IEEE 18th International Conference on Intelligent Environments (IE2022)

             June 20-23, 2022 – Biarritz, France

             In cooperation with IEEE Systems, Man, and Cybernetics Society

             https://ie2022.iutbayonne.univ-pau.fr/
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Important News
~~~~~~~~~~~~~~
– Accepted papers list now online
(https://ie2022.iutbayonne.univ-pau.fr/accepted-papers/)
– Registrations are now open
(https://ie2022.iutbayonne.univ-pau.fr/registration/)
=> early bird deadline: *May 15, 2022*
– Hybrid mode (online/on-site)
=> We strongly encourage you to join the many participants who have
already confirmed their presence
    on site to facilitate exchanges and moments of conviviality. In
particular, we hope to meet on site
    participants from nearby countries.

Scope
~~~~~
Intelligent environments refer to physical spaces in which information
and communication
technologies are woven with sensing/acting technologies in order to
create interacting
spaces enhancing occupants’ experience. The ultimate objective of such
environments is to
provide services to occupants, enrich their activities but also to
develop their awareness.

As in previous years, IE will host a dozen workshops and tutorials in
the most current
fields related to smart environments. Also, special sessions, demos,
posters and an industrial forum
  will be organised as usual by the IE community.

Topics include but are not limited to:
– Advances in theories for the design, implementation, integration and
evaluation of smart spaces,
– Novel architectures and middleware for the integration of devices,
edges, and clouds
– Software facilities to develop, deploy, monitor, update applications
for smart spaces,
– Interaction techniques using voice, gesture, eyes, etc. in smart spaces,
– Robotic technologies in smart spaces to assist human or to manage
resources,
– Planning solutions to better use limited resources in smart spaces,
– Machine learning techniques for novel applications, including
federated learning, few shot learning, etc.
– Solutions to deal with transversal properties including security,
– Privacy, availability, transparency, or explainability in smart spaces.
– Novel applications in smart homes, smart building, smart cities, smart
plants and smart grid

Invited talks
~~~~~~~~~~~~~

Opportunistic Collaborative Learning in Pervasive Computing Applications
Christine Julien [ON-SITE, confirmed]

Smartphones, wearable devices, and other computational units that are
ubiquitous in our environments
are imbued with increasingly more complex sensing, computational, and
communication capabilities.
These devices can generate (and distribute) vast quantities of data that
can be used to build
sophisticated machine learning models for a variety of applications,
e.g., classification and
recommendation. Opportunistic collaborative learning (OppCL) is a
framework for individual devices
in pervasive computing environments to train a deep learning model that
caters to the device’s
personalized needs. In OppCL, each device maintains a local,
personalized model. When the device
encounters another device via peer-to-peer communication, it shares its
model parameters and asks
the neighbor to train the model using the neighbor’s local data. This
talk will present the
motivation and use cases behind the creation of OppCL and a basic model
for collaboratively
training personalized models using opportunistically available
neighboring devices (and their
data!). The talk will discuss multiple schemes for incorporating
encountered model updates as
well as techniques for handling heterogeneity in the pervasive computing
environment, including
bandwidth and latency constrained communication links as well as
computationally constrained
neighboring devices. The talk will also include presentations of
practical implementations for
OppCL in both large scale simulation and in real world devices. The talk
will close with a look
forward into open challenges and opportunities in employing OppCL to
diverse pervasive
computing applications.

Towards Distributed Intelligence in Future Edge Computing
Jiannong Cao [ON-LINE, confirmed]

The emerging advanced IoT applications in connected healthcare,
industrial internet, multi-robot systems,
and other areas demand higher intelligence of the connected devices,
larger scale of the systems, and
better decision-making leveraged by analyzing the data being
continuously generated and the advancement
of AI technologies. In this context, centralized cloud computing would
face high data transmission cost,
high response time, and data privacy issues. The edge cloud paradigm
seeks to alleviate these
inefficiencies by moving the computation and analytics tasks closer to
the end devices. It facilitates
the evolution of IoT from instrumentation and interconnection to
distributed intelligence. This talk
focuses on future collaborative edge computing where edge nodes share
data and computation resources
and perform tasks by leveraging distributed intelligence. It covers the
major problems in distributed
collaboration at the edge we are currently studying, namely
collaborative task execution, distributed
machine learning, and distributed autonomous cooperation. Solutions need
to address the challenging
issues such as distributed data sources, conflicting network flows,
heterogeneous devices, consistency,
and mutual influence during the training.

Reflections on trustworthy and ethical technology from a Human Computer
Interaction perspective
Maria-Antonietta Grasso [ON-SITE, TBC]

The objective to help people flourish has been a part of the agenda of
the Human Computer Interaction
community since its early days. Current concerns around the impacts of
technology and its ethics make
  these early endeavours even more relevant and prominent. The reasons
are many and relate to issues of
  broad societal concern such as sustainability, work organisation and
perpetration of social inequities.
  In this talk I will first discuss emerging attributes that help to
assess a technology as trustworthy
  and ethical. I will then draw on some examples of projects we have
carried out in our industrial lab
  to explain how we included a value orientation in our research and
will propose some concrete
  methodologies we have found useful.

Program Chairs
~~~~~~~~~~~~~~
Song Guo, Hong-Kong PolyU, China
Philippe Lalanda, UGA, France

General Chairs
~~~~~~~~~~~~~~
Schahram Dustdar, TU Wien, Austria
François Portet, UGA, France

Local Chair
~~~~~~~~~~~
Philippe Roose, LIUPPA/E2S, University of Pau, France