据库-字典方法是一种新的范式，因为它不同于传统方法，在传统方法中，数据库仅位于具有大容量内存和服务器的云中。开源部署还将有助于满足工业物联网联盟和开放雾架构的标准。
The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the large-scale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent. An open source software (Python SQLite) situated on Linux operating system is used to create the edge database and the dictionary so that inter-platform portability will be achieved and most IIoT machines will be able to use the platform. Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal significant differences between generated machine signal and reference signal. This database-dictionary approach is a new paradigm since it is different from legacy methods in which databases are situated only in the cloud with huge memory and servers. The open source deployment will also help to satisfy the criteria of Industrial IoT Consortium and the Open Fog Architecture.