Tight coupling between computation, communication, and control pervades the design and application of cyber-physical systems (CPSs). Due to the complexity of these systems, advanced design procedures that account for these tight interconnections are paramount to ensure the safe and reliable operation of control algorithms under computational constraints. This paper presents the Simulator for Hardware Architecture and Real-time Control (Sharc) to assist in the co-design of control algorithms and the computational hardware on which they are run. Sharc simulates the execution of a user-specified control algorithm on a given processor microarchitecture configuration, evaluating how computational constraints affect the dynamical properties of the closed-loop system. We illustrate the power of Sharc by examples of MPC applied to adaptive cruise control and the stabilization of an inverted pendulum. Sharc can be found at github.com/pwintz/sharc.

The Internet-of-Things (IoT) is a large and complex domain. These systems are often constructed using a very diverse set of hardware, software and protocols. This, combined with the ever increasing number of IoT solutions/services that are rushed to market means that most such systems are rife with security holes. Recent incidents (e.g., the Mirai botnet) further highlight such security issues. With emerging technologies such as blockchain and software-defined networks (SDNs), new security solutions are possible in the IoT domain. In this paper we will explore future trends in IoT security: (a) the use of blockchains in IoT security, (b) data provenance for sensor information, (c) reliable and secure transport mechanisms using SDNs (d) scalable authentication and remote attestation mechanisms for IoT devices and (e) threat modeling and risk/maturity assessment frameworks for the domain.

Much legal evidence is being generated by and stored in information systems. In this paper we look at evidence from an auditing point of view. Auditors rely on evidence of the party being audited, who may have a legitimate or illegitimate interest to manipulate it. To assess the quality of audit evidence, we argue for an approach called model-based auditing. It is based on a mathematically precise model of the expected relationships between the flow of money and the flow of goods or services. Such equations are used for cross verification. If the equations do not hold, either something is wrong (violation) or some underlying assumption is false (exception). To show the usefulness of the approach, we look in particular at a case study of a legal dispute about automated contract monitoring. A precise revenue model is instrumental in demonstrating that the data set does indeed constitute appropriate evidence to settle the case.