面向普适环境无线传感器网络的RESTful数据库REnvDB

F. D. Stefani, P. Gamba, E. Goldoni, A. Savioli, Davide Silvestri, Flavio Toffalini
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引用次数: 7

摘要

无线传感器网络(WSN)的每一个应用都要求从传感器节点收集的数据可以从外部访问,以便对其进行处理并从被监测的现象中获取信息。环境保护和农业是最有可能从无线传感器网络的部署中受益的领域。配备温度、湿度和光传感器的设备可以通过实时提供早期警报和有用的状态信息,在保护环境方面发挥作用。由于此类网络通常提供大量原始数据,因此还需要一种简单的方法来组织、存储和检索有用的信息。在这项工作中,我们提出了REnvDB (RESTful Environmental DataBase),这是一个专门为无处不在的监测应用而设计的无线传感器网络的新型数据库。该数据库的数据模型非常灵活,可以满足大多数典型环境监测系统的需求。我们还为数据库提供了RESTful接口,该接口可用于使用万维网的标准协议公开异构wsn收集的数据。由于其模块化设计和元数据的使用,我们的RESTful接口的行为也可以根据特定上下文的需求和约束轻松扩展或修改。最后,整个系统已在实际环境中实施和测试,作为监测集约化农业领域的更大项目的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
REnvDB, a RESTful Database for Pervasive Environmental Wireless Sensor Networks
Every application of a Wireless Sensor Network (WSN) requires the data collected from sensor nodes to be reachable from the outside, in order to allow its processing and to obtain information from the monitored phenomena. Environmental protection and agriculture are examples of areas most likely to benefit from the deployment of WSNs. Devices equipped with temperature, humidity and light sensors could play a role in preserving the environment by providing early alerts and useful status information in real-time. Since such networks usually provide a huge amount of raw data, an easy way to organize, store and retrieve useful information is also needed. In this work we present REnvDB (RESTful Environmental DataBase), a novel database for Wireless Sensor Networks specifically designed for pervasive monitoring applications. The data model of the database is flexible enough to fulfill the requirements of most of the typical environmental monitoring systems. We also provided the database a RESTful interface, which can be used to expose the data collected by heterogeneous WSNs using the standard protocols of the World Wide Web. Thanks to its modular design and the use of metadata, the behavior of our RESTful interface can also be easily extended or modified according to the requirements and constraints of a specific context. Finally, the entire system has been implemented and tested in a real environment as part of a bigger project for the monitoring of an intensive agricultural field.
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