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Yongrui Qin

Bio: Yongrui Qin is an academic researcher from University of Huddersfield. The author has contributed to research in topics: XML & The Internet. The author has an hindex of 16, co-authored 92 publications receiving 1482 citations. Previous affiliations of Yongrui Qin include University of Southern Queensland & University of Adelaide.


Papers
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Journal ArticleDOI
TL;DR: Several inspiring use case scenarios of Fog computing are described, several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them are identified to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
Abstract: The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

341 citations

Journal ArticleDOI
TL;DR: The main techniques and state-of-the-art research efforts in IoT from data-centric perspectives are reviewed, including data stream processing, data storage models, complex event processing, and searching in IoT.

289 citations

Journal ArticleDOI
01 Dec 2017
TL;DR: The impact of security issues and possible solutions are determined, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.
Abstract: Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

267 citations

Proceedings ArticleDOI
Lina Yao1, Quan Z. Sheng1, Yongrui Qin1, Xianzhi Wang1, Ali Shemshadi1, Qi He1 
09 Aug 2015
TL;DR: A Collaborative Filtering method based on Non-negative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information to improve the recommendation accuracy.
Abstract: Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Non-negative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.

104 citations

Posted Content
TL;DR: Several inspiring use case scenarios of Fog computing are described, several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them are identified to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
Abstract: The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

77 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides a tutorial on fog computing and its related computing paradigms, including their similarities and differences, and provides a taxonomy of research topics in fog computing.

783 citations

Journal ArticleDOI
TL;DR: This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case and presents a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information.

690 citations

Journal ArticleDOI
TL;DR: 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 is conducted.
Abstract: 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.

573 citations

Journal ArticleDOI
TL;DR: This paper tries 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.
Abstract: 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.

524 citations