Scientific publications

Smart system of systems adapt to their context, current situation, and configuration. To engineer such systems’ behavior, we need to design and eval-uate system-level control strategies and the intelligent management of key scenarios. We propose a model-based approach called probabilistic system summaries to explore related design choices, e.

Wireless Sensor Networks (WSNs) are commonly deployed in dynamic environments where events, such as moving sensor nodes and changing external interference, impact the performance, or Quality-of-Service (QoS), of the network. QoS is expressed by the values of multiple, possibly conflicting, network quality metrics, such as network lifetime and maximum latency of communicating a packet to the sink.

This paper proposes a modeling approach to capture the mapping of an application on a platform. The approach is based on Scenario-Aware Dataflow (SADF) models. In contrast to the related work, we express the complete design-space in a single formal SADF model. This allows us to have a compact and explorable state-space linked with an executable model capable of symbolically analyzing different mappings for their timing behavior.

Constructing a model of a system for model-based testing, simulation, or model checking can be cumbersome for existing, third party, or legacy components. Active automata learning, a form of black-box reverse engineering, and in particular Angluin’s L⋆ algorithm, support the automatic inference of a model from a System Under Learning (SUL), through observations and tests.

Accurate link quality estimation is an important building block in quality aware routing. In an inherently lossy, unreliable and dynamic medium such as wireless, the task of accurate estimation becomes very challenging. Over the years ETX has been widely used as a reliable link quality estimation metric.

The timed dataflow model of computation is a useful performance analysis tool for Electronic System Level Design automation and embedded software synthesis. Its determinism gives it strong analysability properties. It is expressive enough to cover a large class of applications and platforms. The trend however, in both embedded applications and their platforms is to become more dynamic, reaching the limits of what the model can express and analyse with tight performance guarantees.

This document addresses the way an architect can do his work. It shows many tools that belong to the architect toolkit: The CAFCR model, tools per 5 views (Customer Objectives, Application, Functional, Conceptual and Realization), Qualities, Story telling and Reasoning in multiple dimensions.

Model-based testing allows the creation of test cases from a model of the system under test. Often, such models are difficult to obtain, or even not available. Automata learning helps in inferring the model of a system by observing its behaviour. The model can be employed for many purposes, such as testing other implementations, regression testing, or model checking.

Tasks executing on general purpose multiprocessor platforms exhibit variations in their execution times. As such, there is a need to explicitly consider robustness, i.e., tolerance to these fluctuations. This work aims to quantify the robustness of schedules of directed acyclic graphs (DAGs) on multiprocessors by defining probabilistic robustness metrics and to present a new approach to perform robustness analysis to obtain these metrics.