[robotics-worldwide] [journals] Special Issue on 'Recent Advances in Machine Learning for Unmanned Vehicle Networks' - Deadline: 10 January 2020 - Hindawi, JCNC

Atakan Aral atakan.aral at tuwien.ac.at
Mon Dec 16 02:25:54 PST 2019


Call for original research for Special Issue of Journal of Computer
Networks and Communications (JCNC), Hindawi:

Recent Advances in Machine Learning for Unmanned Vehicle Networks
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.hindawi.com_journals_jcnc_si_397925_cfp_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=egvWCE9m1zLMLFSgNRm_izNmFTGZnYT1FJxlquhYnEk&s=YQvkB5PV83EXfLGqXpBLN-Py_7IkVImDG-eSZesFQtE&e= 

Unmanned Vehicle Networks (UVNs) are autonomous networks formed by
self-organizing aerial, ground, or underwater vehicles. Research in
these networks has steadily increased over recent years, especially in
relation to next-generation civil applications like consumer product
delivery, autonomous, and mobile environmental monitoring, search, and
rescue and disaster management. However, the challenges that accompany
many emerging applications for UVNs cannot alone be solved by static
optimization models, especially if they are applied in complex and
adverse environments.

Machine learning (ML) techniques possess the capability to learn and
implement optimal strategies depending on the specific environment,
thereby paving the way for new research in this field by not only
providing smarter algorithms and approaches but also enabling the
deployment of services and applications that could revolutionize the
way UVN systems operate. Although extensive research has focused on ML
techniques applied to single vehicle systems and applications, the
impacts of applying such techniques to a swarm of autonomous vehicles
remain to be explored. The challenge lies in exploiting the
information exchange and cooperation that is enabled by robust,
reliable, and powerful networking.

This special issue invites original research articles and review
articles that focus on robotic and ML-based networking problems in
relation to UVNs. Interdisciplinary ideas which address major
challenges in ML-based platforms for UVNs are particularly encouraged.

Potential topics include but are not limited to the following:

Modeling and analysis of cooperative systems in UVNs
Nature-inspired mobility management algorithms for UVNs
Deep, unsupervised, and reinforcement learning for UVN mobility management
ML-inspired network architecture, MAC and routing protocols for UVNs
Innovative analysis of data from UVN-aided Internet of Things (IoT) systems
UVN-aided crowdsensing systems
Continual learning and adaptation for UVNs
Edge computing for the real-time execution of ML and data analytics in UVNs
Energy efficient UVNs
Authors can submit their manuscripts through the Manuscript Tracking
System at https://urldefense.proofpoint.com/v2/url?u=https-3A__mts.hindawi.com_submit_journals_jcnc_iuvs_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=egvWCE9m1zLMLFSgNRm_izNmFTGZnYT1FJxlquhYnEk&s=81PEoVLW4bZLDx1M5RfxQEZoWWFQyZHw1VsAmIfG4gY&e= .

Submission Deadline Friday, 10 January 2020
Publication Date May 2020
Papers are published upon acceptance, regardless of the Special Issue
publication date.

Lead Guest Editor
Angelo Trotta, University of Bologna, Bologna, Italy
Guest Editors
Nicola R. Zema, Laboratoire de Recherche en Informatique (LRI),
Gif-sur-yvette, France
Gökhan Seçinti, Istanbul Technical University (ITU), Istanbul, Turkey
Atakan Aral, Institute of Information Systems Engineering, TU Wien,
Vienna, Austria
Kinda Khawam, University of Versailles, Versailles, France


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