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 containing temporal series information.If we separately deal with different sorts of data,we will miss useful information.This paper proposes a method to discover the correlation in multi-faceted data,which contains many types of data with temporal information,and our method can simultaneously deal with multi-faceted data.We transform high-dimensional multi-faceted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models,thenmine the correlation in multi-faceted data by discover the structure of the multivariate Gaussian Graphical Models.With a real data set,we verifies our method,and the experiment demonstrates that the method we propose can correctly find out the correlation among multi-faceted measurement data.
To support quality of service (QoS) management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network (BN) structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts,and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee,those causal relationships are first modeled quantitatively by BN parameter learning. Then,the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values,that is,quantitative QoS guarantee is reached. Simulations with three sequential stages:context discretization,QoS diagnosis and quantitative QoS guarantee,on a peer-to-peer (P2P) network,are discussed and our approach is validated to be effective.
LIN Xiang-tao,CHNEG Bo,CHEN Jun-liang,QIAO Xiu-quan State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China
随着高速发展的互联网和移动通信在业务层面的逐渐融合,国内运营商所采用的"封闭花园"模式的业务架构已经面临各种挑战,因此需要更多地考虑如何有效引入互联网业务和服务模式,不断提升用户体验。在电信运营商逐步开放其电信能力API的背景下,基于Mashup的业务构建模式,提出了一种云计算环境下的电信网络能力服务提供模式。该模式将Mashup的理念移植到电信能力上,并将电信能力封装成Web Element的形式呈现给用户,进一步提升了电信网络能力服务的抽象层次。这种电信能力应用模式打破了电信能力拘泥于手持终端的传统形式,提出了一种新的适合于Web2.0环境的电信网络能力服务提供模式。在"OMP(Open Mobile Internet Platform)应用运行和开发引擎算法及功能研发"项目中的试用证明了其可行性。
