物联网作为国内外新兴的热门技术,正在深刻地影响着人们的生产生活,它在带来诸多好处的同时也给信息存储领域带来挑战.物联网信息存储中心需要根据其数据特性结合分布式实时数据库信息存储管理的优点,设计与之相适应的数据存储方案,而...物联网作为国内外新兴的热门技术,正在深刻地影响着人们的生产生活,它在带来诸多好处的同时也给信息存储领域带来挑战.物联网信息存储中心需要根据其数据特性结合分布式实时数据库信息存储管理的优点,设计与之相适应的数据存储方案,而数据分配策略作为数据存储方案的关键技术是研究的重点.根据物联网传感器信息的海量性、时空相关性、访问失衡性和连续变化性,需要一种基于时域的数据分配模型与之相适应,以此设计出基于自适应时域负载反馈的动态数据分配策略(adaptive time domain data allocation,ATDA).策略根据数据特征,将静态数据分配问题归约成简单线性规划问题,同时采用自适应时域对负载信息进行反馈,最后设置动态负载门限函数实现数据的动态分配.实验表明,该策略与同类Random、Bubba算法相比,在系统短时域负载均衡(LBST)、系统数据迁移量(DM)方面具有更好的性能.展开更多
Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic infe...Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
文摘物联网作为国内外新兴的热门技术,正在深刻地影响着人们的生产生活,它在带来诸多好处的同时也给信息存储领域带来挑战.物联网信息存储中心需要根据其数据特性结合分布式实时数据库信息存储管理的优点,设计与之相适应的数据存储方案,而数据分配策略作为数据存储方案的关键技术是研究的重点.根据物联网传感器信息的海量性、时空相关性、访问失衡性和连续变化性,需要一种基于时域的数据分配模型与之相适应,以此设计出基于自适应时域负载反馈的动态数据分配策略(adaptive time domain data allocation,ATDA).策略根据数据特征,将静态数据分配问题归约成简单线性规划问题,同时采用自适应时域对负载信息进行反馈,最后设置动态负载门限函数实现数据的动态分配.实验表明,该策略与同类Random、Bubba算法相比,在系统短时域负载均衡(LBST)、系统数据迁移量(DM)方面具有更好的性能.
基金Supported by National Natural Science Foundation of China(No.61601039)financially supported by the State Key Research Development Program of China(Grant No.2016YFC0801407)+3 种基金financially supported by the Natural Science Foundation of Beijing Information Science & Technology University(No.1625008)financially supported by the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(NO.ICDD201607)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(NO.SKLNST-2016-2-08)financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(Grant No.CIT&TCD201504056)
文摘Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
