Die produzierende Landwirtschaft kann mit Hilfe geeigneter digitaler Werkzeuge und deren Einsatz in den Prozessabläufen den steigenden Anforderungen an eine moderne und zukunftsfähige Landbewirtschaftung erfolgreich begegnen. Eine dafür wesentliche Voraussetzung, die der drahtlosen Kommunikation, steht aktuell jedoch weder flächendeckend und durchgängig noch zuverlässig zur Verfügung. Das Experimentierfeld LANDNETZ adressiert diese Herausforderung und entwickelt und erprobt Strategien zur völligen Vernetzung sowie zum adäquaten Datenaustausch als grundlegende Bedingung für eine Landwirtschaft 4.0. In diesem Whitepaper beleuchten wir aktuelle in der Landwirtschaft genutzte Vernetzungslösungen und stellen mit Mobilfunk-Campusnetzen einen alternativen Lösungsansatz für digitale Anwendungen in der Landwirtschaft vor. Die im Zuge digitaler Prozesse erhobenen Daten bedürfen sowohl einer sicheren Verarbeitung als auch eines geschützten Austauschs. Unter diesem Aspekt erörtern wir die Frage nach Datensouveränität und zeigen aktuelle Trends und Möglichkeiten für den sicheren Austausch von Daten auf. Im Experimentierfeld LANDNETZ werden im Zusammenspiel mit zahlreichen Kooperationspartnern digitale Anwendungsfälle in der Tierhaltung, des Obst und Weinbaus sowie des Pflanzenbaus konzipiert, erprobt und optimiert. Im Beitrag betrachten wir exemplarisch drei Anwendungsfälle im Hinblick auf die jeweils erforderliche Kommunikationsinfrastruktur und den Datenaustausch näher.

Header compression is one of the technologies, which enables packet-switched computer networks to operate with higher efficiency even if the underlying physical link is limited. Since its inception, the compression was meant to improve dial-up Telnet connections, and has evolved into a complex multi-faceted compression library, which has been integrated into the third and fourth generation of cellular networks, among others. Beyond the promised benefit of decreased bandwidth usage, header compression has shown that it is capable of improving the quality of already existing services, such as real-time audio calls, and is a developing hot topic to this day, realising, for example, Internet Protocol (IP) version 6 support on resource constrained low-power devices. However, header compression is ill equipped to handle the stringent requirements and challenges, which are posed by the coming fifth generation of wireless and cellular networks (5G) and its applications. Even though it can be considered as an already well developed area of computer networks that can compress protocol headers with unparalleled efficiency, header compression is still operating under some assumptions and restrictions that could deny its employment outside of cellular Voice over IP transmissions to certain degrees. Albeit some improvements in the latency domain could be achieved with its help, the application of header compression in both largely interconnected networks and very dynamic ones – such as the wireless mesh and vehicular networks – is not yet assured, as the topic, in this perspective, is still relatively new and unexplored. The main goal of my theses is the presentation and evaluation of novel ideas, which support the application of header compression concepts for the future wireless use-cases, as it holds alluring benefits for the coming network generations, if applied correctly. The dissertation provides a detailed treatment of my contribution in the specific research areas of header compression and network coding, which encompass novel proposals for their enhancement in 5G uses, such as broadcastability and online optimisation, as well as their subsequent analysis from various perspectives, including the achievable compression gains, delay reduction, transmission efficiency, and energy consumption, to name a few. Besides the focus on enabling header compression in 5G, the development of traffic-agnostic and various network-coded compression concepts are also introduced to attain the benefits of both techniques at the same time, namely, reduced bandwidth usage and high reliability in latency sensitive heterogeneous and error prone mesh networks. The generalisation of compression is achieved by the employment of various machine learning concepts, which could approximate the compression characteristics of any packet-based communication flow, while network coding facilitates the exploitation of the low-latency benefits of error correcting codes in heavily interconnected wireless networks.