Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems

Distributed Machine Learning for Cyber-Physical Systems

Wireless sensor networks (WSN) are increasingly used for environmental monitoring over extended periods of time. To facilitate deployments in remote areas, sensor nodes are typically small, solar-powered devices with limited computational capabilities. Over the duration of the deployment, harsh weather conditions can lead to problems like mis-calibration or build-up of dust on sensors and solar...

Cyber-Physical Systems: Imminent Challenges

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small...

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a mac...

From machine-to-machine communications towards cyber-physical systems

In recent years, cyber-physical systems (CPS) have emerged as a promising direction to enrich the interactions between physical and virtual worlds. In this article, we first present the correlations among machine-to-machine (M2M), wireless sensor networks (WSNs), CPS and internet of things (IoT), and introduce some research activities in M2M, including M2M architectures and typical applications...