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*** IEEE Internet of Things Journal Special Issue on Deep Reinforcement
Learning for Emerging IoT Systems **

Nowadays we are witnessing the formation of a massive Internet-of-Things
(IoT) ecosystem that integrates a variety of wireless-enabled devices
ranging from smartphones, wearables, and virtual reality facilities to
sensors, drones and connected vehicles. As IoT is penetrating every aspect
of people’s life, work and entertainment, an increasing number of IoT
devices and the emerging IoT applications are driving an exponential growth
in wireless traffic in the foreseeable future. As a result, current IoT
system architectures are facing significant challenges to handle millions
of devices; thousands of servers; and the transmission and processing of
large volume of data, etc. The growing diversity of IoT serveries and
complexity of mobile network architectures has made monitoring and managing
a multitude of IoT elements extremely difficult. Meanwhile, Deep
Reinforcement Learning (DRL) techniques have been seen as a promising
approach for building such complex IoT systems and to innovate at a rapid
pace. Over the last few years, DRL has achieved remarkable success in
different areas including games, robotics, Natural Language Processing
(NLP), healthcare, etc. Researchers in IoT areas also began to recognize
the power and importance of DRL and have been exploring different DRL
techniques to solve problems specific to the emerging IoT systems.
Researchers have explored the inherent power of fusion between DRL
technologies and IoT systems in both industrial and academic field. DRL
algorithms can provide effective and smart solutions for sequential
decision-making, optimization and control problems, dealing with incomplete
or inconsistent information related to IoT. This special issue aims to
foster the dissemination of high-quality research with emerging ideas,
approaches, theories and practice to resolve the challenging issues related
to DRL in IoT domain. Specially, the special issue is focused on
emphasizing the significance of DRL in modelling, identification,
optimization, and control of future IoT systems.



*Topics include, but are not limited to the following:*

-       Deep Reinforcement Learning driven energy-efficient networks and
services in IoT

-       Deep Reinforcement Learning for Quality-of-Experience Management in
IoT

-       Hybrid Deep Reinforcement Learning Models and Applications for IoT
in Industrial applications

-       Deep Reinforcement Learning based data analytics and decision
automation in IoT

-       Deep Reinforcement Learning architecture/algorithms for large-scale
IoT systems

-       Deep Neural Network modeling, analysis and synthesis techniques in
IoT

-       Deep Reinforcement Learning for IoT and sensor research: energy,
routing, prediction

-       Deep Reinforcement Learning for IoT security and privacy

-       Deep Reinforcement Learning testbed and experiment experiences in
IoT systems

-       Deep Reinforcement Learning for IoT enabled healthcare and
transportation systems


*Submission:*All original manuscripts or revisions to the IEEE IoT Journal
must be submitted electronically through IEEEManuscript Central,
http://mc.manuscriptcentral.com/iot. Author guidelines and submission
information can be found at http://ieee-iotj.org/.


*Important Dates:*

Submission Deadline: October 1, 2019

First Review Due: December 15, 2019

Revision Due: February 1, 2020

Acceptance Notification: March 1, 2020

Final Manuscript Due: March 15, 2020

Publication Date: 2020



*Guest Editors:*

Jia Hu, University of Exeter, UK

Peng Liu, Hangzhou Dianzi University, China

Hong Liu, East China Normal University, China

Obinna Anya, Google, USA

Yan Zhang, University of Oslo, Norway
IEEE Communications Society Tech. Committee on Computer Communications
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