“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

All one needs to know about fog computing and related edge computing paradigms: A complete survey

https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1007&context=aii_fac_pub

Machine Learning for Internet of Things Data Analysis: A Survey

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

IoT Middleware: A Survey on Issues and Enabling Technologies

Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.

Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

Wireless data broadcast is an efficient way of delivering data of common interest to a large population of mobile devices within a proximate area, such as smart cities, battle fields, etc. In this work, we focus ourselves on studying the data placement problem of periodic XML data broadcast in mobile and wireless environments. This is an important issue, particularly when XML becomes prevalent in today’s ubiquitous and mobile computing devices and applications. Taking advantage of the structured characteristics of XML data, effective broadcast programs can be generated based on the XML data on the server only. An XML data broadcast system is developed and a theoretical analysis on the XML data placement on a wireless channel is also presented, which forms the basis of the novel data placement algorithm in this work. The proposed algorithm is validated through a set of experiments. The results show that the proposed algorithm can effectively place XML data on air and significantly improve the overall access efficiency.

/pdf/organizing-xml-data-in-a-wireless-broadcast-system-by-4o2k8akath.pdf

Organizing XML Data in a Wireless Broadcast System by Exploiting Structural Similarity

Existing data placement algorithms for wireless data broadcast generally make assumptions that the clients' queries are already known and the distribution of access frequencies of their queries can be obtained a priori. Unfortunately, these assumptions are not realistic in most real life applications because new mobile clients may join in anytime and clients may be reluctant to disclose their queries due to privacy concerns. In this paper, we study the data placement problem of periodic XML data broadcast in mobile wireless environments. This is an important issue, particularly when XML becomes prevalent in today's ubiquitous Web and mobile computing devices. Taking advantage of the structured characteristics of XML data, we are able to generate effective broadcast programs based purely on XML data on the server without any knowledge of the clients' access patterns. This not only makes our work distinguished from previous studies, but also enables it to have broader applicability. We discuss structural sharing in XML data which forms the basis of our novel data placement algorithm. The proposed placement algorithm is validated through a set of experiments and the results show that our algorithm can effectively place XML data on air and significantly improve the overall access efficiency.

/pdf/effectively-delivering-xml-information-in-periodic-broadcast-2vw49a8w1w.pdf

Effectively Delivering XML Information in Periodic Broadcast Environments

The Internet of Things (IoT), in general, is a compelling paradigm that aims to connect everyday objects to the Internet. Nowadays, IoT is considered as one of the main technologies which contribute towards reshaping our daily lives in the next decade. IoT unlocks many exciting new opportunities in a variety of applications in research and industry domains. However, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is "Does the IoT already exist?". So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data and user interests. To answer this question, in this work, we conduct an in-depth analytical investigation on real IoT data. More specifically, we identify IoT data sources over the Web and develop a crawler engine to collect large-scale real-world IoT data for the first time. We make the results of our work available to the public in order to assist the community in the future research. In particular, we collect the data of nearly two million Internet connected objects and study trends in IoT using a real-world query set from an IoT search engine. Based on the collected data and our analysis, we identify the typical characteristics of IoT data. The most intriguing finding of our study is that IoT data is mainly disseminated using Web Mapping while the emerging IoT solutions such as the Web of Things, are currently not well adopted. On top of our findings, we further discuss future challenges and open research problems in the IoT area.

Searching for the Internet of Things on the Web: Where It Is and What It Looks Like

Nowadays cloud services are being increasingly used by professionals. A wide variety of cloud services are being introduced every day, and each of which is designed to serve a set of specific purposes. Currently, there is no cloud service specific search engine or a comprehensive directory that is available online. Therefore, cloud service customers mainly select cloud services based on the word of mouth, which is of low accuracy and lacks expressiveness. In this paper, we propose a comprehensive cloud service search engine to enable users to perform personalized search based on certain criteria including their own intention of use, cost and the features provided. Specifically, our cloud service search engine focuses on: (1) extracting and identifying cloud services automatically from the Web; (2) building a unified model to represent the cloud service features; and (3) prototyping a search engine for online cloud services. To this end, we propose a novel Service Detection and Tracking (SDT) model for modeling Cloud services. Then based on the SDT model, a cloud service search engine (CSSE) is implemented for helping effectively discover cloud services, relevant service features and service costs that are provided by the cloud service providers.

Toward Unified Cloud Service Discovery for Enhanced Service Identification.

Technology specific expert knowledge is often required to analyse security configurations and determine potential vulnerabilities, but it becomes difficult when it is a new technology such as Fog computing. Furthermore, additional knowledge is also required regarding how the security configuration has been constructed in respect to an organisation's security policies. Traditionally, organisations will often manage their access control permissions relative to their employees needs, posing challenges to administrators. This problem is even exacerbated in Fog computing systems where security configurations are implemented on a large amount of devices at the edges of Internet, and the administrators are required to retain adequate knowledge on how to perform complex administrative tasks. In this paper, a novel approach of translating object-based security configurations in to a graph model is presented. A technique is then developed to autonomously identify vulnerabilities and perform security auditing of large systems without the need for expert knowledge. Throughout the paper, access control configuration data is used as a case study, and empirical analysis is performed on synthetically generated access control permissions.

Yongrui Qin

Papers

Organizing XML Data in a Wireless Broadcast System by Exploiting Structural Similarity

Effectively Delivering XML Information in Periodic Broadcast Environments

Searching for the Internet of Things on the Web: Where It Is and What It Looks Like

Toward Unified Cloud Service Discovery for Enhanced Service Identification.

Security auditing in the fog