11,925 research outputs found
Federated Embedded Systems – a review of the literature in related fields
This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous
computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways
BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems
In this report, we present work towards a framework for modeling and checking
behavior of spatially distributed component systems. Design goals of our
framework are the ability to model spatial behavior in a component oriented,
simple and intuitive way, the possibility to automatically analyse and verify
systems and integration possibilities with other modeling and verification
tools. We present examples and the verification steps necessary to prove
properties such as range coverage or the absence of collisions between
components and technical details
Conceptualizing a framework for cyber-physical systems of systems development and deployment
ABSTRACT
Cyber-physical systems (CPS) refer to the next generation of
embedded ICT systems that are interconnected, collaborative and that provide users and businesses with a wide range of smart applications and services. Software in CPS applications ranges from small systems to large systems, aka. Systems of Systems (SoS), such as smart grids and cities. CPSoS require managing massive amounts of data, being aware of their emerging behavior, and scaling out to progressively evolve and add new systems. Cloud
computing supports processing and storing massive amounts of
data, hosting and delivering services, and configuring selfprovisioned resources. Therefore, cloud computing is the natural candidate to solve CPSoS needs. However, the diversity of platforms and the low-level cloud programming models make difficult to find a common solution for the development and deployment of CPSoS. This paper presents the architectural foundations of a cloud-centric framework for automating the development and deployment of CPSoS service applications to converge towards a common open service platform for CPSoS applications. This framework relies on the well-known qualities of the microservices architecture style, the autonomic computing paradigm, and the model-driven software development approach. Its implementation and validation is on-going at two European and national projects
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
Responsibility and non-repudiation in resource-constrained Internet of Things scenarios
The proliferation and popularity of smart
autonomous systems necessitates the development
of methods and models for ensuring the effective
identification of their owners and controllers. The aim
of this paper is to critically discuss the responsibility of
Things and their impact on human affairs. This starts
with an in-depth analysis of IoT Characteristics such
as Autonomy, Ubiquity and Pervasiveness. We argue
that Things governed by a controller should have an
identifiable relationship between the two parties and
that authentication and non-repudiation are essential
characteristics in all IoT scenarios which require
trustworthy communications. However, resources can
be a problem, for instance, many Things are designed
to perform in low-powered hardware. Hence, we also
propose a protocol to demonstrate how we can achieve the
authenticity of participating Things in a connectionless
and resource-constrained environment
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
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