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Contextual triple inference using a semantic reasoner rule to reduce the weight of semantically annotated data on fail–safe gateway for WSN
Journal of Ambient Intelligence and Humanized Computing Pub Date : 2021-01-06 , DOI: 10.1007/s12652-020-02836-9
Giridhar Urkude , Manju Pandey

The Internet of Things (IoT) combines miscellaneous technologies, which make it more diverse and applicable to different domains than a single technology. Semantic web technologies combined with IoT facilitate ubiquitous computing through machine-to-machine communication and semantic data management. Reusable domain ontologies, which provide a common semantic description for resources, are potential candidates for resolving the interoperability problem. The semantic annotation of sensor data using ontologies includes metadata and other thematic information regarding the data in the form of triples, on which reasoning can be performed to infer knowledge. The semantically annotated data are bulkier than the original data because of thematic metadata, and IoT devices have constrained resources to send this annotated data through a network. To reduce the weight of the annotated sensor data on networks, we established semantic data management by using semantic reasoner rules to reduce the number of triples from the semantic sensor data employing the unambiguous latent context information of a triple term. The triples can again be derived on the server instead of carrying the extra payload. A semantic rule was applied to the Jena semantic reasoner engine to reduce the triple on the annotated data. Furthermore, we developed a method for WSN fail–safe gateway on Zigbee mesh network that sends the semantically annotated sensor data through networks.



中文翻译:

使用语义推理器规则的上下文三重推理可减少WSN故障安全网关上的语义注释数据的权重

物联网(IoT)结合了各种技术,与单一技术相比,它使其更加多样化并适用于不同的领域。语义Web技术与IoT相结合,通过机器对机器通信和语义数据管理促进了无处不在的计算。提供资源通用语义描述的可重用域本体是解决互操作性问题的潜在候选者。使用本体的传感器数据的语义注释包括元数据和其他有关数据的主题信息,这些信息以三元组的形式存在,可以在其上进行推理以推断知识。由于主题元数据,语义注释的数据比原始数据大,并且IoT设备具有受限的资源,无法通过网络发送此注释的数据。为了减少网络上带注释的传感器数据的权重,我们通过使用语义推理规则来建立语义数据管理,以利用三项的明确潜在上下文信息从语义传感器数据减少三元组的数量。三元组可以再次在服务器上导出,而不用携带额外的有效负载。将语义规则应用于Jena语义推理器引擎,以减少带注释的数据的三元组。此外,我们为Zigbee网状网络上的WSN故障安全网关开发了一种方法,该方法可通过网络发送经过语义注释的传感器数据。我们使用语义推理规则建立语义数据管理,以减少语义的传感器数据中的三元组的数量,并利用三元组的明确潜在上下文信息。三元组可以再次在服务器上导出,而不用携带额外的有效负载。将语义规则应用于Jena语义推理器引擎,以减少带注释的数据的三元组。此外,我们为Zigbee网状网络上的WSN故障安全网关开发了一种方法,该方法可通过网络发送经过语义注释的传感器数据。我们使用语义推理规则建立语义数据管理,以减少语义的传感器数据中的三元组的数量,并利用三元组的明确潜在上下文信息。三元组可以再次在服务器上导出,而不是携带额外的有效负载。将语义规则应用于Jena语义推理器引擎,以减少带注释的数据的三元组。此外,我们为Zigbee网状网络上的WSN故障安全网关开发了一种方法,该方法可通过网络发送经过语义注释的传感器数据。

更新日期:2021-01-06
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