Treffer: pyDCOP, a DCOP library for IoT and dynamic systems

Title:
pyDCOP, a DCOP library for IoT and dynamic systems
Contributors:
Orange Labs R&D Rennes, France Télécom, Laboratoire Hubert Curien (LabHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom Paris (IMT), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Département Informatique et systèmes intelligents (FAYOL-ENSMSE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE), France Télécom Recherche et Développement (FTR&D)
Source:
International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019) ; https://hal.science/hal-02098294 ; International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019), May 2019, Montréal, Canada ; https://www2.isye.gatech.edu/~fferdinando3/cfp/OPTMAS19/
Publisher Information:
CCSD
Publication Year:
2019
Collection:
Université Jean Monnet – Saint-Etienne: HAL
Subject Geographic:
Time:
Montréal, Canada
Document Type:
Konferenz conference object
Language:
English
Accession Number:
edsbas.6F75AF75
Database:
BASE

Weitere Informationen

International audience ; This demonstration illustrates the newly developed Python-based framework, pyDCOP, which implements several state-of-the-art distributed constraint reasoning solution methods, provides utilities to deploy them over distributed infrastructures and also equip the system with resilience capabilities.The idea behind pyDCOP is to distribute agents over an Internet-of-Things infrastructure (e.g. Rapsberry Pis) to install collective decisions, as to implement Ambient Intelligence or Smart Home scenarios. Scenarios are modeled in a dedicated format, translated in a distributed constraint optimization or satisfaction problem, then pushed to the devices which coordinate using chosen protocols as to self-configure in a decentralized manner. Besides configuring the system in an optimal manner, it also provides a resilience framework, which equips the system with adaptation capabilities against unpredictable device removals. This mechanism is based on decision replication and a lightweight DCOP-based reparation mechanism.