In recent years, there has been a significant interest in developing machine learning algorithms on embedded systems. This is particularly relevant for bare metal devices in Internet of Things, Robotics, and Industrial applications that face limited memory, processing power, and storage, and which require extreme robustness. To address these constraints, we present MicroFlow, an open-source TinyML framework for the deployment of Neural Networks (NNs) on embedded systems using the Rust programming language. The compiler-based inference engine of MicroFlow, coupled with Rust's memory safety, makes it suitable for TinyML applications in critical environments. The proposed framework enables the successful deployment of NNs on highly resource-constrained devices, including bare-metal 8-bit microcontrollers with only 2kB of RAM. Furthermore, MicroFlow is able to use less Flash and RAM memory than other state-of-the-art solutions for deploying NN reference models (i.e. wake-word and person detection), achieving equally accurate but faster inference compared to existing engines on medium-size NNs, and similar performance on bigger ones. The experimental results prove the efficiency and suitability of MicroFlow for the deployment of TinyML models in critical environments where resources are particularly limited.

With the ever increasing importance of web services and the Cloud as a reliable commodity to provide business value as well as consolidate IT infrastructure, electronic contracts have become very important. WS-Agreement has itself established as a well accepted container format for describing such contracts. However, the semantic interpretation of the terms contained in these contracts, as well as the process of agreeing to contracts when multiple options have to be considered (negotiation), are still pretty much dealt with on a case by case basis. In this paper we address the issues of diverging contracts and varying contract negotiation protocols by introducing the concept of a contract aware marketplace, which abstracts from the heterogeneous offers of different services providers. This allows for the automated consumption of services solely based on preferences, instead of additional restrictions such as understanding of contract terms and/or negotiation protocols. We also contribute an evaluation of several existing negotiation concepts/protocols. We think that reducing the complexity for automated contract negotiation and thus service consumption is a key for the success of future service and Cloud infrastructures.

IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems promising to limit the attack surface on IoT devices by formally specifying their intended network behavior. In this paper, we use SDN to enforce and monitor the expected behaviors of each IoT device, and train one-class classifier models to detect volumetric attacks. Our specific contributions are fourfold. (1) We develop a multi-level inferencing model to dynamically detect anomalous patterns in network activity of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection of anomalous flows. This provides enhanced fine-grained visibility into distributed and direct attacks, allowing us to precisely isolate volumetric attacks with microflow (5-tuple) resolution. (2) We collect traffic traces (benign and a variety of volumetric attacks) from network behavior of IoT devices in our lab, generate labeled datasets, and make them available to the public. (3) We prototype a full working system (modules are released as open-source), demonstrates its efficacy in detecting volumetric attacks on several consumer IoT devices with high accuracy while maintaining low false positives, and provides insights into cost and performance of our system. (4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.

Abstracting away low-level infrastructure by one-click deployment and automatically provisioning the entire stack to run an application. Abstraction and automation are only possible if we come up with common patterns and turn these patterns into a single concept. Developers will lose some flexibility, but gain a lot of speed and don't have to know about the underlying details. My job with the Mendix R&D team is to find the right patterns to include in our platform and to balance speed, ease-of-use, flexibility, and control. The result should be that we continuously improve the productivity of a broad spectrum of developers. It is easy to say that everyone needs to contribute to software because it is the core of the business, but how do we get all these people started and productive? That's where Machine Learning comes into play in the form of AI-assisted development. It is the logical next step for low-code platforms as it adds the next level of abstraction and automation.