Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Systems Modeling and Modularity Assessment for Embedded Computer Control Applications

AbstractThe development of embedded computer control systems(ECS) requires a synergetic integration of heterogeneoustechnologies and multiple engineering disciplines. Withincreasing amount of functionalities and expectations for highproduct qualities, short time-to-market, and low cost, thesuccess of complexity control and built-in flexibility turn outto be one of the major competitive edges for many ECS products.For this reason, modeling and modularity assessment constitutetwo critical subjects of ECS engineering.In the development ofECS, model-based design is currently being exploited in most ofthe sub-systems engineering activities. However, the lack ofsupport for formalization and systematization associated withthe overall systems modeling leads to problems incomprehension, cross-domain communication, and integration oftechnologies and engineering activities. In particular, designchanges and exploitation of "components" are often risky due tothe inability to characterize components' properties and theirsystem-wide contexts. Furthermore, the lack of engineeringtheories for modularity assessment in the context of ECS makesit difficult to identify parameters of concern and to performearly system optimization. This thesis aims to provide a more complete basis for theengineering of ECS in the areas of systems modeling andmodularization. It provides solution domain models for embeddedcomputer control systems and the software subsystems. Thesemeta-models describe the key system aspects, design levels,components, component properties and relationships with ECSspecific semantics. By constituting the common basis forabstracting and relating different concerns, these models willalso help to provide better support for obtaining holisticsystem views and for incorporating useful technologies fromother engineering and research communities such as to improvethe process and to perform system optimization. Further, amodeling framework is derived, aiming to provide a perspectiveon the modeling aspect of ECS development and to codifyimportant modeling concepts and patterns. In order to extendthe scope of engineering analysis to cover flexibility relatedattributes and multi-attribute tradeoffs, this thesis alsoprovides a metrics system for quantifying componentdependencies that are inherent in the functional solutions.Such dependencies are considered as the key factors affectingcomplexity control, concurrent engineering, and flexibility.The metrics system targets early system-level design and takesinto account several domain specific features such asreplication and timing accuracy. Keywords:Domain-Specific Architectures, Model-basedSystem Design, Software Modularization and Components, QualityMetrics.QC 2010052

A Metrics System for Quantifying Operational Coupling in Embedded Computer Control Systems

One central issue in system structuring and quality prediction is the interdependencies of system modules. This paper proposes a novel technique for determining the operational coupling in embedded computer control systems. It allows us to quantify dependencies between modules, formed by different kinds of relationships in a solution, and therefore promotes a more systematic approach to the reasoning about modularity. Compared to other existing coupling metrics, which are often implementation-technology specific such as confining to the inheritance and method invocation relationships in OO software, this metrics system considers both communication and synchronization and can be applied throughout system design. The metrics system has two parts. The first part supports a measurement of coupling by considering individual relationship types separately. The quantification is performed by considering the topology of connections, as well as the multiplicity, replication, frequency, and accuracy of component properties that appear in a relationship. The second part provides a methodology for combining coupling by individual relationship types into an overall coupling, where domain specific heuristics and technology constraints are used to determine the weighting

Combining Self-Organizing Map with Reinforcement Learning for Multivariate Time Series Anomaly Detection

Anomaly detection plays a critical role in condition monitors to support the trustworthiness of Cyber-Physical Systems (CPS). Detecting multivariate anomalous data in such systems is challenging due to the lack of a complete comprehension of anomalous behaviors and features. This paper proposes a framework to address time series multivariate anomaly detection problems by combining the Self-Organizing Map (SOM) with Deep Reinforcement Learning (DRL). By clustering the multivariate data, SOM creates an environment to enable the DRL agents interacting with the collected system  operational data in terms of a tabular dataset. In this environment, Markov chains reveal the likely anomalous features to support the DRL agent exploring and exploiting the state-action space to maximize anomaly detection performance. We use a time series dataset, Skoltech Anomaly Benchmark (SKAB), to evaluate our framework. Compared with the best results by some currently applied methods, our framework improves the F1 score by 9%, from 0.67 to 0.73. QC 20231220</p

A reference architecture for cooperative driving

Cooperative driving systems enable vehicles to adapt their motion to the surrounding traffic situation by utilizing information communicated by other vehicles and infrastructure in the vicinity. How should these systems be designed and integrated into the modern automobile? What are the needed functions, key architectural elements and their relationships? We created a reference architecture that systematically answers these questions and validated it in real world usage scenarios. Key findings concern required services and enabling them via the architecture. We present the reference architecture and discuss how it can influence the design and implementation of such features in automotive systems.Updated from "Submitted" to "Published". QC 20140120DFEA202