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Institute
Flooded Edge Gateways
(2019)
Increasing numbers of internet-compatible devices, in particular in the context of IoT, usually cause increasing amounts of data. The processing and analysis of a continuously growing amount of data in real-time by means of cloud platforms cannot be guaranteed anymore. Approaches of Edge Computing decentralize parts of the data analysis logics towards the data sources in order to control the data transfer rate to the cloud through pre-processing with predefined quality-of-service parameters. In this paper, we present a solution for preventing overloaded gateways by optimizing the transfer of IoT data through a combination of Complex Event Processing and Machine Learning. The presented solution is completely based on open-source technologies and can therefore also be used in smaller companies.
Dynamic Real-Time Range Queries (DRRQ) are a common means to handle mobile clients in high-density areas where both, clients requested by the query and the inquirers, are mobile. In contrast to the very well-known continuous range queries, only a few approaches, such as Adaptive Quad Streaming (AQS), address the mandatory scalability and real-time requirements of these so-called ad-hoc mobility challenges. In this paper we present the highly decentralized solution Adaptive Quad Streaming Flexible (AQSflex) as an extension of the already existing more theoretical AQS approach. Beside a highly distributed cell structure without data structures and a lightweight streaming communication, we use a multi-cell-assignment on limited pool resources instead of an idealistic unlimited cell-per-server assignment. The described experimental results show the potential of our local capacity balancing scheme for cell handover in a strongly decentralized setting. Leafs of a cell hierarchy define a kind of self-optimizing fuzzy edge for the processing resources in high-density systems without any centralized controlling or cloud component.
Die vorausschauende Instandhaltung (engl. Predictive Maintenance) gewinnt für die produzierende Industrie weltweit an Bedeutung, da Produktionsmodernisierungen im Rahmen der Industrie 4.0 sowie die zunehmende Verwendung von heterogenen Sensoreinheiten die Instandhaltungsplanung immer komplexer gestalten. Darüber hinaus ist das Service-Kontingent, welches ein Maschinenbauer seinen Kunden im Bereich der Instandhaltung anbieten kann, durch die Ressource Mensch stark limitiert und nur ortsgebunden einsetzbar. Durch herkömmliche Instandhaltungsprozesse entstehen somit oft hohe Kosten, sowohl für den Maschinenbauer als auch für den Anwender. Dieser Beitrag gibt einen Einblick in aktuelle Forschungen der Sybit GmbH in direkter Zusammenarbeit mit der HTWG Konstanz und renommierten Maschinenbau-Unternehmen. Gemeinsames Ziel ist es, vorhandene Instandhaltungsprozesse durch die Verwendung von Augmented Reality (AR) und weiterführenden Technologien zu unterstützen. Hierbei wird ein Stufenplan erarbeitet und vorgestellt, in dem die notwendigen Erweiterungen auf dem Weg von der Implementierung eines Pilotprojekts bis hin zur vollwertigen Industrie-4.0-Anwendung diskutiert werden. Abschließend wird die plausible Erweiterbarkeit der vorgestellten Entwicklungen erörtert und die Übertragbarkeit der Ergebnisse auf andere Domänen vorgestellt.
Location-aware mobile devices are becoming increasingly popular and GPS sensors are built into nearly every portable unit with computational capabilities. At the same time, the emergence of location-aware virtual services and ideas calls for new efficient spatial real-time queries. Communication latency in mobile environments interacting with high decentralization and the need of scalability in high-density systems with immense client counts leads to major challenges. In this paper we describe a decentralized architecture for continuous range queries in settings in which both, the requested and the requesting clients, are mobile. While prior works commonly use a request-response approach we provide a stream-based adaptive grid solution dealing with arbitrary high client counts and improving communication latency that meets given hard real-time constraints.
Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are mostly static whereas the mobile clients in the ranges move. We focus on a category called Dynamic Real-Time Range Queries (DRRQ) assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters results continuously change. This leads to two requirements: the ability to deal with an arbitrary high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper we present the highly decentralized solution Adaptive Quad Streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimizes data structures on servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.
Dynamic Real-Time Range Queries (DRRQ) are widely used to discover local context between mobile clients in high-density areas where both clients requested by the query and inquirers are mobile. Unlike the very well-known continuous range queries only a few approaches such as Adaptive Quad Streaming (AQS) address scalability and real-time requirements that are important for ad-hoc mobility challenges in fully distributed client networks. In this paper we address fundamental limitations of AQS presenting the new lightweight AQSdynamic which supports arbitrary, individual, and time-dependent range query shapes instead of fixed circular ones, thus covering much more realistic scenarios. More important, AQSdynamic combines the decentralized streaming approach of AQS with "subscriptions"guaranteeing a real-time behavior with constant complexity with to the number of clients.
As part of large-scale project in the Lake Constance region that borders Germany, Austria and Switzerland, seamless learning prototypes are designed, implemented, and evaluated. A design-based research (DBR) approach is used for the development, implementation and evaluation. The project consists of one base project that brings together instructional design and instructional technology specialists as well as (educational) software architects, supporting seven subprojects in developing seamless-learning lighthouse implementations mostly within higher and further education.
Industrial partners are involved in all subprojects. The authors are unaware of similar seamless learning projects using a DBR approach that include evaluation and redesign and allow for comparison of experiences by applying DBR across subprojects. The project aims to generate novel seamless learning implementations, respective novel research and novel software supporting different aspects of seamless learning. The poster will present the project design, delineate areas of research and development, and include initial empirical results.
Successful project management (PM), as one of the most important key competences in the western-oriented working world, is mainly influenced by experience and social skills. As a direct impact on PM training, the degree of practice and reality is crucial for the application of lessons learned in a challenging everyday work life. This work presents a recursive approach that adapts well-known principles of PM itself for PM training. Over three years, we have developed a concept and an integrated software system that support our PM university courses. Stepwise, it transfers theoretical PM knowledge into realistic project phases by automatically adjusting to the individual learning progress. Our study reveals predictors such as degrees of collaboration or weekend work as vital aspects in the PM training progress. The chosen granularity of project phases with variances in different dimensions makes our model a canonical incarnation of seamless learning.
Seamless-Learning-Plattform
(2020)
