Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

Crowd-Centric Counting via Unsupervised Learning

Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based systems for counting targetsrely on localization and data association (i.e., individual-centric information) to infer the number of targets present in an area(i.e., crowd-centric information). However, many applications(e.g., affluence analytics) require only crowd-centric rather than individual-centric information. Moreover, individual-centric approaches may be inadequate due to the complexity of data association. This paper proposes a new technique for crowd-centric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. The proposed technique is validated via experimentation using an ultra-wideband sensor radar in an indoor environment.RYC-2016-1938

Evaluation of Storage Systems for Big Data Analytics

abstract: Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.Dissertation/ThesisMasters Thesis Computer Science 201

Deploying a middleware architecture for next generation mobile systems

Although 2G systems quite adequately cater for voice communications, today demand is for high-speed access to data centric applications and multimedia. Future networks have been designed to provide higher rates for data transmission, but this will be complemented by higher speed access to services via hotspots using secondary wireless interfaces such as Bluetooth or WLAN. With a wide range of applications that may be developed, a growing number of short range wireless interfaces that may be deployed, and with mobile terminals of different capabilities, a means to integrate all these variables in order to facilitate provision of services is desirable. This paper describes an architecture involving the use of middleware that makes software development independent of the specific wireless platfor

Gaspar data-centric framework

This paper presents the Gaspar data-centric framework to develop high performance parallel applications in Java. Our approach is based on data iterators and on the map pattern of computation. The framework provides an efficient data Application Programming Inter-face(API) that supports flexible data layout and data tiling. Data layout and tiling enable the improvement of data locality, which is essential to foster application scalability in modern multi-core systems. The paper presents the framework data-centric concepts and shows that the performance is comparable to pure Java code.(undefined)info:eu-repo/semantics/publishedVersio

Data-Centric Technoloiges: Patent and Copyright Doctrinal Disruptions

Data-centric technologies create information content that directly controls, modifies, or responds to the physical world. This information content resides in the digital world yet has profound economic and societal impact in the physical world. 3D printing and artificial intelligence are examples of data-centric technologies. 3D printing utilizes digital data for eventual printing of physical goods. Artificial intelligence learns from data sets to make predictions or automated decisions for use in physical applications and systems. 3D printing and artificial intelligence technologies are based on digital foundations, blur the digital and physical divide, and dramatically improve physical goods, objects, products, or systems. Data-centric technologies have crossed national borders and rapidly attained adoption, even while patent law and copyright law have been slow to respond. This Article focuses on 3D printing and artificial intelligence technologies and their doctrinal disruptions through a conceptual matrix formulation. It describes how recent litigation over data-centric technologies has repercussions for creators and inventors in the protection of data-centric innovations. Data-centric technologies’ doctrinal disruptions necessitate reevaluation of copyright and patent doctrines, which were spawned in an era of human/physical considerations to now including human/digital, non-human/physical, and non-human/digital considerations. The future of patent law and copyright law will be dominated by non-human/digital considerations and will impact innovation policy

Data-Centric Technologies: Patent and Copyright Doctrinal Disruptions

Data-centric technologies create information content that directly controls, modifies, or responds to the physical world. This information content resides in the digital world yet has profound economic and societal impact in the physical world. 3D printing and artificial intelligence are examples of data-centric technologies. 3D printing utilizes digital data for eventual printing of physical goods. Artificial intelligence learns from data sets to make predictions or automated decisions for use in physical applications and systems. 3D printing and artificial intelligence technologies are based on digital foundations, blur the digital and physical divide, and dramatically improve physical goods, objects, products, or systems. Data-centric technologies have crossed national borders and rapidly attained adoption, even while patent law and copyright law have been slow to respond. This Article focuses on 3D printing and artificial intelligence technologies and their doctrinal disruptions through a conceptual matrix formulation. It describes how recent litigation over data-centric technologies has repercussions for creators and inventors in the protection of data-centric innovations. Data-centric technologies’ doctrinal disruptions necessitate reevaluation of copyright and patent doctrines, which were spawned in an era of human/physical considerations to now including human/digital, non-human/physical, and non-human/digital considerations. The future of patent law and copyright law will be dominated by non-human/digital considerations and will impact innovation policy

Information-Centric Decision-Support Systems: A Blueprint for \u3cem\u3e‘Interoperability’\u3c/em\u3e

For the past 20 years the US military services have suffered under the limitations of stove-piped computer software applications that function as discrete entities within a fragmented data-processing environment. Lack of interoperability has been identified by numerous think tanks, advisory boards, and studies, as the primary information systems problem (e.g., Army Science Board 2000, Air Force SAB 2000 Command and Control Study, and NSB Network-Centric Naval Forces 2000). Yet, despite this level of attention, all attempts to achieve interoperability within the current data-centric information systems environment have proven to be expensive, unreliable, and generally unsuccessful

Applied Koopman Operator Theory for Power Systems Technology

Koopman operator is a composition operator defined for a dynamical system described by nonlinear differential or difference equation. Although the original system is nonlinear and evolves on a finite-dimensional state space, the Koopman operator itself is linear but infinite-dimensional (evolves on a function space). This linear operator captures the full information of the dynamics described by the original nonlinear system. In particular, spectral properties of the Koopman operator play a crucial role in analyzing the original system. In the first part of this paper, we review the so-called Koopman operator theory for nonlinear dynamical systems, with emphasis on modal decomposition and computation that are direct to wide applications. Then, in the second part, we present a series of applications of the Koopman operator theory to power systems technology. The applications are established as data-centric methods, namely, how to use massive quantities of data obtained numerically and experimentally, through spectral analysis of the Koopman operator: coherency identification of swings in coupled synchronous generators, precursor diagnostic of instabilities in the coupled swing dynamics, and stability assessment of power systems without any use of mathematical models. Future problems of this research direction are identified in the last concluding part of this paper.Comment: 31 pages, 11 figure