[en] Technological advances in recent years lead to the miniaturization
of a whole arsenal of different sensors. They can be used
to offer new services in eHealth applications, smart homes, robotics
or smart cities. With the increasing diversity and the cooperation
needed between these sensors in order to provide the best possible
services to the user the systems that use the data coming from these
sensors need to be able to handle conflicting information and thus
also conflicting actions. In this paper we propose an approach that
uses Hidden Markov Models in a first step to analyse the incoming
data and in a second step uses a rule engine in order to handle
the occurring conflicts.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
NEYENS, Gilles ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
ZAMPUNIERIS, Denis ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Conflict handling for autonomic systems
Date de publication/diffusion :
2017
Nom de la manifestation :
SASO 2017 - 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Organisateur de la manifestation :
IEEE Computer Society Technical & Conference Activities Board
Lieu de la manifestation :
Tucson, Etats-Unis - Arizona
Date de la manifestation :
September 2017
Manifestation à portée :
International
Titre de l'ouvrage principal :
Procedings of the 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Tucson, AZ, USA 18-22 September 2017
Maison d'édition :
IEEE Computer Society Publications
ISBN/EAN :
978-1-5090-6558-5
Pagination :
369-370
Peer reviewed :
Peer reviewed
Commentaire :
Procedings of the 11th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Tucson, AZ, USA 18-22 September 2017 - 2nd IEEE International Workshops on Foundations and Applications of Self* Systems
