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*Emerging Trends and Challenges in Edge-Fog-Cloud Interplay in the
Internet
of Things (IoT)*
Elsevier Information Systems (Q1)
Submission Deadline: June 15, 2020
https://www.journals.elsevier.com/information-systems/call-for-papers/emerging-trends-and-challenges
The Intelligent Internet of Things (IoT) tsunami and public
embracement,
and the ubiquitous adoption of devices in virtually every industry
is
affecting every aspect of life, ranging from smart cars, smart
homes, smart
cities, smart factories to smart health, and smart environments.
The
integration of IoT and Cloud Computing has created another
paradigm, the
cloud IoT, to address some of the major challenges of IoT, such as
advanced
analytics capabilities and big data storage. However, in the cloud
IoT
model, the massive amount of data coming from “smart things” needs
to be
uploaded to the cloud, demanding a considerable amount of
available
communication bandwidth. Cloud-based IoT model cannot meet the
strict
computing time requirement in latency-critical applications
requiring a
real-time operation. An excellent example of such a case is
eHealth
applications such as arrhythmia monitoring and classification in
which
volume, variety, and velocity, as well as end-to-end response time
and
communication bandwidth, should be handled efficiently. Edge or
Fog
Computing has emerged as a solution to address the drawbacks of
Cloud-based
IoT solutions in which computing and storage resources are located
not only
in the cloud but also at the edges near the source of data.
Hierarchical
collaborative edge-fog-cloud architecture brings tremendous
benefits as it
makes possible to distribute the intelligence and computation
—including
data analysis, machine learning (ML) training, and decision
making—to
achieve an optimal solution while satisfying the given constraints
(i.e.,
optimization for energy versus optimization for latency) of each
use case.
However, due to the hierarchical, cross-layer, and distributed
nature of
this IoT model, many challenges from smart things, to network,
architecture, algorithms/software, and security still need to be
addressed
to develop consistent, suitable, scalable, safe, flexible and
power-efficient systems. The main objective of this Special Issue
(SI) is
to address all important aspects of emerging technologies for
edge-fog-cloud computing in IoT covering architectures,
techniques,
protocols, policies, applications, distributed machine learnings,
as well
as the interaction between edge, fog and cloud analytics. Authors
are
invited to submit high-quality papers containing original work
from either
academia or industry reporting novel advances in (but not limited
to) the
following topics:
- Distributed architectures and reference models.
- Resource Management Mechanisms.
- Service placement, migration and adaptation.
- Low-latency High-reliability energy-efficient network protocols
and
communications in edge-fog-cloud.
- The impact of 5G technology on edge-fog-cloud interplay.
- Edge-fog-cloud management protocols and policies for workload
communication and distribution.
- Privacy and security issues including secure firmware,
communications,
and strategies to detect and mitigate attacks, as well as Over the
air
updates for safety IoT devices.
- Trust-Oriented Designs of next-generation hierarchical IoT
systems.
- Optimization of the utility-privacy tradeoffs.
- Big-data analytics, machine learning algorithms, and
scalable/parallel/distributed algorithms.
- Collaborative distributed machine learning and data analytics
from
Edge to Fog and Cloud.
- Privacy-preserving Machine Learning and Data Processing
solutions in
hierarchical IoT solutions.
- Privacy-Preserving Machine Learning (PPML) and Multi-party
computation
(MPC) techniques.
- Performance monitoring & evaluation.
- Real-world experiences and use cases (eHealth, automotive,
transportation and logistics, retail, industry 4.0, etc.)
*Important Dates*
Submissions Deadline: June 15, 2020
Revision Due: September 15, 2020
Final Manuscript Due: October 30, 2020
First Reviews Due: August 15, 2020
Second Reviews Due/Notification: October 15, 2020
Publication Date: 2020
*Submission Guidelines*
Solicited original submissions must not be currently under
consideration
for publication in other venues. Author guidelines are at
https://www.journals.elsevier.com/information-systems/.
*Guest Editors*
*Farshad Firouzi*, Duke University
*Sebastián Ventura, *University of Córdoba
*Bahar Farahani*, Shahid Beheshti University
*Alysson Bessani,* Universidade de Lisboa
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You might also be interested in our IEEE Conference (IEEE IoT,
IEEE CAS,
IEEE RAS, IEEE CEDA, IEEE ComSoc). Selected best papers will also
be
published in SIs:
https://coinsconf.com/
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