More than 60 billion devices and sensors are expected to
be connected to the Internet by 2025. Our ever-increasing
ability to allocate, process, and extract valuable information from the
large-scale amounts of data generated at the network's edge triggered the IoT
era. Alongside the growth of the IoT capabilities, Artificial Intelligence
(AI) has emerged as the next technology phenomenon. For a long time,
centralized deployment models were utilized to facilitate such AI/ML
operations. Yet, a novel paradigm known as the Artificial
Intelligence of Things (AIoT) comes into
play. The possibility to improve the IoT capabilities that generate
enormous data, using the AI/ML approaches, which consume these data for
supporting intelligent management is extensive. By harnessing machine
intelligence's empowered abilities, IoT applications and architectures yield
advanced operations closer to the data source. This scenario relies on using
different AI systems to support the IoT and Edge-to-Cloud activities.
However, the need to operate the large-scale heterogeneous
devices and sensors while being performance-efficient in real-time is
challenging. Such challenges are related to providing edge
intelligence, including training edge devices, unstable
performance, uncertain security, and more challenges. These
challenges behove extensive attention from academia and industry. The
purpose of this workshop is to afford a venue covering all aspects of
cutting-edge technologies at the crossing of AI and IoT to advance their joint
applications.
Call for Contributions:
High-quality research contributions describing original and
unpublished results of conceptual, constructive, empirical, experimental, and
theoretical work in AIoT are cordially
invited for the workshop. The workshop solicits posters, demos, and full papers
contributions that address the workshop's themes and topics.
AIoTCon will emphasize PhD student's participation, postdoctoral
fellows, and other researchers from all over the world. AIoTCon would be an opportunity to bring together a
group of experts from diverse fields to discuss recent challenges/progress and
shed light on open questions to lead the next decade of research trends.
Invited Speakers
- Blesson Varghese, Associate Professor
at Queen's University Belfast, UK. Principal Investigator of the Edge Computing
Hub, Rakuten Mobile, Japan
Topic of interest:
We seek both conceptual and empirical papers offering new insights
to the development of AI/ML for IoT architectures and applications, focusing on
advanced AI/ML deployment for IoT computing, Edge/Fog Computing, Edge-to-Cloud
deployment models, Big Data management, 5G/6G networks, and Data Streaming.
Specific topics of interest include, but are not limited, to the
following:
AI/ML for resource management at IoT and Edge/Fog architectures
AI/ML for resource provisioning at IoT and Edge/Fog
architectures
AI/ML for Resource Allocation at IoT and Edge/Fog
architectures
AI/ML for workload characterization at IoT and
Edge/Fog architectures
AI/ML for application placement at IoT and Edge/Fog
architectures
Federated machine learning at IoT and Edge/Fog
architectures
Security, privacy, trust and
provenance at IoT and Edge/Fog architectures
Distributed Intelligence at the IoT and Edge/Fog
Online ML at the IoT and Edge/Fog
Distributed Ledger Technologies and Blockchain in IoT
Environments
AI/ML for Ultra-reliable low latency communication
AI/ML for Mission-Critical IoT
AI/ML purpose-built hardware at the edge
Submission guidelines:
Prospective authors are requested to submit new, unpublished
manuscripts for inclusion in the upcoming event described in this call for
papers. Paper submissions should follow the submission format and guidelines
at: https://www.just.edu.jo/icics/icics2021/submissions.html
Submission System:
https://easychair.org/conferences/?conf=icics2021
Important Dates
Paper Submission Deadline: 10 April 2021
Notification of Acceptance: 30 April 2021
Camera Ready Submission: 10 May 2021
Organizing Committee
Tomás F. Pena, Associated Professor. University of Santiago de
Compostela, CiTIUS research center, Spain
Feras M. Awaysheh, Assistant
Professor of Big Data Systems, Delta research center,
University of Tartu, Estonia