| 摘要(英) |
The Internet of Things (IoT) is composed of various embedded devices that generate a large amount of sensor observation data. These observations are usually heterogeneous because they follow different data models or service agreements, which brings difficulties to data integration. To solve this problem, it is necessary to follow open standards specifically designed for IoT, such as the Open Geospatial Consortium (OGC) SensorThings API (STA). STA not only defines an IoT data model to capture the complexity of attributes and their relationships, but also provides a RESTful service interface to access IoT data. In order to manage the relationship between the properties of the Internet of Things, many STA implementations use relational database (RDB) solutions, such as FROST Server, GOST and Mozilla STA. However, RDB faces serious performance problems when managing large amounts of multi-dimensional data. Therefore, this research proposes a scalable and efficient data management solution for multi-dimensional IoT data query on STA services. Specifically, we use MongoDB as a data storage system, which is a distributed document-based database that supports table-join operators similar to RDB. In order to improve the performance of querying large amount of multi-dimensional IoT data, we apply the previously proposed Adaptive Multi-Attribute Indexing Framework (AMAIF) solution. In the experiment, we compare the proposed STA implementation with FROST Server. The experiment results show that in the multi-dimensional data query, the proposed system can effectively improve the speed of query response. And in terms of the data scalability, a large amount of data can be inserted quickly and can be easily divided and expanded in KVS stores. |
| 參考文獻 |
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