The rapid adoption of the internet has occurred despite the lack of a ubiquitous identity meta-system. The status quo is a patchwork of proprietary security systems. A number of security issues have arisen as a result which threaten to lead to a loss of trust in the internet, and may limit the scope of applications built on it; effectively constraining the potential of the internet as a platform for business and services. Current initiatives by a broad consortium of industry leaders promise a vastly improved landscape with a set of interoperable protocols and systems, built on open specifications, and guided by a set of core identity principles, enabling a more secure online experience. Simultaneously there have arisen a large number of location aware web application and services which detect and use a user’s location to enhance their application experience. These advances, although useful, present new security and privacy issues. This paper investigates the operation of one of the new identity technologies, information cards, and proposes extensions to the existing supported schemas to incorporate recent advances in geo-location technology. The proposal is supported by reference to existing o pen source implementations.

The formal definition of network coding by Ahlswede et al. in 2000 has led to several breakthroughs in information theory, for example solving the bottleneck problem in butterfly networks and breaking the min-cut max-flow theorem for multicast communication. Especially promising is the usage of network coding as a packet-level Forward Error Correction (FEC) scheme to increase the robustness of a data stream against packet loss, also known as intra-session coding. Yet, despite these benefits, network coding-based FEC is still rarely deployed in real-world networks. To bridge this gap between information theory and real-world usage, this cumulative thesis will present our contributions to the integration, evaluation, and optimization of network coding-based FEC. The first set of contributions introduces and evaluates efficient ways to integrate coding into UDP-based IoT protocols to speed up bulk data transfers in lossy scenarios. This includes a packet-level FEC extension for the Constrained Application Protocol (CoAP) [P1] and one for MQTT for Sensor Networks (MQTT-SN), which levels the underlying publish-subscribe architecture [P2]. The second set of contributions addresses the development of novel evaluation tools and methods to better quantify possible coding gains. This includes link ’em, our award-winning link emulation bridge for reproducible networking research [P3], and also SPQER, a word recognition-based metric to evaluate the impact of packet loss on the Quality of Experience of Voice over IP applications [P5]. Finally, we highlight the impact of padding overhead for applications with heterogeneous packet lengths [P6] and introduce a novel packet-preserving coding scheme to significantly reduce this problem [P4]. Because many of the shown contributions can be applied to other areas of network coding research as well, this thesis does not only make meaningful contributions to specific network coding challenges, but also paves the way for future work to further close the gap between information theory and real-world usage.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.