Publications

Peer-to-peer botnets, as exemplified by the Storm Worm, and the spreading phase of Stuxnet, are a relatively new threat to security on the internet: infected computers automatically search for other computers to be infected, thus spreading the infection rapidly. In a recent paper, such botnets have been modeled using Stochastic Activity Networks, allowing the use of discrete-event simulation to judge strategies for combating their spread.

The quality of the communication links in a Wireless Sensor Network often shows significant asymmetry and variation over time, due to, for example, heterogeneous settings of the transmission power, moving nodes or chang- ing external interference. This makes it difficult for nodes to accurately maintain system-level properties, such as the minimum-energy path from the node to a given reference node, as required by many protocols.

In many applications of Wireless Sensor Networks (WSNs), heterogeneity is a common property in terms of different sensor types and different circumstances like node location, link quality, and local node density. In many applications, there are several different sensor types with entirely different Quality-ofService (QoS) requirements.

Synthesizing an implementation from a model in a predictable way is one of the major challenges in real-time systemdesign. In our previous work we addressed this problem by generating in real-time an execution path through a modeland by synchronizing the model time with the physical time. The execution path as observed in model time has a timedifference with the execution path as observed in physical time.

In this short paper I will address the question whether the methods and techniques we develop are applied well in industrial practice. To address this question, I will make a few observations from the academic field, as well as from industrial practice. This will be followed by a concise analysis of the cause of the perceived gap between the academic state-of-the-art and industrial practice.

The ever increasing performance gap between processors and memories is one of the biggest performance bottlenecks for computer systems. In this paper, we propose a task scheduling technique that schedules an application, modeled with a task graph, on a multiprocessor system-onchip (MPSoC) that contains a limited on-chip memory.

Synchronous dataflow graphs (SDFGs) are widely used to model streaming applications such as signal processing and multimedia applications in embedded systems. Trade-off analysis between performance and resource usage of SDFGs allows designers to explore implementation alternatives of a system while meeting its performance requirements and resource constraints.

Model-based testing is one of the promising technologies to increase the efficiency and effectiveness of software testing. In modelbased testing, a model specifies the required behaviour of a system, and test cases are algorithmically generated from this model. Obtaining a valid model, however, is often difficult if the system is complex, contains legacy or third-party components, or if documentation is incomplete.

Dataflow formalisms are useful for specifying signal processing and streaming applications. To adequately capture the dynamic aspects of modern applications, the formalism of Scenario-Aware Dataflow (SADF) was recently introduced, which allows analysis of worst/best-case and average-case performance across different modes of operation (scenarios).

Embedded control is a key product technology differentiator for many high-tech industries, including ASML. The strong increase in complexity of embedded control systems, combined with the occurrence of late changes in control requirements, results in many timing performance problems showing up only during the integration phase.