Mobility-Based Routing Overhead Management in Reconfigurable Wireless Ad Hoc Networks Routing Overheads are the non-data message packets whose roles are establishment and maintenance of routes for data packets as well as neighbourhood discovery and maintenance. They have to be broadcasted in the network either through flooding or other techniques that can ensure that a path exists before data packets can be sent to various destinations. They can be sent reactively or periodically to neighbours so as to keep nodes updated on their neighbourhoods. While we cannot do without these overhead packets, they occupy much of the limited wireless bandwidth available in wireless networks. In a reconfigurable wireless ad hoc network scenario, these packets have more negative effects, as links need to be confirmed more frequently than in traditional networks mainly because of the unpredictable behaviour of the ad hoc networks. We therefore need suitable algorithms that will manage these overheads so as to allow data packet to have more access to the wireless medium, save node energy for longer life of the network, increased efficiency, and scalability. Various protocols have been suggested in the research area. They mostly address routing overheads for suitability of particular protocols leading to lack of standardisation and inapplicability to other protocol classes. In this dissertation ways of ensuring that the routing overheads are kept low are investigated. The issue is addressed both at node and network levels with a common goal of improving efficiency and performance of ad hoc networks without dedicating ourselves to a particular class of routing protocol. At node level, a method hereby referred to as "link availability forecast", that minimises routing overheads used for maintenance of neighbourhood, is derived. The targeted packets are packets that are broadcasted periodically (e.g. hello messages). The basic idea in this method is collection of mobility parameters from the neighbours and predictions or forecasts of these parameters in future. Using these parameters in simple calculations helps in identifying link availabilities between nodes participating in maintenance of networks backbone. At the network level, various approaches have been suggested. The first approach is the cone flooding method that broadcasts route request messages through a predetermined cone shaped region. This region is determined through computation using last known mobility parameters of the destination. Another approach is what is hereby referred as "destination search reverse zone method". In this method, a node will keep routes to destinations for a long time and use these routes for tracing the destination. The destination will then initiate route search in a reverse manner, whereby the source selects the best route for next delivery. A modification to this method is for the source node to determine the zone of route search and define the boundaries within which the packet should be broadcasted. The later method has been used for simulation purposes. The protocol used for verification of the improvements offered by the schemes was the AODV. The link availability forecast scheme was implemented on the AODV and labelled AODV_LA while the network level implementation was labelled AODV_RO. A combination of the two schemes was labelled AODV_LARO.

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Das Rekrutieren der so genannten High Potentials innerhalb des Social Web ist ein heiß diskutiertes Thema unter den Personalverantwortlichen der Unternehmen. Ganze Blogs von Personal- und Marketingexperten, wie bspw. personalmarketing2null.wordpress.com oder saatkorn.wordpress.com, beschäftigen sich fast ausschließlich mit diesem Thema. Allerdings mangelt es an kommunikationswissenschaftlicher Forschung zu Personalmarketing-Aktivitäten deutscher Unternehmen in Sozialen Netzwerken. Zwar liegen verschiedene Befragungen Personalverantwortlicher zu Sinn und Zweck von Personalmarketing in Sozialen Netzwerken vor, zwar gibt es zahlreiche How-to-Anleitungen, Handlungsempfehlungen und vielseitige Stimmen aus den verschiedensten Bereichen zu diesem Thema. Allen entbehrt es aber an einer grundlegenden Analyse von Erwartungen, Möglichkeiten und dem Status Quo von Personalmarketing auf Social Network Sites. Diese Lücke möchte die vorliegende Arbeit zumindest aus kommunikationswissenschaftlicher Perspektive schließen. (...)

In large and complex systems, failures can have dramatic consequences, such as black-outs, pandemics or the loss of entire classes of an ecosystem. Nevertheless, it is a centuries-old intuition that by using networks to capture the core of the complexity of such systems, one might understand in which part of a system a phenomenon originates. I investigate this intuition using spectral methods to decouple the dynamics of complex systems near stationary states into independent dynamical modes. In this description, phenomena are tied to a specific part of a system through localized eigenvectors which have large amplitudes only on a few nodes of the system's network. Studying the occurrence of localized eigenvectors, I find that such localization occurs exactly for a few small network structures, and approximately for the dynamical modes associated with the most prominent failures in complex systems. My findings confirm that understanding the functioning of complex systems generally requires to treat them as complex entities, rather than collections of interwoven small parts. Exceptions to this are only few structures carrying exact localization, whose functioning is tied to the meso-scale, between the size of individual elements and the size of the global network. However, while understanding the functioning of a complex system is hampered by the necessary global analysis, the prominent failures, due to their localization, allow an understanding on a manageable local scale. Intriguingly, food webs might exploit this localization of failures to stabilize by causing the break-off of small problematic parts, whereas typical attempts to optimize technological systems for stability lead to delocalization and large-scale failures. Thus, this thesis provides insights into the interplay of complexity and localization, which is paramount to ascertain the functioning of the ever-growing networks on which we humans depend.

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

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