Shi Feifei, Li Qingjuan, Zhu Tao, Ning Huansheng
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Beijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, China.
Sensors (Basel). 2018 Jan 22;18(1):313. doi: 10.3390/s18010313.
With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.
随着物联网(IoT)的发展,越来越多的传感器、执行器和移动设备被部署到我们的日常生活中。结果是产生了海量数据,迫切需要挖掘这些海量数据背后隐藏的信息。然而,由多模态传感器或设备生成的物联网数据在格式、领域和类型上存在很大差异,这给机器的处理和理解带来了挑战。因此,为物联网添加语义成为一种压倒性的趋势。本文对物联网中的数据语义化进行了系统综述,包括其背景、处理流程、流行技术、应用、现有挑战和开放问题。它调查了为物联网数据添加语义的发展现状,主要涉及传感器数据,并指出了当前值得进一步研究的问题和挑战。