Theses

This habilitation thesis (“Sammelhabilitation”) collects and summarises original research by the author, primarily in the area of security for spontaneous interaction. Spontaneous interaction is one of the key aspects of ubiquitous computing, and securing such spontaneous interactions between devices that typically communicate over wireless and therefore invisible channels requires human-verifiable authentication. Sub-topics discussed in this thesis include interaction methods, cryptographic protocols, and sensor data analysis. The thesis consists of two parts: Part I defines the focus of this specific research area, methodically reviews the current state of the field, puts the collected publications into perspective, and summarises the author's contributions. Part II contains reproductions of the twelve publications collected in this thesis.

Pervasive Computing is a new area of research with increasing prominence; it is situated at the intersection between human/computer interaction, embedded and distributed systems and networking technology. Its declared aim is a holistic design of computer systems, which is often described as the disappearance of computer technology into the periphery of daily life. One central aspect of this vision is a partial replacement of explicit, obtrusive interfaces for human/computer interaction that demand exclusive user attention with implicit ones embedded into real-world artifacts that allow intuitive and unobtrusive use. This kind of interaction with computer systems suits human users better, but necessitates an adaption of such systems to the respective context in which they are used. Context is, in this regard, understood as any information about the current situation of a person, place or object that is relevant to the user interaction. Context-based interaction, which is pursued by the design and implementation of context-sensitive systems, is therefore one of the building blocks of Pervasive Computing. Within the last five years, a number of seminal publications on the recognition of current context from a combination of different sensors have been written within this field. This dissertation tackles the next logical step after the recognition of the current context: the prediction of future contexts. The general concept is the prediction of abstract contexts to allow computer systems to proactively prepare for future situations. This kind of high-level context prediction allows an integral consideration of all ascertainable aspects of context, in contrast to the autonomous prediction of individual aspects like the geographical position of the user. It allows to consider patterns and interrelations in the user behavior which are not apparent at the lower levels of raw sensor data. The present thesis analyzes prerequisites for user-centered prediction of context and presents an architecture for autonomous, background context recognition and prediction, building upon established methods for data based prediction like the various instances of Markov models. Especial attention is turned to implicit user interaction to prevent disruptions of users during their normal tasks and to continuous adaption of the developed systems to changed conditions. Another considered aspect is the economical use of resources to allow an integration of context prediction into embedded systems. The developed architecture is being implemented in terms of a flexible software framework and evaluated with recorded real-world data from everyday situations. This examination shows that the prediction of abstract contexts is already possible within certain limits, but that there is still room for future improvements of the prediction quality.

Spiking Neural Networks are considered as a new computation paradigm, representing the next generation of Artificial Neural Networks by offering more flexibility and degrees of freedom for modeling computational elements. Although this type of Neural Networks is rather new and there exists only a vague knowledge about its features, it is clearly more powerful than its predecessor, not only being able to simulate Artificial Neural Networks in real time but also offering new computational elements that were not available previously. Unfortunately, the simulation of Spiking Neural Networks currently involves the use of continuous simulation techniques which do not scale easily to large networks with many neurons. In this diploma thesis, a new model for Spiking Neural Networks is introduced; it allows the use of fast discrete event simulation techniques and possibly offers enormous advantages in terms of simulation flexibility and scalability without restricting the qualitative computational power. As a proof of concept, the new model has been implemented in a prototype simulation framework, written platform-independently in Java. This simulation framework utilizes solely discrete event simulation and has been successfully used to emulate typical Artificial Neural Networks and to simulate a biologically inspired filter model. The results of the conducted example simulations are presented and possible directions for future research are given. Additionally, a few advanced techniques regarding the use of discrete event simulation, which offers some new opportunities, are shortly discussed.