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 of 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).