*CALL FOR PAPERS *

Special Issue on

*DISTRIBUTED INTELLIGENCE AT THE EDGE FOR THE FUTURE INTERNET OF THINGS*  -
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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING////
/
*WEBSITE:*
https://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/call-for-papers/distributed-intelligence-at-the-edge-for-the-future


**IMPOTRANT DATES:*

*Submission deadline: 31-May-2021
Acceptance deadline: 31-Oct-2021
Publication: late 2021
*
SCOPE*:
Currently and even more in the future, business, industry, finance and 
retail, healthcare, media, entertainment and many others, are and will 
be completely managed, coordinated, and controlled using huge amounts of 
data. These operations are performed by the Internet of Things (IoT) 
system of connected computing, digital, and mechanical devices, all of 
them named using unique identifiers (UIDs) and able to transfer data 
over a network without human intervention.

To extract value from such massive data volumes, processing power 
offered by cloud computing is still utilized. However, streaming data to 
the cloud exposes some limitations related to the increased 
communication and data transfer, which introduces delays and consumes 
network bandwidth. Another limitation that cloud-based computing for IoT 
poses is limited network connectivity. Therefore, the adoption of cloud 
computing to process data generated by IoT devices may not be applicable 
at all to classes of applications such as those needed for real-time, 
low latency, and mobile applications. Therefore, it is beyond 
imagination to use cloud computing to collect data, store, and work out 
results. Therefore, there has been a move towards mobile communication 
and edge computing. Billions of devices have been connected to the 
Internet and created zettabytes of data items. The problem remains on 
how to extract information from collected data best.

The use of Artificial Intelligence, machine learning, neural network, 
and data analytic techniques in edge processing resulted in a new 
inter-disciplinary field that enables distributed intelligence with edge 
devices and is known as distributed edge AI or edge intelligence. 
However, research on edge AI and distributed edge AI is still relatively 
new, and thus models, techniques, and protocols supporting intelligent 
management, querying and mining of large-scale amounts of data produced 
at the edge are required. A lot of challenges related to providing edge 
intelligence include training edge devices, so they can become smarter 
and smarter. There is also a need of the presentation of the most recent 
outcome of research of distributed intelligence. This need could be 
illustrated by a smart city that contains for instance: garages, 
parkings, car washing systems, traffic unloading centrals etc. – usually 
belonging to different companies and running different protocols. A most 
likely scenario is that these devices could use different AI systems to 
support their activities. However, all of them are parts of one 
interconnected smart city; different AI systems must cooperate for 
common goal(s). Thus, we need distributed intelligence. Examples and 
different AI systems working for different edges could be multiplied; 
they support a variety of edges. All want to make money, kick 
competitors from the market out, and grab their systems. Furthermore, 
there is an emphasis on creating better software and algorithms that can 
run efficiently on resource-constrained devices. Moreover, purpose-built 
hardware at the edge is becoming increasingly important in the field of 
machine learning because companies can run software much more 
efficiently if they use specialized chips. Another key challenge of 
distributed edge AI will be the continued improvement of user interfaces 
that are used to communicate with other humans, including text, voice, 
vision, and different forms of body language.

These only are some of the challenges of edge intelligence. This field 
is expected to arise in the upcoming years and become an essential part 
of the next generation of the Internet of Things that expands its reach 
into almost every domain. Therefore, this Special Issue seeks to 
identify and provide high-quality research on recent advances on edge AI 
and distributed edge AI. We are interested in all aspects pertaining to 
this multidisciplinary paradigm.


*TOPICS OF INTEREST *
Topics of interest include, but are not limited to, the following:

· Distributed Intelligence at the Edge
· Modeling and Development of IoT applications using Edge AI
· Distributed AI with/for Secure Edge Networking
· Machine-Learning Algorithms for IoT Applications
· Optimization, Control, And Automation in Edge Computing for IoT
· Secure Intelligent IoT-Edge Systems
· Secure Intelligent Coordination and Networking Between IoT, Edge, and 
Cloud
· AI-Based Resource Allocation in IoT-Edge Systems
· Trust and Privacy Management in Intelligent IoT-Edge Systems
· Quality of Service and Energy Efficiency for Intelligent IoT-Edge Systems
· Data Mining and Big Data Analytics for Security and Resource 
Management in IoT-Edge Systems
· Distributed Ledger Technologies and Blockchain in IoT Environments

*SUBMISSION GUIDELINES*
Original, high-quality contributions that are not yet published or that 
are not currently under review by other journals or peer-reviewed 
conferences are sought. Papers will be peer-reviewed by independent 
reviewers and selected based on originality, scientific quality, and 
relevance to this Special Issue. The Guest Editors will make final 
decisions about the acceptance of the papers.
Authors should prepare their manuscript according to the Guide for 
Authors available from the online submission page of the Journal of 
Parallel and Distributed Computing.


*GUEST EDITORS*

Andrzej Goscinski
Deakin University, Australia
andrzej.goscinski@deakin.edu.au

Flavia C. Delicato
Fluminense Federal University, Brazil
fdelicato@gmail.com or fdelicato@ic.uff.br

Anna Kobusińska
Poznań University of Technology, Poland
Anna.kobusinka@cs.put.poznan.pl

Gautam Srivastava
Brandon University, Canada
srivastavag@brandonu.ca

Giancarlo Fortino
University of Calabria, Italy
g.fortino@unical.it


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