Auflistung nach Schlagwort "Edge Computing"
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- TextdokumentThe 5th GI/ACM Workshop 2020 Scope and Draft Programme on Standardization of Secure and Safe Smart Manufacturing Systems with respect to IEC 62443 IACS(INFORMATIK 2020, 2021) deMeer, Jan; Waedt, Karl; Rennoch, Axel; Hof, Hans-JoachimThe 5th GI/ACM Workshop Programme on Standardization of Secure and Safe Production within Industrial Automation and Control Systems (IACS) took place virtually at September 28, 2020 at the Karlsruhe Institute of Technology (KIT) that hosted the 50th GI's yearly assembly (GI Informatik 2020 Jahrestagung): https://informatik2020.de/programm/workshops/
- TextdokumentAgriRegio: Infrastruktur zur Förderung von digitaler Resilienz und Klimaresilienz im ländlichen Raum am Beispiel der Pilotregion Nahe-Donnersberg(INFORMATIK 2022, 2022) Reuter,Christian; Kuntke,Franz; Trapp,Matthias; Wied,Christian; Brill,Gerwin; Müller,Georg; Steinbrink,Enno; Franken,Jonas; Eberz-Eder,Daniel; Schneider,WolfgangDie Digitalisierung schreitet auch in der Landwirtschaft immer weiter voran. Vermehrt werden in landwirtschaftlichen Betrieben sogenannte Smart Farming-Technologien eingesetzt, mit deren Hilfe verschiedenste Arbeitsabläufe automatisiert ablaufen, kontrolliert werden und mit anderen Betrieben ausgetauscht werden können. Durch die verfügbaren Daten und die Vernetzung mit anderen Betrieben, ergeben sich vielfältige neue Möglichkeiten in Bezug auf ressourcenschonendes, wirtschaftlicheres und kollaboratives Arbeiten. Problematiken ergeben sich mit Blick auf die Speicherung dieser sensiblen Betriebsdaten, vor allem, wenn hierfür nur einige wenige Anbieter zur Verfügung stehen. Das Forschungsprojekt „AgriRegio“ soll die digitalisierte Datenerfassung und -nutzung in landwirtschaftlichen Betrieben widerstandsfähiger machen und die sicherheitskritische Infrastruktur schützen. Sieben Projektpartner erproben dazu smarte Sensoren auf Basis standardisierter Open-Source-Technologien in der Landwirtschaft, bei denen die Betriebsdaten dezentral auf lokalen Servern gespeichert werden.
- KonferenzbeitragAI in the Wild: Challenges of Remote Deployments(INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Dede, Jens; Wewetzer, David; Förster, AnnaThe effect of humanity on the earth becomes more and more apparent. Besides the publicly discussed climate change and overpopulation, also the number of conflicts with wildlife increases. The technological progress of the past years helped to understand these challenges better. Monitoring solutions, known to the public as the Internet of Things (IoT), increase the amount of collected data, whereas artificial intelligence (AI) supports analyzing and gathering a deeper understanding. Most projects in the area of wildlife try to achieve a more sustainable usage of natural resources and a better coexistence with our environment. The mAInZaun project focuses on the conflict between wolves and livestock. It aims to introduce these new technologies into grazing management and foster non-lethally coexistence between livestock and predators. Artificial intelligence (AI) analyzes images and videos of the areas surrounding the pasture. The algorithms detect possible attackers or predators, such as wolves, stray dogs, bobcats, etc. In the second step, these animals are scared away using adaptive technologies. These can be sound, ultrasound, scent, light, etc. These systems are usually operated in remote environments, raising challenges like hardware design, power requirements, and maintenance. This paper will discuss these challenges and how we address them in the mAInZaun project.
- ZeitschriftenartikelAnforderungen für Zeitreihendatenbanken in der industriellen Edge(HMD Praxis der Wirtschaftsinformatik: Vol. 56, No. 6, 2019) Petrik, Dimitri; Mormul, Mathias; Reimann, PeterDas industrielle Internet der Dinge (iIoT) integriert Informations- und Kommunikationstechnologien in die industriellen Prozesse und erweitert sie durch Echtzeit-Datenanalyse. Eine bedeutende Menge an Daten, die in der industriellen Fertigung generiert werden, sind sensorbasierte Zeitreihendaten, die in regelmäßigen Abständen generiert werden und zusätzlich zum Sensorwert einen Zeitstempel enthalten. Spezielle Zeitreihen-Datenbanken (TSDB) sind dafür ausgelegt, die Zeitreihendaten effizienter zu speichern. Wenn TSDBs in der Nähe der Maschine (in der industriellen Edge) eingesetzt werden, sind Maschinendaten zur Überwachung zeitkritischer Prozesse aufgrund der niedrigen Latenz schnell verfügbar, was die erforderliche Zeit für die Datenverarbeitung reduziert. Bisherige Untersuchungen zu TSDBs sind bei der Auswahl für den Einsatz in der industriellen Edge nur begrenzt hilfreich. Die meisten verfügbaren Benchmarks von TSDBs sind performanceorientiert und berücksichtigen nicht die Einschränkungen der industriellen Edge. Wir adressieren diese Lücke und identifizieren die funktionalen Kriterien für den Einsatz von TSDBs im maschinennahen Umfeld und bilden somit einen qualitativen Anforderungskatalog. Des Weiteren zeigen wir am Beispiel von InfluxDB, wie dieser Katalog verwendet werden kann, mit dem Ziel die Auswahl einer geeigneten TSDB für Sensordaten in der Edge zu unterstützen. The industrial Internet of Things (iIoT) integrates information and communication technologies in the industrial processes, and extends them through real-time data analytics. A significant amount of data generated in industrial manufacturing is sensor-based time series data, which is generated at regular intervals, and includes a time stamp in addition to the sensor value. Special time series databases (TSDB) are designed to store the time series data more efficiently. If TSDBs are used close to the machine (in the industrial edge), machine data is quickly available for monitoring time-critical processes due to low latency. This helps to reduce the time required for data processing. Previous research on TSDBs is of limited help during the selection of TSDBs for industrial edge. Most available benchmarks of TSDBs are performance-oriented, and do not consider the restrictions of the industrial edge. We address this gap by identifying the functional criteria for the use of TSDBs in the industrial edge, and by building a qualitative requirements catalogue. Furthermore, we exemplarily show how to use this catalogue by applying it to the TSDB to support the selection of a suitable TSDB for recording sensor data in the edge.
- KonferenzbeitragCombining the Concepts of Semantic Data Integration and Edge Computing(INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Farnbauer-Schmidt, Matthias; Lindner, Julian; Kaffenberger, Christopher; Albrecht, JensThe Internet of Things (IoT) is growing rapidly. Therefore, there are more and more vendors, which led to IoT being a heterogeneous collection of different IoT platforms, isolated solutions and several protocols. It has been proposed to use Data Integration to overcome this heterogeneity. In addition, costs are on the raise due to increasing volume of data which increases demands on bandwidth and cloud computing capabilities. Again a solution has already been proposed by reducing the amount of data to forward by processing data at the edge of an IoT-System, e. g. filtering or aggregation. This concept is called Edge Computing. In this article the Semantic Edge Computing Runtime (SECR) is introduced, combining both concepts. The application of Data Integration enables Edge Computing to be performed on a higher level of abstraction. In addition, the developed Driver-approach allows SECR’s Data Integration algorithm to be applied to a wide range of data sources without imposing requirements on them. The Data Integration itself is based on technologies of Semantic Web, applying metadata to raw data giving it context for interpretation. Furthermore, SECR’s REST-API enables applications to alternate Data Integration and Edge Computing at runtime. The tests of SECR’s prototype implementation have shown its suitability for deployment on an edge device and its scalability, being able to handle 128 data sources and Edge Computing Tasks.
- TextdokumentEdge Computing Standardisation and Initiatives(INFORMATIK 2020, 2021) Rennoch, Axel; Willner, AlexanderSince Edge Computing (EC) became more important in industry and research several standardisation groups and initiatives are considering related technologies in their strategies and future roadmaps. The work includes the definition of reference architecture models, access interfaces but also addresses edge node autonomy and security aspects. This contribution introduces some basic concepts and common understanding of EC within selected standardisation groups and industrial initiatives. Additionally, technical viewpoints and topics are discussed that are relevant for various communities.
- KonferenzbeitragHybrid-Cloud-Infrastrukturen – Edge Computing und KI-basierte Anwendungen in der Landwirtschaft für resiliente und effektive Produktions- und Biodiversitätsmaßnahmen(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Daniel Eberz-Eder, Franz KuntkeMobile Erhebung semantisch modellierter Daten und deren Auswertung im Feld durch Hybrid Cloud Computing sind Grundlage des Resilienten Smart Farmings im Projekt GeoBox. Eine skalierbare Architektur und semantische Datenmodellierung ermöglichen das Management betrieblicher Software-Container, die flexible Anpassung an neue Aufgaben und die Realisierung einfach nutzbarer externer Services, vorgestellt am Beispiel eines Resistenz-Beratungs-Chatbots und von Blühstreifen als Biodiversitätsmaßnahme.
- TextdokumentIndustrie 4.0 – Aktuelle Entwicklungen aus Sicht der Informatik(INFORMATIK 2020, 2021) Usländer, ThomasDer Artikel fasst die in der Sitzung SES-06 Industrie 4.0 vorgetragenen Beiträge zusammenfassen und setzt sie in den Kontext der aktuellen Entwicklung rund um die Initiative Industrie 4.0 und deren Vision 2030.
- TextdokumentOperational Security Analysis and Challenge for IoT Solutions(INFORMATIK 2020, 2021) Gao, Yuan; Lou, XinxinThe marketing engagement of Internet of Things (IoT) shows a wide vista together with Industry 4.0 regarding modern manufacturing and services. However, the evolution of technologies and rising regulation concerns regarding security and privacy are bring challenges to IoT solutions. On one side, the security analysis of IoT solutions has to consider the security posture in a much wider scope including both edge and cloud sides even across global geo-locations. On the other side, new regulation requirements demand a full tracking of data access. In addition, authorizations should be evaluated explicitly and can be revoked any time for maximizing data protection. Both challenges can be solved by implementing a novel security model targeting those requirements while zero trust model is a good candidate. Thus in this paper, we compared the most commonly used perimeter security model and the zero trust model under the circumstance for modern IoT solutions. Furthermore, from the regulation perspective, the concepts of zero trust model are analyzed to show its compliance with regulation requirements. For easing the discussion of IoT solutions, a general IoT architecture is proposed and relevant zero trust model implementations are described. Especially, the zero trust model relevant security controls are highlighted as a guidance for the design of IoT solutions. As the conclusion, we propose a general implementation of zero trust model within the context of IoT solution to solve the challenges facing by the industry.
- TextdokumentRequirements and Mechanisms for Smart Home Updates(INFORMATIK 2020, 2021) Zdankin, Peter; Carl, Oskar; Waltereit, Marian; Matkovic, Viktor; Weis, TorbenThe interconnection of sensors and actuators of smart home devices creates dependencies that allow for ubiquitous services. These devices can be subject to transformative changes through software updates that might lead to unintended consequences. Users have no tools to predict the negative consequences caused by updating their smart home. In this paper, we address this problem and propose mechanisms that enable organized update planning in a smart home. We compare self-description standard approaches that allow reasoning about resulting functionality before updates are installed. Updating devices to their latest versions is not necessarily the best way to update smart homes, therefore we discuss multi-objective optimization in the update process. Finally, outsourcing functionality to external providers might reduce the complexity of certain tasks, but can also pose threats if the wrong tasks are offloaded.