Theoretical and Empirical Aspects of Big Data in Social IoT

Aim and Scope

Nowadays, the Internet of Things (IoT) and Big Data are currently hot research topics. IoT and big data together are going to change the pace of development of organizations and businesses. Social IoT (SIoT) is an emerging paradigm of IoT in which different IoT devices interact and establish relationships with each other to provide proactive and smart services. Social IoT uses distributed sensors and other connected devices to improve social solutions in fields such as energy, utility services, and transportation. There is no doubt that sensors for collecting vast amounts of data are set to become an integral part of our lives. Big data gives a new path for data management. There is no denying that today enormous data is present to organizations due to IoT. Big Data, combined with appropriate and fast analysis, translates into better, smarter, and more productive lives for many users of IoT technologies. Smart homes, as another example, make use of real-time analytics and monitoring alongside automation and other communications technologies. By comparing the results of real-time analytics with patterns mined through Big Data, potential disasters like floods, fires, or burglaries can be flagged remotely and avoided entirely for users of smart home devices.

Moreover, the collection, manipulation, and storage of massive data cause many security and privacy issues. Again, inference, inter-operability, and complexity are some of the issues caused by big data management. Big data and social IoT collaboratively create an intelligent network with new data mining techniques to extract valuable information of users by monitoring their behavior or actions. Businesses and service providers yield better services as they have enough opportunities to stand out from their competitors by interacting with their customers. Enterprises can utilize social IoT platforms to influence users’ lives by interfacing directly with potential users to provide fast and reliable service to users. Social media, IoT and big data have changed the concept of privacy for the users. Some researchers have said that social media, IoT, and big data will make the concept of privacy non-existent very soon. Consumers feel to have a more digital presence, which compels a user to share every big or small thing of their life on social media platforms. Organizations have started monitoring this behavior of users and try to provide media messages based on personalization and geographical location of the users as personal devices share data with the IoT network. This Special Issue aims to provide a platform for researchers and practitioners to exchange and publish the latest challenges, research trends, results, and so on the theoretical and empirical aspects of big data in social IoT. This Special Issue aims to develop solutions that effectively integrate these technologies so that organizations can build strong relationships with their users.

This Special Issue is in memoriam of our colleague and friend Professor Antonio “Picus” Picariello, who passed away on June 23 2020 at the age of 55. Picus received the Ph.D. degree in Computer Engineering from the University of Naples Federico II, Italy, in 1998. He was a Full Professor with the Department of Electrical Engineering and Information Technology at the University of Naples Federico II. He was the Director of the National Lab of Computer Science, Telematics and Multimedia of the Italian Consortium on Computer Science and Engineering. He worked in the field of Multimedia Database and Multimedia Information Systems, Multimedia Ontology and Semantic Web, Natural Language Processing, Big Data, Big Data Analytics, and Social Networks Analysis. His last class he had before passing away has been Big Data Analytics. This Special Issue spans over the latest topics Picus was focusing before his departure. The topics relevant to this Special Issue include but are not limited to:

• Big Data virtualization in Social IoT
• Deep Learning for Big Data in Social IoT
• Big Data in Social IoT for healthcare
• Big Data in Social IoT for access control systems
• Big Data in Social IoT for transportation
• Big Data in Social IoT for smart cities
• Big Data in Social IoT for energy management
• Ambient Intelligence in Social IoT
• Context-aware computing of Dig Data in Social IoT
• Big Data Analysis in Social IoT (SIoT)
• Big Data inter-operability in Social IoT
• Big Data and Biometrics in Social IoT
• Semantic Web applied to Big Data and Social IoT
• Natural Language Processing in Social IoT
• Security, privacy and trust aspects of Big Data in Social IoT

Submission Details

Each paper for submission shall strictly follow the instructions given in the “Guide for Authors” at https://www.elsevier.com/journals/big-data-research/2214-5796/guide-for-authors. Note that published papers and those currently under review by other journals or conferences are prohibited. Each paper will be reviewed rigorously by three or more domain experts depending on the decision of assigned associate editor, and possibly in two rounds, i.e., minor/major revisions will undergo another round of review. Prospective authors are invited to submit their papers directly via the online submission system at https://www.editorialmanager.com/bdr/default.aspx. For more information, please contact the Guest Editors.

Important Dates

Submission deadline: April 30, 2021

First round notification: July 30, 2021

Revised version due: September 10, 2021

Final notification: October 05, 2021

Final version due: October 05, 2021

Publication tentative date: As per journal’s policy

Guest Editors

Prof. B. B. Gupta, National Institute of Technology, Kurukshetra, India

Prof. Aniello Castiglione, University of Naples Parthenope, Naples, Italy

Prof. Yaliang Zhao, Henan University, Kaifeng, China

Prof. Laurence Yang, St. Francis Xavier University, Antigonish, Canada

Prof. Vincenzo Moscato, University of Naples Federico II, Naples, Italy