Recent Advances in Internet of Things and Emerging Social Internet of Things: Vision, Challenges and Trends

In recent years, the Internet of Things (IoT), together with its related emerging technologies, has been driving a revolution in the way people perceive and interact with the surrounding environment [...

Introduction to the Special Section on Social Computing and Social Internet of Things

The papers in this special section focus on social computing and the social Internet of Things (SIoT). SIoT is a new and latest paradigm that extends Internet of Things. This provides an ideal platform for interconnected devices and objects to effectively interact across social platforms for the betterment of the community on a whole. Any Social Internet of things based system means that the data is distributed in nature and focuses on the interest of a larger group of people than a particular individual. Thus social Internet of things have a wide scope and can be used to develop a wide range of applications that involves a group of people or community working towards accomplishing a common objective such as joint ventures, office setup, co-ownerships and so on. Social Computing may be defined as the study of the collaborative behavior of a group of computer users working on some common objectives

Guest Editorial Special Issue on Graph-Powered Machine Learning for Internet of Things

Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph data continuously, which stimulates fertilization and making use of advanced graph-powered methods on the diverse, dynamic, and large-scale graph IoT data

Edge-Enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical counterpart. The state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. The current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access, such as low-quality visualization. To this end, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm. Metaverse applications leverage edge devices' computing power to perform the required computations for heavy tasks, such as collision detection in the virtual universe and high-computational 3D physics in virtual simulations. The computational costs of a Metaverse entity, such as collision detection or physics emulation, are performed at the device of the associated physical entity. To validate the effectiveness of the proposed architecture, we simulate a distributed social Metaverse application. The simulation results show that the proposed architecture can reduce the latency by 50% when compared with cloud-based Metaverse applications

Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems

Lv, Z.; Song, H.; Lloret, J.; Kim, D.; De Souza, J. (2019). Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems. IEEE Access. 7:18070-18075. https://doi.org/10.1109/ACCESS.2019.2895441S1807018075

SciTech News Volume 71, No. 1 (2017)

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How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

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Emergence of data communication networks and the evolution of global data communication via the Internet have provided a potential platform for researchers around the globe to disseminate their research findings to the global community