Call for Papers: 1st International Workshop on Deep Learning in Pervasive Computing (PerDL 2021)

1st International Workshop on
Deep Learning in Pervasive Computing (PerDL 2021)
part of
IEEE International Conference on Pervasive Computing (PerCom)
March 22-26, Kassel (Germany)
Call for Papers
The most recent advances in  artificial intelligence, as concerns both
software  and hardware,  are fostering  a multitude  of smart  devices
capable to  recognize and  to react  to music, images, as well  as to
other “stimuli”.  These  autonomous things, from robots to cameras to
healthcare  devices,  could exploit  recent  advances  in Internet  of
Things and resort to pervasive and distributed computing techniques in
order to avoid constant connection to the Cloud.

With  artificial   intelligence  embedded   in  a  great   variety  of
communicating  devices and  machines, we  are reaching  the so  called
pervasive  intelligence  scenario,  wherein machine  and devices  can
communicate  with each  other independently  of any human being.  The
proper integration  of deep  learning into  these smart devices could
boost definitively this trend into a common reality.

On  one hand,  local  chips  for deep  learning  may benefit  Internet
connectivity as well as proper and efficient pervasive and distributed
computing   techniques,    in   order   to   increase    their   local
performance. This  can be achieved  by exploiting the well-known edge
and  fog computing  paradigms, which  do not  suffer from  the latency
issues typical of traditional Cloud-based analyses. This is especially
true because  deep learning requires great computational power, which
could be properly distributed and  parallelized, and a great amount of
data, which  could also be  available in the  form of fast streams of
data managed pervasively by a multitude of devices. On the other hand,
deep learning techniques could help to improve the performance of both
parallel and  distributed computing techniques themselves,  by finding
out  opportune strategies  and mechanisms  to efficiently  distributed
workload and tasks across different connected smart nodes.

The  PerDL   workshop  aims   to  bring  together   practitioners  and
researchers working  on pervasive computing  and on deep  learning, by
soliciting  contributions  on,  but  not limiting  to,  the following

– Advances in pervasive and distributed deep learning techniques and

– Theories, models and novel algorithms for rendering deep learning
 suitable to pervasive and distributed computing;

– Novel applications of deep learning techniques in the context of
 pervasive and distributed computing;

– Technological innovations making possible the integration of deep
 learning and pervasive computing;

– Fog and edge computing techniques for deep learning;

– Researches to make the computational complexity of deep learning
 methods suitable for distributed devices;

– Studies to efficiently distribute and retrieve great amounts of data
 useful for deep learning algorithms;

– Deep learning techniques to improve the performance of pervasive and
 parallel computations.

The submitted papers may regard analytical, empirical, technological,
or methodological themes, as well as a combination of these. The
impact and influence of the contributions should be demonstrated in
the context of both pervasive computing and deep learning. Papers
applying known techniques from other fields are encouraged, provided
that the main topics of the workshop are properly addressed.

General Co-Chair and Program Chairs

Dr. Riccardo Pecori (University of Sannio, Italy)
Dr. Marta Cimitile (Unitelma Sapienza University, Italy)
Prof. Lerina Aversano (University of Sannio, Italy)

Important dates

Submission deadline of full papers: November 9th, 2020
Notification of acceptance: January 5th, 2021
Camera Ready: February 5th, 2021 (FIRM)

Submission Information

Paper submission must be done, as for the main conference, via
EDAS. If you do not have an EDAS account, please, register first (it’s
free) via the following link:

The direct submission link for PerDL 2021 is:

Special note:
PerCom 2021 and its workshops will follow a double-blind review
process. As a result, authors must make a good faith effort to
anonymize their submissions.