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Self-adaptive Device Management for the IoT Using Constraint Solving

Moualla Ghada, Bolle Sebastien, Douet Marc, Rutten Eric

Annals of Computer Science and Information Systems

2022

Vol. 30

641--650

In the context of IoT, Device Management (DM), i.e., remote administration of IoT devices, becomes essential to keep them connected, updated and secure, thus increasing their lifespan through firmware and configuration updates and security patches. Legacy DM solutions are adequate when dealing with home devices (such as TV set-top boxes) but need to be extended to adapt to new IoT requirements. Indeed, their manual operation by system administrators requires advanced knowledge and skills. Further, the static DM platform - a component above IoT platforms that offers advanced features such as campaign updates / massive operation management - is unable to scale and adapt to IoT dynamicity. To cope with this, this work, performed in an industrial context at Orange, proposes a self-adaptive architecture with runtime horizontal scaling of DM servers, with an autonomic Auto-Scaling Manager, integrating in the loop constraint programming for decision-making, validated with a meaningful industrial use-case.

Coordinated autonomic loops for target identification, load and error-aware Device Management for the IoT

Ayeb Neil, Rutten Eric, Bolle Sébastien, Coupaye Thierry, Douet Marc

Annals of Computer Science and Information Systems

2020

Vol. 21

491--500

With the expansion of Internet of Things (IoT) that relies on heterogeneous, dynamic, and massively deployed devices, device management (DM) (i.e., remote administration such as firmware update, configuration, troubleshooting and tracking) is required for proper quality of service and user experience, deployment of new functions, bug corrections and security patches distribution. Existing industrial DM platforms and approaches do not suit IoT devices and are already showing their limits with a few static home devices (e.g., routers, TV Decoders). Indeed, undetected buggy firmware deployment and manual target device identification are common issues in existing systems. Besides, these platforms are manually operated by experts (e.g., system administrators) and require extensive knowledge and skills. Such approaches cannot be applied on massive and diverse devices forming the IoT. To tackle these issues, our work in an industrial research context proposes to apply autonomic computing to DM platforms operation and impact tracking. Specifically, our contribution relies on automated device targeting (i.e., aiming only suitable devices) and impact-aware DM (i.e., error and anomalies detection preceding patch generalization on all suitable devices of a given fleet). Our solution is composed of three coordinated autonomic loops and allows more accurate and faster irregularity diagnosis, vertical scaling along with simpler IoT DM platform administration. For experimental validation, we developed a prototype that demonstrates encouraging results compared to simulated legacy telecommunication operator approaches (namely Orange).