Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of executing arbitrary models on arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of IoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of various models, on a wide variety of low-power IoT boards, based on popular microcontroller architectures. U-TOE allows easily reproducible and customizable comparative evaluation experiments on a wide variety of IoT hardware all-at-once. The availability of a toolkit such as U-TOE is desirable to accelerate research combining Artificial Intelligence and IoT towards fully exploiting the potential of edge computing.

CubeSat design is facilitated by the increasing availability of open-source software in the domain, and a variety of low-cost hardware blueprints based on commodity micro-controllers. We attain the rock-bottom price to reach orbit as entities that design, launch and operate CubeSats started selling to multiple tenants tiny rack slots for low-power payloads that may be hosted on their CubeSat. The question arises of how to provide state-of-the-art security for software updates on a multi-tenant CubeSat, whereby mutual trust between tenants is limited. In this paper, we provide a case-study: ThingSat, a low-power payload we designed, is currently hosted on a CubeSat orbiting at 500km altitude operated by a separate entity. We then design Cubedate, a framework for securing continuous deployment of software to be updated on orbiting multi-tenant CubeSats. We also provide a highly portable open-source implementation of Cubedate, based on the IoT operating system RIOT, which we evaluate experimentally.

Abstract RIOT is a micro-kernel dedicated to IoT applications that adopts eBPF (extended Berkeley Packet Filters) to implement so-called femto-containers. As micro-controllers rarely feature hardware memory protection, the isolation of eBPF virtual machines (VM) is critical to ensure system integrity against potentially malicious programs. This paper shows how to directly derive, within the Coq proof assistant, the verified C implementation of an eBPF virtual machine from a Gallina specification. Leveraging the formal semantics of the CompCert C compiler, we obtain an end-to-end theorem stating that the C code of our VM inherits the safety and security properties of the Gallina specification. Our refinement methodology ensures that the isolation property of the specification holds in the verified C implementation. Preliminary experiments demonstrate satisfying performance.

Low-power operating system runtimes used on IoT microcontrollers typically provide rudimentary APIs, basic connectivity and, sometimes, a (secure) firmware update mechanism. In contrast, on less constrained hardware, networked software has entered the age of serverless, microservices and agility. With a view to bridge this gap, in the paper we design Femto-Containers, a new middleware runtime which can be embedded on heterogeneous low-power IoT devices. Femto-Containers enable the secure deployment, execution and isolation of small virtual software functions on low-power IoT devices, over the network. We implement Femto-Containers, and provide integration in RIOT, a popular open source IoT operating system. We then evaluate the performance of our implementation, which was formally verified for fault-isolation, guaranteeing that RIOT is shielded from logic loaded and executed in a Femto-Container. Our experiments on various popular micro-controller architectures (Arm Cortex-M, ESP32 and RISC-V) show that Femto-Containers offer an attractive trade-off in terms of memory footprint overhead, energy consumption, and security.

The following topics are dealt with: Internet of Things; embedded systems; microcontrollers; telecommunication power management; protocols; data privacy; wireless sensor networks; virtual machines; operating systems (computers); transport protocols.