Special section on Research challenges and directions of Data Mining in Edge Computing systems (VSI-dmec)

Overview:

An increasing traffic of valuable, heterogeneous and dynamic data constantly flows from billions of smart devices towards a plethora of innovative Internet-of-Things (IoT) applications. Pervasively deployed within the environment so to be involved in our daily activities, these devices typically represent precious information sources and/or actuators, often with limited resources. Therefore, an effective and efficient Data Mining activity at the network edge becomes necessary to address their computation, networking, mobility and energy issues, while still providing adequate timely information extraction. To this end, the adoption of distributed and decentralized computing paradigms is widely acknowledged as a suitable solution to alleviate the issues of scalability, latency and privacy, using centralized approaches, like Cloud Computing.

In this special section, new Data Mining approaches particularly tailored for the IoT scenario will be investigated, in particular, with respect to the promising, emerging novel computing paradigm of Edge Computing. Indeed, conventional Data Mining techniques need to be adjusted for optimizing the long pipeline which eventually leads, across data collection, processing and communication, to the information extraction at the network edge.

Authors are invited to submit outstanding and original unpublished research manuscripts focused on effective and efficient data mining techniques for the gathering, analysis and exploitation of the distributed data generated at the network edge that would adapt to high mobility, resource boundedness, dynamic topology and power constraints. The section will also emphasize the presentation of innovative aspects related to a simulation-based approach for quantitative evaluation by combining elements of information technology and telecommunications networking, the pros and cons of different Edge, Cloud or hybrid deployments in the light of the specific applications requirements. Both theoretical and experimental aspects are welcome.

Suggested topics include:

• Systems for and applications of data gathering, integration and analysis from distributed smart devices
• IoT data stream mining
• Data Mining for Event Stream Processing (ESP) and Complex Event Processing (CEP) at network edge
• Fuzzy-knowledge retrieval from smart devices data
• Mobility- and scale-tolerant sensors data mining.
• Large-scale and evolutionary algorithms for big data mining at the network edge.
• Security and privacy of edge computing systems
• Models, architectures, applications, and tools for data mining at the edge.
• Simulation models and tools for edge computing systems
• Use cases of data mining at the edge in key IoT domains (Smart Grid, Smart City, Smart Health, Smart Manufacturing, etc.)

Submission guidelines:

Unpublished manuscripts, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are completely re-written or substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.

Before submission, authors should carefully read the Guide for Authors available at https://www.journals.elsevier.com/computers-and-electrical-engineering . Authors should submit their papers through the journal’s web submission tool at evise.com/profile/#/COMPELECENG/login by selecting “VSI-dmec” from the “Issues” pull-down menu during the submission process. For additional questions, contact the Main Guest Editor.

Schedule 

Submission of manuscript: June 30, 2020

First notification: August 30, 2020

Submission of revised manuscript: September 30, 2020

Notification of the re-review: October 31, 2020

Final notification: November 30, 2020

Final paper due: December 31, 2020

Publication date: April 30, 2021

Guest Editors

• (Main Guest Editor) Dr. Claudio Savaglio, Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, Italy. csavaglio@dimes.unical.it
• Dr. Teemu Leppänen, Center for Ubiquitous Computing, University of Oulu, Finland. teemu.leppanen@oulu.fi
• Prof. Giuseppe Di Fatta, Department of Computer Science, University of Reading, UK. g.difatta@reading.ac.uk

Claudio Savaglio is a Research Fellow at the Dept. of Informatics, Modeling, Electronics and Systems (DIMES) of the University of Calabria, where he has received a Ph.D. in Information and Communication Technology. His research interests include autonomic and cognitive Internet of Things Systems, cyber-physical networks, Edge Computing, and Agent-oriented middleware and development methodologies. He has participated in regional, national and international research projects, funded by national organizations and the European Union. He has been visiting scholar at the Eindhoven University of Technology, the Netherland, the University of Texas and the New Jersey Institute of Technology, USA, and the Universitat Politècnica de València, Spain. He is the author of more than 30 papers in international journals, conferences and book chapters. He also served with different roles (chair, organizer, program committee member, guest editor, reviewer) in international journals, conferences and book series

Teemu Leppänen received his doctoral degree from the University of Oulu, Finland. He is currently a research scientist with the Center for Ubiquitous Computing, University of Oulu, Finland. His research interests include Internet of Things edge computing, with focus on distributed artificial intelligence, targeting energy efficiency and human-machine interactions. Another topic in his research is building real-world IoT infrastructures in urban environments. Currently he participates in a large 8-year research program to develop fundamental concepts of future sixth generation networks (6Genesis Flagship). He has visited the Institute of Industrial Science, the University of Tokyo, Japan in 2012-2013 and the University of Calabria, Italy, in 2019. He has authored 50 peer-reviewed articles in journals, research books and international conferences. He is Senior Member of IEEE.

Giuseppe Di Fatta is an Associate Professor of Computer Science and the Head of the Department of Computer Science at the University of Reading, UK. In 1999, he was a research fellow at the International Computer Science Institute (ICSI), Berkeley, CA, USA. From 2000 to 2004, he was with the High-Performance Computing and Networking Institute of the National Research Council, Italy. From 2004 to 2006, he was with the University of Konstanz, Germany. His research interests include data mining algorithms, distributed and parallel computing, big data in sciences and data-driven multidisciplinary applications. He has published over 100 articles in peer-reviewed conferences and journals. In 2013–18 he served in the editorial board of the Elsevier Journal of Network and Computer Applications. He is the co-founder of the IEEE ICDM Workshop on Data Mining in Networks and has chaired several international events, such as the 2015 International Conference on Internet and Distributed Computing Systems.