Neïl Ayeb - Administration autonomique et décentralisée de flottes d'équipements de l’Internet des Objets

Organized by: 
Neïl Ayeb
Neïl Ayeb

Le jury est composé de :

  • Laurence DUCHIEN - Professeure, Université de Lille, rapporteuse
  • Thomas LEDOUX - Professeur, IMT Atlantique - Nantes, rapporteur
  • Françoise BAUDE - Professeure, Université de Nice, examinatrice
  • Thierry MONTEIL - Professeur, INSA Toulouse, examinateur
  • Noël DE PALMA - Professeur, Université Grenoble-Alpes, examinateur
  • Eric RUTTEN - Chargé de Recherche - HC , HDR, INRIA, directeur de thèse
  • Sébastien BOLLE - Responsable de Programme de Recherche, Orange Labs, co-encadrant
  • Thierry COUPAYE - Directeur de Domaine de Recherche, HDR, Orange Labs, co-encadrant
  • Marc DOUET - Responsable de Projet de Recherche, Orange Labs, invité


With the expansion of Internet of Things (IoT) that relies on heterogeneous; dynamic; and massively deployed devices; Device Management (DM), which consists of firmware update, configuration, troubleshooting and tracking, is required for proper quality of service and user experience, deployment of new functions, bug fixes and distribution of security patches.
Existing Home and IoT industrial DM platforms are already showing their limits with a few static home and IoT devices (e.g., routers, TV Decoders). Currently, these platforms are mainly manually operated by experts such as system administrators, and require extensive knowledge and skills. Heterogeneity implies that devices have diverse compute and network capabilities. Dynamicity translates to variation of devices environments (e.g., network quality, running services, nearby devices). The massive aspect is reflected in fleets composed of billions of devices as opposed to millions currently.
Therefore, IoT device administration requires launching administration operations that assure the well-functioning of device fleets. These operations are to be adapted in terms of nature, speed, target, accordingly to devices current service requirements, computing capabilities and network conditions. Existing manually operated approaches cannot be applied on these massive and diverse devices forming the IoT.
To tackle these issues, our work in an industrial research context, at Orange Labs, proposes applying autonomic computing to platform operation and distribution. It aims to ensure that administration requirements of a device fleet are automatically fulfilled using the optimal amount of resources and with the least amount of execution errors.
Specifically, our contribution relies on four coordinated autonomic loops. The first two loops are responsible for handling fleet variation and update operations dispatching, while the remaining two others focus on vertical and horizontal scalability. Our approach allows automatic administration platform operation, more accurate and faster error diagnosis, vertical and horizontal scaling along with simpler IoT DM platform administration.
For experimental validation, we developed two prototypes: one that demonstrates the usability of our approach with Orange's industrial IoT platform for its piloting, while the other one demonstrates vertical scalability using extended open-source remote administration software. Our prototypes show encouraging results, such as two times faster firmware upgrade operation execution speed, compared to existing legacy telecommunication operator approaches.