Large-Scale Real-Time Semantic Processing Framework for Internet of Things
Large-Scale Real-Time Semantic Processing Framework for Internet of Things
复制标题
物联网大规模实时语义处理框架
DOI：
10.1155/2015/365372
复制
发表时间：
2015-01-01
影响因子：
2.3
通讯作者：
Zhang, Wen
中科院分区：
文献类型：
--
作者：
Chen, Xi;Chen, Huajun;Zhang, Wen
如今，先进的传感器技术与云计算和大数据正在产生大规模的异构和实时的物联网数据。为了充分利用数据，开发和部署无处不在的基于物联网的应用程序在我们日常生活的各个方面是非常迫切的。然而，物联网传感器数据的异构性、多样性、海量性和真实的实时性等特点给传感器数据的有效处理带来了挑战。语义Web技术被认为是物联网发展的关键。虽然大多数现有的工作主要集中在物联网数据的建模，注释和表示上，但很少有工作集中在大规模流式物联网数据的后台处理上。在本文中，我们提出了一个大规模的实时语义处理框架，并实现了一个弹性的分布式流引擎的物联网应用。该引擎基于流行的分布式计算平台SPARK，有效地捕获和建模各种物联网应用的不同场景。在此基础上，给出了一个家庭环境监测的典型应用案例，以说明我们的引擎的效率。结果表明，我们的系统可以扩展为大量的传感器流与不同类型的物联网应用。
Nowadays, the advanced sensor technology with cloud computing and big data is generating large-scale heterogeneous and real-time IOT (Internet of Things) data. To make full use of the data, development and deploy of ubiquitous IOT-based applications in various aspects of our daily life are quite urgent. However, the characteristics of IOT sensor data, including heterogeneity, variety, volume, and real time, bring many challenges to effectively process the sensor data. The Semantic Web technologies are viewed as a key for the development of IOT. While most of the existing efforts are mainly focused on the modeling, annotation, and representation of IOT data, there has been little work focusing on the background processing of large-scale streaming IOT data. In the paper, we present a large-scale real-time semantic processing framework and implement an elastic distributed streaming engine for IOT applications. The proposed engine efficiently captures and models different scenarios for all kinds of IOT applications based on popular distributed computing platform SPARK. Based on the engine, a typical use case on home environment monitoring is given to illustrate the efficiency of our engine. The results show that our system can scale for large number of sensor streams with different types of IOT applications.