As the number of Internet of Things (IoT) devices that are used daily is increasing, the inadequacy of cloud computing to provide neseccary IoT-related features, such as low latency, geographic distribution and location awareness, is becoming more evident. Fog computing is introduced as a new computing paradigm, in order to solve this problem by extending the cloud‟s storage and computing resources to the network edge. However, the introduction of this new paradigm is also confronted by various security threats and challenges since the security practices that are implemented in cloud computing cannot be applied directly to this new architecture paradigm. To this end, various papers have been published in the context of fog computing security, in an effort to establish the best security practices towards the standardization of fog computing. In this thesis, we perform a systematic literature review of current research in order to provide with a classification of the various security threats and challenges in fog computing. Furthermore, we present the solutions that have been proposed so far and which security challenge do they address. Finally, we attempt to distinguish common aspects between the various proposals, evaluate current research on the subject and suggest directions for future research.

Fog harvesting is emerging as a promising means to ease the water shortage crisis in arid regions of the world with ample fog. The current state-of-the-art for fog harvesting is mesh netting, which is accessible yet struggles from a dual constraint: a course mesh lets most microscopic fog droplets pass through it, while a fine mesh clogs. In recent years, fog harps have been gaining attention as a superior alternative to meshes, bypassing these inherent constraints. In this work, we expand upon previous fog harp research with a focus on optimization. First, we analyze wire tangling in a harp due to capillary forces, resulting in a mathematical model that is able to predict when wire tangling will occur. Second, we systematically vary three key parameters of a fog harp (wire material, center-to-center wire pitch, and wire length), arriving at an optimal combination. Finally, we develop a numerical model to describe the dynamics of a fog droplet sliding down a harp wire while coalescing with others littered along it. By applying all knowledge acquired through these studies, the next generation of fog harps will push the performance ceiling of practical fog harvesters higher than ever. / Master of Science / The human population continues to grow, and with it the demand for fresh water. This need has caused many to turn to unconventional sources of water, including fog (the suspension of microscopic liquid water droplets in the air). Fog harvesters already exist in arid regions of the world as mesh nets, but suffer dual constraints from their grid-like structure: course meshes fail to capture most fog droplets passing through, while fine meshes get clogged. To bypass these inherent limits, we turn to nature for a solution. It has been observed that California redwood trees are able to effectively collect fog on their straight leaf needles, dripping droplets to the roots below. Inspired by this, we fabricate a device called a fog harp, which removes the impeding horizontal wires of meshes to effectively capture and slide droplets down its vertical wires. In this work, we expand upon previous fog harp research by investigating ways to optimize its water collection efficiency. First, we develop a mathematical model to describe the tangling of harp wires due to merging droplets on adjacent wires pulling them together. Second, we systematically vary three key parameters of the fog harp (wire material, center-to-center wire spacing, and wire length) to arrive at the optimal combination. Finally, we develop a model to describe the dynamics of droplets sliding down harp wires while merging with others littered along it. These studies will raise the performance ceiling of fog harps and push them to real-world applications.

The integration of Fog-Cloud computing in manufacturing has given rise to a new paradigm called Fog manufacturing. Fog manufacturing is a form of distributed computing platform that integrates Fog-Cloud collaborative computing strategy to facilitate responsive, scalable, and reliable data analysis in manufacturing networks. The computation services provided by Fog-Cloud computing can effectively support quality prediction, process monitoring, and diagnosis efforts in a timely manner for manufacturing processes. However, the communication and computation resources for Fog-Cloud computing are limited in Fog manufacturing. Therefore, it is significant to effectively utilize the computation services based on the optimal computation task offloading, scheduling, and hardware autoscaling strategies to finish the computation tasks on time without compromising on the quality of the computation service. A prerequisite for adapting such optimal strategies is to accurately predict the run-time metrics (e.g., Time-latency) of the Fog nodes by capturing their inherent stochastic nature in real-time. It is because these run-time metrics are directly related to the performance of the computation service in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The run-time metrics that reflect the performance in the Fog nodes are heterogenous in nature and the performance cannot be effectively modeled through traditional predictive analysis. In this thesis, a multi-task learning methodology is adopted to predict the run-time metrics that reflect performance in Fog manufacturing by addressing the heterogeneities among the Fog nodes. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model and benchmark models. The proposed model can be further extended in computation tasks offloading and architecture optimization in Fog manufacturing to minimize the time-latency and improve the robustness of the system. / Master of Science / Smart manufacturing aims at utilizing Internet of things (IoT), data analytics, cloud computing, etc. to handle varying market demand without compromising the productivity or quality in a manufacturing plant. To support these efforts, Fog manufacturing has been identified as a suitable computing architecture to handle the surge of data generated from the IoT devices. In Fog manufacturing computational tasks are completed locally through the means of interconnected computing devices called Fog nodes. However, the communication and computation resources in Fog manufacturing are limited. Therefore, its effective utilization requires optimal strategies to schedule the computational tasks and assign the computational tasks to the Fog nodes. A prerequisite for adapting such strategies is to accurately predict the performance of the Fog nodes. In this thesis, a multi-task learning methodology is adopted to predict the performance in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The metrics that reflect the performance in the Fog nodes are heterogenous in nature and cannot be effectively modeled through conventional predictive analysis. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model and benchmark models. The results show that the multi-task learning model has better prediction accuracy than the benchmarks and that it can model the heterogeneities among the Fog nodes. The proposed model can further be incorporated in scheduling and assignment strategies to effectively utilize Fog manufacturing's computational services.

In this work, we were motivated to develop novel devices for water harvesting inspired by natural trees, and to understand their collection efficiency and working principles. We accomplished that with scale-model and large-scale fog harps, floating leaves, and synthetic trees. Fluids mechanics, physics, and thermodynamics were applied to solve the problems and rationalize the results. Redwood-inspired fog harps were designed with stainless steel vertical wires, using 3D-printing and laser-cutting techniques. Fog harps always harvested more water than any of the meshes, tested both under heavy fog and light fog conditions. The aerodynamic efficiency, deposition efficiency, and sliding efficiency were calculated to compare the fog harvesting performance. These findings provide insight into the new design of fog harvesters with high-efficiency fog harvesting performance, and future development of large fog harps, applied into regions even with light fog conditions, as an economically viable means. synthetic trees were fabricated with a nanoporous ceramic disk and silicone tubes. This tree system was tested in an environmental chamber (6 cm short trees) or a plant growth chamber (3m tall trees), both with controlled ambient humidities. The system pressure was calculated with Darcy's equation, Poiseuille equation and Laplace equation. The stable transpiration can happen to any scalable tree, which pumps water up an array of large tubes. Our synthetic trees, like natural trees, have the ability to lift water across a wide range of water temperatures and ambient humidities. They can be used as the large-scale evaporation-driven hydraulic pump, for example, pumped storage hydropower, filtration, underground water extraction. / Doctor of Philosophy / The purpose of this work is to investigate and characterize novel techniques for water harvesting that are inspired by natural trees. We are interested in two modes of water harvesting in particular: fog harps and synthetic trees. Fog harps were comprised of only vertical wires, inspired by the parallel structures of redwoods, which can capture and shed off fog droplets efficiently. Fog harps harvested more water than the traditional mesh nets, both under heavy fog and light fog conditions. Redwood-inspired fog harps have the high-efficient fog harvesting performance. They can be set up at coastal deserts to collect water from fog, where there is scarce rainfall but plenty of fog, like Chile, Peru and South Africa. Synthetic trees were designed with nanoporous disk (leaf) and tubes (xylem conduits), inspired by the transpiration process in natural trees. This transpiration-powered pump can lift water against the gravity at large scales, driven by the water evaporating from the nanopores. They can be used as the large-scale evaporation-driven hydraulic pump, for example, pumped storage hydropower, filtration, underground water extraction.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2001. / Bibliography: leaves 81-87.

Diss. (sammanfattning) Lund : Univ., 2001 / Bilagan utgöres av sammanfattning på svenska med titeln: Experimentella studier av aerosolpartiklars växelverkan med moln och dimma. Adobe PDF with 49 leaves. Härtill 5 uppsatser. Includes bibliographical references. Also available in PDF via the World Wide Web.

Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 43-44.

Die Modellkomponenten für parametrisierteWolkenphysik, Strahlung und Sichtweitenbestimmung im Nebelvorhersagemodell PAFOG, das kürzlich in Zusammenarbeit mit dem Deutschen Wetterdienst als lokales Vorhersagesystem entwickelt wurde und für die Kurzfristprognose eingesetzt werden kann, werden vorgestellt. Die Modellphilosophie orientiert sich an einer mathematisch-physikalisch fundierten Beschreibung der beteiligten meteorologischen Prozesse, deren Einzelheiten in dieser Arbeit diskutiert werden. / This paper presents the model components for parameterized cloud physics, radiation and visibility determination as implemented in the local forecast model PAFOG. PAFOG has been recently developed in cooperation with the GermanWeather Service DWD. PAFOG can be employed for short-range forecasts of radiation fog and visibility. The philosophy of the model strongly emphasizes a mathematically and physically based formulation of the involved meteorological processes the details of which are discussed in this paper.

Die im Nebelvorhersagemodell PAFOG enthaltenen Modellkomponenten für parametrisierte Wolkenphysik, Strahlung und Sichtweitenbestimmung wurden durch Module zur Beschreibung der Interaktion mit dem Boden und der Vegetation ergänzt. Das auf diese Weise komplettierte Modellsystem PAFOG-V kann dazu verwendet werden, das lokale Auftreten von Strahlungsnebel und niedriger stratiformer Bewölkung vorherzusagen. / The paper presents an extension of the model components for parameterized cloud physics, radiation and visibility determination as implemented in the local forecast model PAFOG to include the interaction with the soil and the vegetation. The resulting forecast system PAFOG-V can be used to predict local events of radiation fogs and of low level stratiform clouds.

A detailed examination of low visibility (LV) occurrences and the weather types that cause low visibility, with a focus on fog, was performed for five weather stations in the western Canadian Arctic, in the vicinity of the Amundsen Gulf area of the eastern Beaufort Sea. A series of climatologies were developed that established patterns of LV occurrence as a proportion of all observations and as a function of LV events caused by fog. Frequency climatologies for other weather types were also performed; in particular, for snow, blowing snow, rain, and drizzle. Annual climatologies were used to identify trends in several weather parameters over the 1980-2015 period of study. Monthlies were used to identify typical patterns of occurrence over the course of a year, and hourlies over the course of a day. A dataset of multi-hour fog events was also created; some of these were related to synoptic patterns. Analysis was also broken down by season. Results indicate several things. Monthly climatologies showed considerable diversity across the study area. Three distinct groupings were noted: Tuktoyaktuk and Ulukhaktok with a maximum frequency of LV conditions in February, Aklavik and Inuvik with a maxiumum frequency in October, and Sachs Harbour in August. The February maximum in Tuktoyaktuk and Ulukhaktok was related to cold air temperatures combined with small amounts of moisture from sea ice leads. The Alkavik and Inuvik October maximum was related to moisture advected over land from remaining open water, as well as diurnal snow melt adding moisture to the boundary layer that condenses as the evening cools off. The August maximum in Sachs Harbour is a reflection of proximity to open water and cold air temperatures. Hourly climatologies in the spring/fall season showed most stations have maximum occurrence of LV events caused by fog in the early morning. This is a radiative effect; cooling overnight causes radiation fog that peaks in occurrence just as morning begins. This peak is pushed into the midday in the winter, and is much weaker in the summer, both reflections of the changing pattern of daylight hours. / Graduate