Cooperative VET in Training Networks: Analysing the Free-Rider Problem in a Sociology-of-Conventions Perspective

In training networks, particularly small and medium-sized enterprises pool their resources to train apprentices within the framework of the dual VET system, while an intermediary organisation is tasked with managing operations. Over the course of their apprenticeship, the apprentices switch from one training company to another on a (half-) yearly basis. Drawing on a case study of four training networks in Switzerland and the theoretical framework of the sociology of conventions, this paper aims to understand the reasons for the slow dissemination and reluctant adoption of this promising form of organising VET in Switzerland. The results of the study show that the system of moving from one company to another creates a variety of free-rider constellations in the distribution of the collectively generated corporative benefits. This explains why companies are reluctant to participate in this model. For the network to be sustainable, the intermediary organisation has to address discontent arising from free-rider problems while taking into account that the solutions found are always tentative and will often result in new free-rider problems.

WEAVE: Efficient Geographical Routing in Large-Scale Networks

We propose WEAVE, a geographical 2D/3D routing protocol that maintains information on a small number of waypoints and checkpoints for forwarding packets to any destination. Nodes obtain the routing information from partial traces gathered in incoming packets and use a system of checkpoints along with the segments of routes to weave end-to-end paths close to the shortest ones. WEAVE does not generate any control traffic, it is suitable for routing in both 2D and 3D networks, and does not require any strong assumption on the underlying network graph such as the Unit Disk or a Planar Graph. WEAVE compares favorably with existing protocols in both testbed experiments and simulations.

Information-centric communication in mobile and wireless networks

Information-centric networking (ICN) is a new communication paradigm that has been proposed to cope with drawbacks of host-based communication protocols, namely scalability and security. In this thesis, we base our work on Named Data Networking (NDN), which is a popular ICN architecture, and investigate NDN in the context of wireless and mobile ad hoc networks. In a first part, we focus on NDN efficiency (and potential improvements) in wireless environments by investigating NDN in wireless one-hop communication, i.e., without any routing protocols. A basic requirement to initiate informationcentric communication is the knowledge of existing and available content names. Therefore, we develop three opportunistic content discovery algorithms and evaluate them in diverse scenarios for different node densities and content distributions. After content names are known, requesters can retrieve content opportunistically from any neighbor node that provides the content. However, in case of short contact times to content sources, content retrieval may be disrupted. Therefore, we develop a requester application that keeps meta information of disrupted content retrievals and enables resume operations when a new content source has been found. Besides message efficiency, we also evaluate power consumption of information-centric broadcast and unicast communication. Based on our findings, we develop two mechanisms to increase efficiency of information-centric wireless one-hop communication. The first approach called Dynamic Unicast (DU) avoids broadcast communication whenever possible since broadcast transmissions result in more duplicate Data transmissions, lower data rates and higher energy consumption on mobile nodes, which are not interested in overheard Data, compared to unicast communication. Hence, DU uses broadcast communication only until a content source has been found and then retrieves content directly via unicast from the same source. The second approach called RC-NDN targets efficiency of wireless broadcast communication by reducing the number of duplicate Data transmissions. In particular, RC-NDN is a Data encoding scheme for content sources that increases diversity in wireless broadcast transmissions such that multiple concurrent requesters can profit from each others’ (overheard) message transmissions. If requesters and content sources are not in one-hop distance to each other, requests need to be forwarded via multi-hop routing. Therefore, in a second part of this thesis, we investigate information-centric wireless multi-hop communication. First, we consider multi-hop broadcast communication in the context of rather static community networks. We introduce the concept of preferred forwarders, which relay Interest messages slightly faster than non-preferred forwarders to reduce redundant duplicate message transmissions. While this approach works well in static networks, the performance may degrade in mobile networks if preferred forwarders may regularly move away. Thus, to enable routing in mobile ad hoc networks, we extend DU for multi-hop communication. Compared to one-hop communication, multi-hop DU requires efficient path update mechanisms (since multi-hop paths may expire quickly) and new forwarding strategies to maintain NDN benefits (request aggregation and caching) such that only a few messages need to be transmitted over the entire end-to-end path even in case of multiple concurrent requesters. To perform quick retransmission in case of collisions or other transmission errors, we implement and evaluate retransmission timers from related work and compare them to CCNTimer, which is a new algorithm that enables shorter content retrieval times in information-centric wireless multi-hop communication. Yet, in case of intermittent connectivity between requesters and content sources, multi-hop routing protocols may not work because they require continuous end-to-end paths. Therefore, we present agent-based content retrieval (ACR) for delay-tolerant networks. In ACR, requester nodes can delegate content retrieval to mobile agent nodes, which move closer to content sources, can retrieve content and return it to requesters. Thus, ACR exploits the mobility of agent nodes to retrieve content from remote locations. To enable delay-tolerant communication via agents, retrieved content needs to be stored persistently such that requesters can verify its authenticity via original publisher signatures. To achieve this, we develop a persistent caching concept that maintains received popular content in repositories and deletes unpopular content if free space is required. Since our persistent caching concept can complement regular short-term caching in the content store, it can also be used for network caching to store popular delay-tolerant content at edge routers (to reduce network traffic and improve network performance) while real-time traffic can still be maintained and served from the content store.

MaxMAC: a Maximally Traffic-Adaptive MAC Protocol for Wireless Sensor Networks

Energy efficiency is a major concern in the design of Wireless Sensor Networks (WSNs) and their communication protocols. As the radio transceiver typically accounts for a major portion of a WSN node’s power consumption, researchers have proposed Energy-Efficient Medium Access (E2-MAC) protocols that switch the radio transceiver off for a major part of the time. Such protocols typically trade off energy-efficiency versus classical quality of service parameters (throughput, latency, reliability). Today’s E2-MAC protocols are able to deliver little amounts of data with a low energy footprint, but introduce severe restrictions with respect to throughput and latency. Regrettably, they yet fail to adapt to varying traffic load at run-time. This paper presents MaxMAC, an E2-MAC protocol that targets at achieving maximal adaptivity with respect to throughput and latency. By adaptively tuning essential parameters at run-time, the protocol reaches the throughput and latency of energy-unconstrained CSMA in high-traffic phases, while still exhibiting a high energy-efficiency in periods of sparse traffic. The paper compares the protocol against a selection of today’s E2-MAC protocols and evaluates its advantages and drawbacks.

An Evaluation of Compression Schemes for Wireless Networks

Data gathering, either for event recognition or for monitoring applications is the primary intention for sensor network deployments. In many cases, data is acquired periodically and autonomously, and simply logged onto secondary storage (e.g. flash memory) either for delayed offline analysis or for on demand burst transfer. Moreover, operational data such as connectivity information, node and network state is typically kept as well. Naturally, measurement and/or connectivity logging comes at a cost. Space for doing so is limited. Finding a good representative model for the data and providing clever coding of information, thus data compression, may be a means to use the available space to its best. In this paper, we explore the design space for data compression for wireless sensor and mesh networks by profiling common, publicly available algorithms. Several goals such as a low overhead in terms of utilized memory and compression time as well as a decent compression ratio have to be well balanced in order to find a simple, yet effective compression scheme.

Socio-Spatial Relationships in Dairy Cows

Farm animals may serve as models for evaluating social networks in a controlled environment. We used an automated system to track, at fine temporal and spatial resolution (once per minute, +/- 50 cm) every individual in six herds of dairy cows (Bos taurus). We then analysed the data using social network analyses. Relationships were based on non-random attachment and avoidance relationships in respect to synchronous use and distances observed in three different functional areas (activity, feeding and lying). We found that neither synchrony nor distance between cows was strongly predictable among the three functional areas. The emerging social networks were tightly knit for attachment relationships and less dense for avoidance relationships. These networks loosened up from the feeding and lying area to the activity area, and were less dense for relationships based on synchronicity than on median distance with respect to node degree, relative size of the largest cluster, density and diameter of the network. In addition, synchronicity was higher in dyads of dairy cows that had grown up together and shared their last dry period. This last effect disappeared with increasing herd size. Dairy herds can be characterized by one strongly clustered network including most of the herd members with many non-random attachment and avoidance relationships. Closely synchronous dyads were composed of cows with more intense previous contact. The automatic tracking of a large number of individuals proved promising in acquiring the data necessary for tackling social network analyses.

Ion fractionation in young sea ice from Kongsfjorden, Svalbard

The fractionation of major sea-water ions, or deviation in their relative concentrations from Standard Mean Ocean Water ratios, has been frequently observed in sea ice. It is generally thought to be associated with precipitation of solid salts at certain eutectic temperatures. The variability found in bulk sea-ice samples indicates that the fractionation of ions depends on the often unknown thermal history of sea ice, which affects the structure of pore networks and fate of solid salts within them. Here we investigate the distribution of ions in Arctic sea ice that is a few weeks old with a reconstructible thermal history. We separate the centrifugable (interconnected) and entrapped (likely disconnected) contributions to the ice salinity and determine their ion fractionation signatures. The results indicate that differential diffusion of ions, rather than eutectic precipitation of cryohydrates, has led to significant ion fractionation. The finding emphasizes the role of coupled diffusive–convective salt transport through complex pore networks in shaping the biogeochemistry of sea ice.

ADAM: Administration and Deployment of Adhoc Mesh Networks

Costly on-site node repairs in wireless mesh networks (WMNs) can be required due to misconfiguration, corrupt software updates, or unavailability during updates. We propose ADAM as a novel management framework that guarantees accessibility of individual nodes in these situations. ADAM uses a decentralised distribution mechanism and self-healing mechanisms for safe configuration and software updates. In order to implement the ADAM management and self-healing mechanisms, an easy-to-learn and extendable build system for a small footprint embedded Linux distribution for WMNs has been developed. The paper presents the ADAM concept, the build system for the Linux distribution and the management architecture.

Widespread grey matter changes and hemodynamic correlates to interictal epileptiform discharges in pharmacoresistant mesial temporal epilepsy

Focal onset epilepsies most often occur in the temporal lobes. To improve diagnosis and therapy of patients suffering from pharmacoresistant temporal lobe epilepsy it is highly important to better understand the underlying functional and structural networks. In mesial temporal lobe epilepsy (MTLE) widespread functional networks are involved in seizure generation and propagation. In this study we have analyzed the spatial distribution of hemodynamic correlates (HC) to interictal epileptiform discharges on simultaneous EEG/fMRI recordings and relative grey matter volume (rGMV) reductions in 10 patients with MTLE. HC occurred beyond the seizure onset zone in the hippocampus, in the ipsilateral insular/operculum, temporo-polar and lateral neocortex, cerebellum, along the central sulcus and bilaterally in the cingulate gyrus. rGMV reductions were detected in the middle temporal gyrus, inferior temporal gyrus and uncus to the hippocampus, the insula, the posterior cingulate and the anterior lobe of the cerebellum. Overlaps between HC and decreased rGMV were detected along the mesolimbic network ipsilateral to the seizure onset zone. We conclude that interictal epileptic activity in MTLE induces widespread metabolic changes in functional networks involved in MTLE seizure activity. These functional networks are spatially overlapping with areas that show a reduction in relative grey matter volumes.

Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.