Contact : Nawal Guermouche
Organisation : LAAS-CNRS
Année : Master 2 (A PhD can be undertaken after the end of the internship.)
Description du stage :
The aim of this internship is to study the problem of QoS prediction and their potential impact on the different underlying IoT systems.
This is essential to enable, each independent IoT system (e.g., transportation system), to self-manage and to self-adapt to its context to guarantee locally its QoS parameters. In addition, the mutual impacts that can exist between the different systems must be handled to ensure the awaited global QoS of the IoT SoS (e.g., QoS of a smart city). In this internship, machine learning techniques will be investigated and used to enable proactive and predictive QoS of IoT SoS.
Mots-clé : IoT, WoT, IoT ressources, QoS, IoT system of systems, service based systems, proactive management, prediction, machine learning, smart cities
Ressources supplémentaires : * Predictive-QoS-IoT-NG-1.pdf