Fog Computing with Distributed Database
Fog Computing with Distributed Database
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DOI：
10.1109/aina.2018.00096
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Tsukasa Kudo
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文献类型：
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作者：
Tsukasa Kudo
近年来，随着物联网的进步，各种传感器的输入数据被积累在云服务器上，并作为大数据用于各种分析。另一方面，为了将大量数据传输到云端服务器，存在网络带宽限制、传感器反馈控制延迟等问题。针对这些问题，人们提出了雾计算，其中在传感器附近安装的雾节点对传感器数据进行初步处理，仅将其处理结果传输到云服务器。然而，在该方法中，在云服务器处的各种分析需要传感器的原始数据的情况下，必须额外地传输这样的数据。也就是说，需要一种机制来管理整个系统的数据并相互利用它。在本文中，我提出了一个由三个层次组成的数据模型：第一层保存原始传感器数据并放置在雾节点中；第二层保存原始传感器数据并放置在雾节点中。第二级保存初级处理提取的提取数据；第三级保存分析结果数据。第二层和第三层放置在云服务器中。并且，通过使用分布式数据库构建该数据模型，可以高效地从云服务器引用雾节点中的任意原始传感器数据。此外，我使用 MongoDB（一种 NoSQL 数据库）以两种方式实现此参考处理，以评估此数据模型。并且，我表明有必要根据系统环境来选择参考方式：网络带宽、雾节点和云服务器的数据库性能以及雾节点的数量。
In recent years, with the progress of IoT, the entry data from various sensors is accumulated on the cloud server and used for various analyses as big data. On the other hand, in order to transfer a large amount of data to the cloud server, there were the problems such as the restriction of network bandwidth, and the delay of feedback control of the sensors. For these problems, Fog computing has been proposed in which the primary processing of the sensor data is performed at the fog node installed near the sensors, and only its processing results are transferred to the cloud server. However, in this method, in the case where the original data of the sensor is required for various analyses at the cloud server, such a data must be transferred additionally. That is, a mechanism is necessary to manage the data of the entire system and to mutually utilize it. In this paper, I propose a data model which consists of three levels: the first level saves the original sensor data and is placed in the fog node; the second level saves the extraction data extracted by the primary processing; the third level saves the analysis results data. The second and third levels are placed in the cloud server. And, by constructing this data model with a distributed database, it can be performed efficiently to refer the arbitrary original sensor data in the fog nodes from the cloud server. Moreover, I implement this reference processing in two ways using MongoDB, which is a kind of NoSQL database, to evaluate this data model. And, I show it is necessary to select the reference way according to the system environment: the network bandwidth, the database performance of the fog node and cloud server, and the number of the fog nodes.