2021, Scherb, Christopher, Grewe, Dennis, Tschudin, Christian

Today's Internet is heavily used for multimedia streaming from cloud backends, while the Internet of Things (IoT) challenges the traditional data flow, with high data volumes produced at the network edge. Information Centric Networking (ICN) advocates against a host-centric communication model using content identifiers decoupling content from a location, and therefore, promising for distributed edge computing environments. However, the resulting coupling of data to content identifiers in ICNs introduces new challenges regarding dissemination of large data volumes and services and synchronization across multiple consumers. We present Tangle Centric Networking (TCN) - a decentralized data structure for coordinated distribution of data and services for ICN deployments. TCN simplifies the management of data and service changes and updates them accordingly in network nodes using principles of Tangles. Using simulations, first implementations of TCN show improvements in data discovery as well as less synchronization overhead of large volumes of data compared to a state-of-the-art ICN system.

2018, Grewe, Dennis, Marxer, Claudio, Scherb, Christopher, Wagner, Marco, Tschudin, Christian

Recently, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity transitioned from a vision of the future to reality. Applications in such environments vary from local propagation of road conditions to large-scale traffic flow control systems. In this demo, we present a network stack for the data exchange in the automotive IoT, based on the Named Function Networking (NFN) principles. In NFN, the communication model is not restricted to propagation of static data but natively supports computation-offloading to other nodes. We present solutions and report on experiments with real cars on a test course.

2018, Scherb, Christopher, Grewe, Dennis, Marxer, Claudio, Tschudin, Christian

Named Function Networking (NFN) is an extension for Information Centric Networking (ICN) to execute computation inside the Network. Thereby, NFN consists of two contributions: A workflow definition and a resolution strategy. The ICN communication model enables NFN to reuse already computed results by using the network's content store. To resolve a computation, NFN first tries to find a cached result and only if no result was found, the computation is executed - so-called FoX (Find or Execute) resolution strategy. Initially, NFN was optimized for cloud computing and data center computing next to the big data objects. However, new trends like IoT and edge computing describe other requirements to network computations (e.g. dynamic computations of automotive services at the edge). In this paper, we explore how the resolution strategy can be modified to fit to IoT scenarios without changing the workflow definition. Based on two exemplary IoT use cases (home automation and automotive IOT), new resolution strategies will be presented.