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IEEE/IFIP International Conference on embedded and Ubiquitous Computing, EUC

作者： Stefano Tennina Melanie Bouroche Pedro Braga Ricardo Gomes Mario Alves Farrukh Mirza Vincenzo Ciriello Gabriella Carrozza Pedro Oliveira Vinny Cahill CISTER Research Center Polytechnic Institute of Porto Porto Portugal Distributed Systems Group Department of Computer Science Trinity College Dublin Dublin Ireland Critical Software S. A. Coimbra Portugal SESM scarl Naples Italy

Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective, feasible and usable system architectures that address both functional and non-functional requirements in an integrated fashion. In this paper, we outline the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to use standard commercially-available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. The EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ node test-bed, which is, to the best of our knowledge, the largest single-site WSN test-bed in Europe to date.

关键词： Wireless sensor networks computer architecture Protocols Middleware Monitoring Peer to peer computing Scalability

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Challenges for Software Engineering in Automation

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Journal of Software Engineering and Applications 2014年第5期7卷 440-451页

作者： Birgit Vogel-Heuser Christian Diedrich Alexander Fay Sabine Jeschke Stefan Kowalewski Martin Wollschlaeger Peter Gohner Institute of Automation and Information Systems Technische Universitat MünchenMünchenGermany Institute for Automation and Communication Otto von Guericke University MagdeburgMagdeburgGermany Institute of Automation Technology Helmut-Schmidt-UniversityHamburgGermany Institute of Information Management in Mechanical Engineering RWTH Aachen UniversityAachenGermany Computer Science 11—Embedded Software Laboratory RWTH Aachen UniversityAachenGermany Institute for Applied Computer Science Dresden University of TechnologyDresdenGermany Institute of Industrial Automation and Software Engineering University of StuttgartStuttgartGermany

This paper gives an introduction to the essential challenges of software engineering and requirements that software has to fulfill in the domain of automation. Besides, the functional characteristics, specific constraints and circumstances are considered for deriving requirements concerning usability, the technical process, the automation functions, used platform and the well-established models, which are described in detail. On the other hand, challenges result from the circumstances at different points in the single phases of the life cycle of the automated system. The requirements for life-cycle-management, tools and the changeability during runtime are described in detail.

关键词： Automation Software Engineering Models Tools

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A sustainable and secure load management model for green cloud data centres

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Scientific Reports 2023年第1期13卷 1-22页

作者： Saxena, Deepika Singh, Ashutosh Kumar Lee, Chung-Nan Buyya, Rajkumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra 136119 India Department of Computer Science Goethe University Frankfurt Germany Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung 804201 Taiwan Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne 804201 Australia

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%. © 2023, The Author(s).

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Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

arXiv

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arXiv 2020年

作者： Löffler, Felicitas Wesp, Valentin König-Ries, Birgitta Klan, Friederike Heinz-Nixdorf Chair for Distributed Information Systems Department of Mathematics and Computer Science Friedrich Schiller University Jena Jena Germany Michael-Stifel-Center for Data-Driven and Simulation Science Jena Germany Halle-Jena-Leipzig Germany Citizen Science Group DLR-Institute of Data Science German Aerospace Center Jena Germany

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. We analyze the primary source in dataset search - metadata - and determine if they reflect scholarly search interests. We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled. To determine search interests in biodiversity research, we gathered 169 questions that researchers aimed to answer with the help of retrieved data, identified biological entities and grouped them into 13 categories. The categories were evaluated with nine biodiversity scholars who assigned one of the types to pre-labeled biological entities in the questions. Our findings indicate that environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important search interests in biodiversity research. The comparison with existing metadata standards shows that domain-specific standards cover search interests quite well, whereas general standards do not explicitly contain elements that reflect search interests. We inspect metadata from five large data repositories. Our results confirm that metadata currently poorly reflect search interests in biodiversity research. From these findings, we derive recommendations for researchers and data repositories how to bridge the gap between search interest and metadata provided.

关键词： Semantic Web

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3D printing of hierarchical structures made of inorganic silicon-rich glass featuring self-forming nanogratings

arXiv

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arXiv 2024年

作者： Huang, Po-Han Chen, Shiqian Hartwig, Oliver Marschner, David E. Duesberg, Georg S. Stemme, Göran Li, Jiantong Gylfason, Kristinn B. Niklaus, Frank Division of Micro and Nanosystems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Kista16440 Sweden Institute of Physics EIT 2 Faculty of Electrical Engineering and Information Technology University of the Bundeswehr Munich & SENS Research Center Neubiberg85577 Germany

Hierarchical structures are abundant in nature, such as in the superhydrophobic surfaces of lotus leaves and the structural coloration of butterfly wings. They consist of ordered features across multiple size scales, and their unique properties have attracted enormous interest in wide-ranging fields, including energy storage, nanofluidics, and nanophotonics. Femtosecond lasers, capable of inducing various material modifications, have shown promise for manufacturing tailored hierarchical structures. However, existing methods such as multiphoton lithography and 3D printing using nanoparticle-filled inks typically involve polymers and suffer from high process complexity. Here, we demonstrate 3D printing of hierarchical structures in inorganic silicon-rich glass featuring self-forming nanogratings. This approach takes advantage of our finding that femtosecond laser pulses can induce simultaneous multiphoton crosslinking and self-formation of nanogratings in hydrogen silsesquioxane (HSQ). The 3D printing process combines the 3D patterning capability of multiphoton lithography and the efficient generation of periodic structures by the self-formation of nanogratings. We 3D-printed micro-supercapacitors with large surface areas and a remarkable areal capacitance of 1 mF/cm2 at an ultrahigh scan rate of 50 V/s, thereby demonstrating the utility of our 3D printing approach for device applications in emerging fields such as energy storage. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D printing

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TACLeBench: A benchmark collection to support worst-case execution time research 16

TACLeBench: A benchmark collection to support worst-case exe...

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16th International Workshop on Worst-Case Execution Time Analysis, WCET 2016

作者： Falk, Heiko Altmeyer, Sebastian Hellinckx, Peter Lisper, Björn Puffitsch, Wolfgang Rochange, Christine Schoeberl, Martin Sørensen, Rasmus Bo Wägemann, Peter Wegener, Simon Hamburg University of Technology Institute of Embedded Systems Hamburg Germany University of Amsterdam Amsterdam Netherlands University of Antwerp iMinds Antwerp Belgium Mälardalen University School of Innovation Design and Engineering Västerås Sweden Technical University of Denmark Department of Applied Mathematics and Computer Science Lyngby Denmark University of Toulouse Toulouse France Friedrich-Alexander University Erlangen-Nürnberg Erlangen-Nürnberg Germany AbsInt Angewandte Informatik GmbH Saarbrücken Germany

ISBN: (纸本)9783959770255

Engineering related research, such as research on worst-case execution time, uses experimentation to evaluate ideas. For these experiments we need example programs. Furthermore, to make the research experimentation repeatable those programs shall be made publicly available. We collected open-source programs, adapted them to a common coding style, and provide the collection in open-source. The benchmark collection is called TACLeBench and is available from GitHub in version 1.9 at the publication date of this paper. One of the main features of TACLeBench is that all programs are self-contained without any dependencies on standard libraries or an operating system. © Heiko Falk,Sebastian Altmeyer, Peter Hellinckx, Björn Lisper, Wolfgang Puffitsch, Christine Rochange, Martin Schoeberl, Rasmus Bo Sørensen, Peter Wägemann, and Simon Wegener.

关键词： Real time systems

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Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection

arXiv

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arXiv 2024年

作者： Bucher, David Porawski, Daniel Wimmer, Benedikt Nüßlein, Jonas O’Meara, Corey Mohseni, Naeimeh Cortiana, Giorgio Linnhoff-Popien, Claudia Aqarios GmbH Prinzregentenstraße 120 Munich81677 Germany Ludwig-Maximilians University Munich Institute for Computer Science Mobile and Distributed Systems Chair Germany E.ON Digital Technology GmbH Laatzener Str. 1 Hannover30539 Germany

Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identification of self-reliant grid subsets becomes crucial for operation. This problem can be represented as a modification to the well-known NP-hard Community Detection (CD) problem. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computation, which is expected to find better-quality partitions faster. The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them. To assess quantum optimization for sizeable problems, we apply a hierarchical divisive method that solves sub-problem QUBOs to perform grid bisections. Furthermore, we propose a customization of the Louvain heuristic that includes self-reliance. In the evaluation, we first demonstrate that this problem examines exponential runtime scaling classically. Then, using different IEEE power system test cases, we benchmark the solution quality for multiple approaches: D-Wave’s hybrid quantum-classical solvers, classical heuristics, and a branch-and-bound solver. As a result, we observe that the hybrid solvers provide very promising results, both with and without the divisive algorithm, regarding solution quality achieved within a given time frame. Directly utilizing D-Wave’s Quantum Annealing (QA) hardware shows inferior partitioning. © 2024, CC BY.

关键词： Hierarchical clustering

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Dominik Romanów Piotr Grzesik Paweł Benecki Alexandre Niyomugaba Theodore Habimana Daniel Kostrzewa Krzysztof Tokarz Che-Lun Hung Dariusz Mrozek Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science INES Ruhengeri - Institute of Applied Sciences Rwanda Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.

关键词： Remotely guided vehicles Program processors Filtering Pipelines Predictive models Logic gates Data processing Monitoring Predictive maintenance Smart manufacturing

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Challenges and Opportunities of DevOps in Cyber-Physical Production systems Engineering

Challenges and Opportunities of DevOps in Cyber-Physical Pro...

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Industrial Cyber-Physical systems (ICPS)

作者： István Koren Felix Rinker Kristof Meixner Jasminka Matevska Jörg Walter Chair of Process and Data Science RWTH Aachen University Aachen Germany Christian Doppler Laboratory SQI Institute of Information Systems Engineering TU Wien Vienna Austria School of Electrical Engineering and Computer Science City University of Applied Sciences Bremen Germany Distributed Computing and Communication OFFIS - Institute for Information Technology Oldenburg Germany

DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.

关键词： Production systems Data collection Software systems Hardware Safety Stakeholders Security

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Localization in Wireless Networks via Laser Scanning and Bayesian Compressed Sensing

Localization in Wireless Networks via Laser Scanning and Bay...

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IEEE Workshop on Signal Processing Advances in Wireless Communications

作者： Sofia Nikitaki Philipp M.Scholl Kristof Van Laerhoven Panagiotis Tsakalides Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH-ICS) Embedded Sensing Systems Technische Universitat Darmstadt

ISBN: (纸本)9781467355766

WiFi indoor localization has seen a renaissance with the introduction of RSSI-based approaches. However, manual finger-printing techniques that split the indoor environment into pre-defined grids are implicitly bounding the maximum achievable localization accuracy. WoLF, our proposed Wireless localization and Laser-scanner assisted Fingerprinting system, solves this problem by automating the way indoor fingerprint maps are generated. We furthermore show that WiFi localization on the generated high resolution maps can be performed by sparse reconstruction which exploits the peculiarities imposed by the physical characteristics of indoor environments. Particularly, we propose a Bayesian Compressed Sensing (BCS) approach in order to find the position of the mobile user and dynamically determine the sufficient number of APs required for accurate positioning. BCS employs a Bayesian formalism in order to reconstruct a sparse signal using an undetermined system of equations. Experimental results with data collected in a university building validate WoLF in terms of localization accuracy under actual environmental conditions.

关键词： Localization Networks Laser

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