18 research outputs found
Black-box Integration of Heterogeneous Modeling Languages for Cyber-Physical Systems
Robots belong to a class of Cyber-Physical Systems where complex software as
a mobile device has to full tasks in a complex environment. Modeling robotics
applications for analysis and code generation requires modeling languages for
the logical software architecture and the system behavior. The
MontiArcAutomaton modeling framework integrates six independently developed
modeling languages to model robotics applications: a component & connector
architecture description language, automata, I/O tables, class diagrams, OCL,
and a Java DSL. We describe how we integrated these languages into
MontiArcAutomaton a-posteriori in a black-box integration fashion.Comment: 6 pages, 4 figures. GEMOC Workshop 2013 - International Workshop on
The Globalization of Modeling Languages, Miami, Florida (USA), Volume 1102 of
CEUR Workshop Proceedings, CEUR-WS.org, 201
Code Generator Composition for Model-Driven Engineering of Robotics Component & Connector Systems
Engineering software for robotics applications requires multidomain and
application-specific solutions. Model-driven engineering and modeling language
integration provide means for developing specialized, yet reusable models of
robotics software architectures. Code generators transform these platform
independent models into executable code specific to robotic platforms.
Generative software engineering for multidomain applications requires not only
the integration of modeling languages but also the integration of validation
mechanisms and code generators. In this paper we sketch a conceptual model for
code generator composition and show an instantiation of this model in the
MontiArc- Automaton framework. MontiArcAutomaton allows modeling software
architectures as component and connector models with different component
behavior modeling languages. Effective means for code generator integration are
a necessity for the post hoc integration of applicationspecific languages in
model-based robotics software engineering.Comment: 12 pages, 4 figures, In: Proceedings of the 1st International
Workshop on Model-Driven Robot Software Engineering (MORSE 2014), York, Great
Britain, Volume 1319 of CEUR Workshop Proceedings, 201
Multi-Platform Generative Development of Component & Connector Systems using Model and Code Libraries
Component-based software engineering aims to reduce software development
effort by reusing established components as building blocks of complex systems.
Defining components in general-purpose programming languages restricts their
reuse to platforms supporting these languages and complicates component
composition with implementation details. The vision of model-driven engineering
is to reduce the gap between developer intention and implementation details by
lifting abstract models to primary development artifacts and systematically
transforming these into executable systems. For sufficiently complex systems
the transformation from abstract models to platform-specific implementations
requires augmentation with platform-specific components. We propose a
model-driven mechanism to transform platform-independent logical component &
connector architectures into platform-specific implementations combining model
and code libraries. This mechanism allows to postpone commitment to a specific
platform and thus increases reuse of software architectures and components.Comment: 10 pages, 4 figures, 1 listin
Synthesizing a Lego Forklift Controller in GR(1): A Case Study
Reactive synthesis is an automated procedure to obtain a
correct-by-construction reactive system from a given specification. GR(1) is a
well-known fragment of linear temporal logic (LTL) where synthesis is possible
using a polynomial symbolic algorithm. We conducted a case study to learn about
the challenges that software engineers may face when using GR(1) synthesis for
the development of a reactive robotic system. In the case study we developed
two variants of a forklift controller, deployed on a Lego robot. The case study
employs LTL specification patterns as an extension of the GR(1) specification
language, an examination of two specification variants for execution
scheduling, traceability from the synthesized controller to constraints in the
specification, and generated counter strategies to support understanding
reasons for unrealizability. We present the specifications we developed, our
observations, and challenges faced during the case study.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies
This paper proposes a methodology for designing decision support systems for
visualising and mitigating the Internet of Things cyber risks. Digital
technologies present new cyber risk in the supply chain which are often not
visible to companies participating in the supply chains. This study
investigates how the Internet of Things cyber risks can be visualised and
mitigated in the process of designing business and supply chain strategies. The
emerging DSS methodology present new findings on how digital technologies
affect business and supply chain systems. Through epistemological analysis, the
article derives with a decision support system for visualising supply chain
cyber risk from Internet of Things digital technologies. Such methods do not
exist at present and this represents the first attempt to devise a decision
support system that would enable practitioners to develop a step by step
process for visualising, assessing and mitigating the emerging cyber risk from
IoT technologies on shared infrastructure in legacy supply chain systems
The ALI Architecture Description Language
Architecture Description Languages (ADLs) have emerged over the past two decades as a means to abstract details of large-scale systems in order to enable better intellectual control over the complete systems. Recently, there has been an explosion in the number of ADLs created in the research community. However, industrial adoption of these ADLs has been rather limited. This has been attributed to various reasons, including the lack of support of some ADLs for: variability management, requirements traceability, architectural artefact reusability and multiple architectural views. To overcome these limitations, this paper is a report on ALI, an ADL that was designed to complement existing work by adding mechanisms to address the aforementioned limitations. The ALI design principles, concepts, notations and formal semantics are presented in this paper. The notation is illustrated using two distinct case studies, one from the information systems domain " an Asset Management System (AMS); and another from the embedded systems domain - a Wheel Brake System (WBS)
Cyber risk at the edge: Current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks