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Co-Design of Robust and Secure Networked Embedded Control Systems

ResearchersNils Vreman, Martina MaggioAnton Cervin, and Karl-Erik Årzén, in collaboration with the Embedded Systems Lab at Linköping University and the Real-Time Systems Lab at Sant'Anna School of Advanced Studies in Pisa

Funding: ELLIIT

Duration: 2010–

In the design of embedded control systems it is important to use the limited platform resources (e.g., CPU time, network bandwidth, energy) as efficiently as possible. At the same time, any optimistic assumptions at design time may lead to runtime failures caused by missed deadlines, lost controls, or energy depletion. In this project we aim to develop theory and co-design methodology for robust and secure embedded control systems that should operate efficiently also in the presence of uncertainties or unforeseen events. We will consider robustness towards, among other things, plant perturbations, malicious intrusion, execution-time overruns, and varying network capacity. One aspect of high interest is intrusion detection for highly resource-constrained control applications. In such a context, solutions have to deliver not only according to the traditional metrics of false-positive and false-negative, but also perform well according to new, specific quality metrics: detection latency, power consumption, processor load, and communication overhead.

During 2018, Nils Vreman joined the department as a PhD student and started to work in the project with Martina Maggio as main supervisor. The work is currently focused on mitigating side-channel attacks in real-time systems via schedule randomization. The idea is to in each hyperperiod choose randomly between a set of static schedules to hide the temporal execution. The minimal cardinality and the optimal diversity among the schedules have been studied.

In a parallel line of research, we have started to develop a new tool for analysis of real-time controller performance, called JitterTime. The tool has been used by visiting PhD student Paolo Pazzaglia to evaluate the performance of control tasks that experience deadline overruns.