Edge-based network disease models, in comparison to classic compartmental epidemiological models, better capture social factors affecting disease spread such as contact duration and social heterogeneity. We reason that there should exist infinitely many equilibria rather than only an endemic equilibrium and a disease-free equilibrium for the edge-based network disease model commonly used in the literature, as there do not exist any changes in demographic in the model. We modify the commonly used network model by relaxing some assumed conditions and factor in a dependency on initial conditions. We find that this modification still accounts for realistic dynamics of disease spread (such as the probability of contracting a disease based off your neighbors' susceptibility to the disease) based on the basic reproduction number. Specifically, if the basic reproduction number is below 1, then the infection dies out; while if the basic reproduction number is above 1, then there is possibility of an epidemic.

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The automation industry interest in Internet of Things (IoT) and Microsoft Azure IoT Hub has increased, as it provides improvements in terms of reducing costs, increases revenue and provides efficient management of devices and machines. Prevas is a company working in this field and in this project the assignment was to look at the possibilities of improving an already existing solution they have.The aim was to set up a connection between a Programmable Logic Controller (PLC) and a Azure IoT hub and get the two components to communicate with each other. Through a simple application the PLC and the Azure IoT hub will exchange data with each other. To establish communication of results, the IoT Edge runtime has been installed to transform the Raspberry Pi into an IoT Edge device. The application was created in Node Red, where it was possible to create a connection and communication between the PLC and the Azure IoT hub. Through this application, notifications and messages can be sent between the PLC and the cloud. In the application, it is checked that a machine is working during specific times and if the machine is off when it should be running, an error message will be sent to the IoT hub. / Inom automationsindustrin har intresset för Internet of Things (IoT) och Microsoft Azure IoT-hubben ökat då det ger förbättringar i form av minskade kostnader och ökade intäkter. IoT tillhandahåller en effektiv hantering av enheter och maskiner då allt kan styras från en och samma plats. Prevas är ett företag som arbetar inom detta, och i detta projekt var uppdraget att se på möjligheterna att förbättra en befintlig lösning som de har.Syftet var att sätta upp en anslutning mellan en Programmable Logic Controller (PLC) och Azure IoT-hubben och få de två komponenterna att kommunicera. Genom en enkel applikation ska PLC:n och Azure IoT-hubben utbyta data med varandra. För att upprätthålla kommunikation för resultat har IoT Edge runtime installerats för att omvandla Raspberry Pi:n till en edge-enhet. Applikationen skapades i Node Red, där det var möjligt att skapa en anslutning och kommunikation mellan PLC:n och Azure IoT-hubben. Genom denna applikation kan meddelanden skickas mellan molnet och enheten. I applikationen så kontrolleras det att en maskin arbetar under specifika tider och är maskinen avstängd när den ska vara igång kommer ett felmeddelande att skickas till IoT-hubben.

This study addressed two aspects of ecological theory developed primarily in North America and examined these theories using an Australian passerine as a model species. The first theory concerns the mechanisms by which habitat fragmentation affects avian populations. I investigated the mechanisms causing the decline of the Brown Treecreeper (Climacteris picumnus) in fragmented habitat, and specifically considered the effects of isolation and habitat degradation, which are potentially important in Australian woodlands, and edge (patch size), which are important in North America. Brown Treecreeper groups were as productive in isolated patches as in connected patches of habitat regardless of patch size, yet unpaired males were common in isolated fragments of habitat. I conducted a field experiment that confirmed that female dispersal was disrupted among isolated fragments. Thus, my results suggested Brown Treecreepers were declining due to disruption of dispersal by habitat fragmentation rather than degradation or edge effects. I compared the results of an individual-based, spatially explicit simulation model to field observations and concluded that territory spatial arrangement and matrix composition altered dispersal success, recruitment, and subsequent population growth. With the aid of a geographic information system, I determined that both landscape factors (fragmentation patterns within 4.5-km) and habitat characteristics (cavity density) explained Brown Treecreeper presence and absence from random locations in woodland habitat. The birds appear to be absent from suitable habitat in unsuitable landscapes. The second theory I addressed concerns the maintenance of avian cooperative breeding. The most widely accepted models to explain cooperative breeding suggest that individuals that delay dispersal obtain a payoff under conditions in which the quality of breeding positions varies greatly. These models arose chiefly from a few long-term studies in North American. This is an unfortunate bias because the occurrence of cooperative breeding among birds of Gondwanan origin is 22%, whereas the worldwide incidence is only 3%. I used demographic and habitat data to examine the influence of habitat and cooperative breeding on Brown Treecreeper fitness. Group size affected one component of fitness and habitat variables affected another. High cavity density may be favorable due to intense inter-specific competition for suitable cavities, which Brown Treecreepers require for roosting and nesting. Low tree density may be advantageous by favoring ground foraging, in which Brown Treecreepers frequently engage. Experimental manipulations of important habitat variables are needed to determine whether variability in these ecological factors is critical in maintaining group formation in this species. / Ph. D.

Boundary layer characteristics at the trailing edge of a tiltrotor blade model were measured using a flattened pitot probe and a single hot wire. The blade was mounted in Virginia Tech Stability Wind tunnel stationary on a turntable on the wind tunnel's upper wall with the tip pointing down. The measurement point was located at 1 mm behind the trailing edge to make it possible to measure the flow near the blade surface and measure the boundary layer on both sides of the trailing edge in a same run. Mean velocity profiles were measured for a variety of Reynolds numbers and angles of attack. Turbulence intensity and spectral measurements were performed using a single hot wire at the highest Reynolds number. Conclusion was reached that both of the flattened pitot probe and single hot wire are good for boundary layer thickness measurements. Displacement thickness, which is important in trailing edge noise prediction, was calculated from the profile data and fit using an algebra expression against the tip angle of attack. Once the relationship between tip angle of attack and local effective angle of attack is obtained by lifting line theory, the results can be used in the trailing edge noise prediction code. / Master of Science

Exploring coordinated relationships is an important task in data analytics. For example, an intelligence analyst may want to find three suspicious people who all visited the same four cities. However, existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. This work presents a visual analytics approach that applies biclusters to support coordinated relationships exploration. Each computed bicluster aggregates individual relationships into coordinated sets. Thus, coordinated relationships can be formalized as biclusters. However, how to incorporate biclusters into a visual analytics tool to support sensemaking tasks is challenging. To address this, this work features three key contributions: 1) a five-level design framework for bicluster visualizations, 2) BiSet, highlighting bicluster-based edge bundling, seriation-based multiple lists ordering, and interactions for dynamic information foraging and management, and 3) an evaluation of BiSet. / Ph. D.

This Thesis is a case study on the design and development of a medical center comprised of three buildings to be located on the Blacksburg Campus of Virginia Tech. The three buildings will form a space unlike any other space on campus -- it will be an Urban Precinct that will introduce a new and inspiring space where students can experience urban life on their own traditionally rural campus. / Master of Architecture

Edge computing is an emerging technology that can overcome the limitations of centralized cloud computing by enabling distributed, low-latency computation at a network edge. Particularly, in edge computing, some of the cloud's functionalities such as storage, processing, and computing are migrated to end-user devices called edge nodes so as to reduce the round-trip delay needed to reach the cloud data center. Despite the major benefits and practical applications of using edge computing, one must address many technical challenges that include edge network formation, computational task allocation, and radio resource allocation, while considering the uncertainties innate in edge nodes, such as incomplete future information on their wireless channel gains and computing capabilities. The goal of this dissertation is to develop foundational science for the deployment, performance analysis, and low-complexity optimization of edge computing under the aforementioned uncertainties. First, the problems of edge network formation and task distribution are jointly investigated while considering a hybrid edge-cloud architecture under uncertainty on the arrivals of computing tasks. In particular, a novel online framework is proposed to form an edge network, distribute the computational tasks, and update a target competitive ratio defined as the ratio between the latency achieved by the proposed online algorithm and the optimal latency. The results show that the proposed framework achieves the target competitive ratio that is affected by the wireless data rate and computing speeds of edge nodes. Next, a new notion of ephemeral edge computing is proposed in which edge computing must occur under a stringent requirement on the total computing time period available for the computing process. To maximize the number of computed tasks in ephemeral edge networks under the uncertainty on future task arrivals, a novel online framework is proposed to enable a source edge node to offload computing tasks from sensors and allocate them to neighboring edge nodes for distributed task computing, within the limited total time period. Then, edge computing is applied for mobile blockchain and online caching systems, respectively. First, a mobile blockchain framework is designed to use edge devices as mobile miners, and the performance is analyzed in terms of the probability of forking event and energy consumption. Second, an online computational caching framework is designed to minimize the edge network latency. The proposed caching framework enables each edge node to store intermediate computation results (IRs) from previous computations and download IRs from neighboring nodes under uncertainty on future computation. Subsequently, online optimization is extended to investigate other edge networking applications. In particular, the problem of online ON/OFF scheduling of self-powered small cell base stations is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs that consist of energy consumption and transmission delay of a network. Such a framework can enable the self-powered base stations to be functioned as energy-efficient edge nodes. Also, the problem of radio resource allocation is studied when a base station is assisted by self-powered reconfigurable intelligent surfaces (RIS). To this end, a deep reinforcement learning approach is proposed to jointly optimize the transmit power, phase shifting, and RIS reflector's ON/OFF states under the uncertainties on the downlink wireless channel information and the harvested energy at the RIS. Finally, the online problem of dynamic channel allocation is studied for full-duplex device-to-device (D2D) networks so that D2D users can share their data with a low communication latency when users dynamically arrive on the network. In conclusion, the analytical foundations and frameworks presented in this dissertation will provide key guidelines for effective design of edge computing in wireless networks. / Doctor of Philosophy / Smart cities will rely on an Internet of Things (IoT) system that interconnects cars, drones, sensors, home appliances, and other digital devices. Modern IoT systems are inherently designed to process real-time information such as temperature, humidity, or even car navigational data, at any time and location. A unique challenge in the design of such an IoT is the need to process large volumes of data over a wireless network that consists of heterogeneous IoT devices such as smartphones, vehicles, home access points, robots, and drones. These devices must perform local (on-device or so-called edge) processing of their data without relying on a remote cloud. This vision of a smart city seen as a mobile computing platform gives rise to the emerging concept of edge computing using which smartphones, sensors, vehicles, and drones can exchange and process data locally on their own devices. Edge computing allows overcoming the limitations of centralized cloud computation by enabling distributed, low-latency computation at the network edge. Despite the promising opportunities of edge computing as an enabler for smart city services such as autonomous vehicles, drones, or smart homes, one must address many challenges related to managing time-varying resources such as energy and storage, in a dynamic way. For instance, managing communication, energy, and computing resources in an IoT requires handling many uncertain factors such as the intermittent availability of wireless connectivity and the fact that the devices do not know a priori what type of tasks they need to process. The goal of this dissertation is to address the fundamental challenges in edge computing under uncertainty in an IoT. In particular, this dissertation introduces novel mathematical algorithms and frameworks that exploit ideas from the fields of online optimization, machine learning, and wireless communication to enable future IoT services such as smart factories, virtual reality, and autonomous systems. In this dissertation, holistic frameworks are developed by designing, analyzing, and optimizing wireless communications systems with an emphasize on emerging IoT applications. To this end, various mathematical frameworks and efficient algorithms are proposed by drawing on tools from wireless communications, online optimization, and machine learning to yield key innovations. The results show that the developed solutions can enable an IoT to operate efficiently in presence of uncertainty stemming from time-varying dynamics such as mobility of vehicles or changes in the wireless networking environment. As such, the outcomes of this research can be used as a building block for the large deployment of smart city technologies that heavily rely on the IoT.

As compared to traditional cloud computing, edge computing provides computational, sensor, and storage resources co-located with client requests, thereby reducing network transmission and providing context-awareness. While server farms can allocate cloud computing resources on demand at runtime, edge-based heterogeneous devices, ranging from stationary servers to mobile, IoT, and energy harvesting devices are not nearly as reliable and abundant. As a result, edge application developers face the following obstacles: 1) heterogeneous devices provide hard-to-access resources, due to dissimilar capabilities, operating systems, execution platforms, and communication interfaces; 2) unreliable resources cause high failure rates, due to device mobility, low energy status, and other environmental factors; 3) resource scarcity hinders the performance; 4) the dissimilar and dynamic resources across edge environments make QoS impossible to guarantee. Edge environments are characterized by the prevalence of equivalent functionalities, which satisfy the same application requirements by different means. The thesis of this research is that equivalent functionalities can be exploited to improve the reliability, efficiency, and adaptiveness of edge-based services. To prove this thesis, this dissertation comprises three key interrelated research thrusts: 1) create a system architecture and programming support for providing edge services that run on heterogeneous and ever changing edge devices; 2) introduce programming abstractions for executing equivalent functionalities; 3) apply equivalent functionalities to improve the reliability, efficiency, and adaptiveness of edge services. We demonstrate how the connected devices with unreliable, dynamic, and scarce resources can automatically form a reliable, adaptive, and efficient execution environment for sensing, computing, and other non-trivial tasks. This dissertation is based on 5 conference papers, presented at ICDCS'20, ICWS'19, EDGE'19, CLOUD'18, and MobileSoft'18 / Doctor of Philosophy / As mobile and IoT devices are generating ever-increasing volumes of sensor data, it has become impossible to transfer this data to remote cloud-based servers for processing. As an alternative, edge computing coordinates nearby computing resources that can be used for local processing. However, while cloud computing resources are abundant and reliable, edge computing ones are scarce and unreliable. This dissertation research introduces novel execution strategies that make it possible to provide reliable, efficient, and flexible edge-based computing services in dissimilar edge environments.