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Das Verbundprojekt „Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken“ (PIMKoWe – Förderkennzeichen „02P17D160“) ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen für die Produktion, Dienstleistung und Arbeit von morgen“ der Bekanntmachung „Industrie 4.0 – Intelligente Kollaborationen in dynamischen Wertschöpfungs-netzwerken“ (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) gefördert und durch den Projektträger des Karlsruher Instituts für Technologie (PTKA) betreut. Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattformlösung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertschöpfungsnetzwerken des industriellen Sektors.

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view's complementarity be the contribution of the view to the global view, chosen in this paper to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. The data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance.

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However, despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL.

Routing is one of the most important issues in any communication network. It defines on which path packets are transmitted from the source of a connection to the destination. It allows to control the distribution of flows between different locations in the network and thereby is a means to influence the load distribution or to reach certain constraints imposed by particular applications. As failures in communication networks appear regularly and cannot be completely avoided, routing is required to be resilient against such outages, i.e., routing still has to be able to forward packets on backup paths even if primary paths are not working any more. Throughout the years, various routing technologies have been introduced that are very different in their control structure, in their way of working, and in their ability to handle certain failure cases. Each of the different routing approaches opens up their own specific questions regarding configuration, optimization, and inclusion of resilience issues. This monograph investigates, with the example of three particular routing technologies, some concrete issues regarding the analysis and optimization of resilience. It thereby contributes to a better general, technology-independent understanding of these approaches and of their diverse potential for the use in future network architectures. The first considered routing type, is decentralized intra-domain routing based on administrative IP link costs and the shortest path principle. Typical examples are common today's intra-domain routing protocols OSPF and IS-IS. This type of routing includes automatic restoration abilities in case of failures what makes it in general very robust even in the case of severe network outages including several failed components. Furthermore, special IP-Fast Reroute mechanisms allow for a faster reaction on outages. For routing based on link costs, traffic engineering, e.g. the optimization of the maximum relative link load in the network, can be done indirectly by changing the administrative link costs to adequate values. The second considered routing type, MPLS-based routing, is based on the a priori configuration of primary and backup paths, so-called Label Switched Paths. The routing layout of MPLS paths offers more freedom compared to IP-based routing as it is not restricted by any shortest path constraints but any paths can be setup. However, this in general involves a higher configuration effort. Finally, in the third considered routing type, typically centralized routing using a Software Defined Networking (SDN) architecture, simple switches only forward packets according to routing decisions made by centralized controller units. SDN-based routing layouts offer the same freedom as for explicit paths configured using MPLS. In case of a failure, new rules can be setup by the controllers to continue the routing in the reduced topology. However, new resilience issues arise caused by the centralized architecture. If controllers are not reachable anymore, the forwarding rules in the single nodes cannot be adapted anymore. This might render a rerouting in case of connection problems in severe failure scenarios infeasible., Routing stellt eine der zentralen Aufgaben in Kommunikationsnetzen dar. Es bestimmt darüber, auf welchem Weg Verkehr von der Quelle zum Ziel transportiert wird. Durch geschicktes Routing kann dadurch eine Verteilung der Verkehrsflüsse zum Beispiel zur Lastverteilung erreicht werden. Da Fehler in Kommunikationsnetzen nicht vollständig verhindert werden können, muss Routing insbesondere ausfallsicher sein, d.h., im Falle von Fehlern im Netz muss das Routing weiterhin in der Lage sein, Pakete auf alternativen Pfaden zum Ziel zu transportieren. Es existieren verschiedene gängige Routingverfahren und Technologien, die sich hinsichtlich Ihrer Arbeitsweise, Ihrer Kontrollstrukturen und Ihrer Funktionalität in bestimmten Fehlerszenarien unterscheiden. Für diese verschiedenen Ansätze ergeben sich jeweils eigene Fragestellungen hinsichtlich der Konfiguration, der Optimierung und der Berücksichtigung von Ausfallsicherheit. Diese Doktorarbeit behandelt am Beispiel bestimmter Technologien einige konkrete Fragestellungen zur Analyse und Optimierung der Ausfallsicherheit. Sie liefert damit einen Beitrag zum besseren generellen Verständnis verschiedenartiger Routingansätze und deren unterschiedlichen Potentials für den Einsatz in zukünftigen Netzarchitekturen. Zuerst wird dezentrales Routing behandelt, basierend auf administrativen Linkgewichten und dem Prinzip der kürzesten Pfade, wie es beispielsweise in den Protokollen IS-IS und OSPF genutzt wird. Diese Routingverfahren beinhalteten automatische Rekonvergenz-Mechanismen um im Falle von Fehlern auf der verbleibenden Netzstruktur weiterhin einen Transport von Verkehr zu ermöglichen. Spezielle IP-Fast Reroute Mechanismen ermöglichen zudem eine schnelle Reaktion im Falle von Fehlern. Routing basierend auf Linkgewichten lässt sich nur indirekt durch die Wahl geeigneter Gewichte beeinflussen und optimieren. Die zweite in der Doktorarbeit behandelte Routingart ist MPLS-basiertes Routing, bei dem Labels für Pakete verwendet werden und Pakete anhand sogenannter vorkonfigurierter Label Switched Paths weitergeleitet werden. Diese Technologie bietet mehr Freiheiten bei der Wahl des Pfadlayouts, was aber wiederum im Allgemeinen einen erhöhten Konfigurationsaufwand mit sich bringt. Schließlich greift die Doktorarbeit auch das Routing in SDN Netzen auf. Dort erfolgt eine Trennung von Control Plane und Data Plane, so dass einzelne dedizierte Controller die Routingentscheidungen festlegen und ansonsten einfache Switches mit reduzierter Komplexität den Verkehr lediglich entsprechend der festgelegten Regeln weiterleiten. Dies ermöglicht die größte Freiheit bei der Konfiguration des Routing bringt aber wiederum neue Fragestellungen bedingt durch die zentralen Kontrolleinheiten mit sich.