The influx of new data-intensive applications, such as machine learning and artificial intelligence, in high performance computing (HPC) and data centers (DC), has driven the design of efficient interconnection networks to meet the requisite bandwidth of the growing traffic demand. While the exponentially-growing traffic demand is expected to continue into the future, the free scaling of CMOS-based electrical interconnection networks will eventually taper off due to Moore’s Law. These trends suggest that building all-electrical interconnects to meet the increased demand for low latency, high throughput networking will become increasingly impractical going forward. Integrating optical interconnects capable of supporting high bandwidth links and dynamic network topology reconfiguration offer a potential solution to scaling current networks. However, the insertion of photonic interconnection networks offers a massive design space in terms of network topology and control plane that is currently under-explored. The work in this dissertation is centered around the study and development of control plane challenges to aid in the eventual adoption of optically-enabled reconfigurable networks. We begin by exploring Flexspander, a novel reconfigurable network topology that combines the flexible random expander networks construction with topological-reconfigurability using optical circuit switching (OCS). By incorporating random expander graph construction, as opposed to other more symmetric reconfigurable topologies, Flexspander can be built with a broader range of electrical packet switch (EPS) radix, while retaining high throughput and low latency when coupled with multi-path routing. In addition, we propose a topology-routing co-optimization scheme to improve network robustness under traffic uncertainties. Our proposed scheme employs a two-step strategy: First, we optimize the topology and routing strategy by maximizing throughput and average packet hop count for the expected traffic patterns based on historical traffic patterns. Second, we employ a desensitization step on top of the topology and routing solution to lower performance degradation due to traffic variations. We demonstrate the effectiveness of our approach using production traces from Facebook's Altoona data center, and show that even with infrequent reconfigurations, our solution can attain performances within 15\% of an offline optimal oracle. Next, we study the problem of routing scheme design in reconfigurable networks, which is a more under-studied problem compared to routing design for static networks. We first perform theoretical analyses to first identify the key properties an effective routing protocol for reconfigurable networks should possess. Using findings from these theoretical analyses, we propose a lightweight but effective routing scheme that yields high performance for practical HPC and DC workloads when employed with reconfigurable networks. Finally, we explore two fundamental design problems in the optical reconfigurable network design. First, it investigates how different OCS placement in the physical network topology lead to different tradeoffs in terms of power consumption/cost, network performance, and scalability. Second, we investigate how network performance is affected by different reconfiguration periods to understand how frequency of topology reconfiguration affects application performance. Taken together, the work in this dissertation tackles several key challenges related to efficient control plane for reconfigurable network designs, with the goal of facilitating the eventual adoption of optically-enable reconfigurable networks in high performance systems.

Improvement of wireless technologies is a natural consequence of the progress in the field of science and technology. Usage of wireless networks based on the ZigBee technology, which are capable of indepent operation in present form, providing detailed information about physical environment and process management, brings many advantages. On the other hand, there is a serious issue about commissioning. This Master's Thesis deals with introduction of ZigBee technology and its usage, describes issue about device commissioning and types of commissioning. The main part of the Thesis is my own proposal for solving this problem, proposing algorithm using the Bitcloud stack for Coordinator, End Device and Router and its demonstration at the application. Along with analysing the application there are described possible solutions for creating a new network, adding a new node into the existing network and changing a node. The result is a graphical application and firmware for each device. The result of succesfully associated devices in network is supported by the measurement.

The diploma thesis describes theory that is needed for application development for acquisition of communication statistical data from network infrastructure devices. Aplication is called SDSKSI. The project compares protocols suitable for this purpose. Finally SNMP protocol was chosen because it is the most common in network devices. SNMP is described in detail. Each SNMP operation has its own practical demonstration. In the project there is also described MIB database and data types of MIB objects. Application is able to create network topology. Then administrator of network can imagine how the network looks like. For each device that support SNMP protocol are periodically collected and stored statistical data which can be exported to the file. For application development were chosen programming languages according to several criteria. Content of the laboratory exercise is presented. At the end of the project there are some system solutions for collecting statistical data. Diploma thesis contents two attachments. The first is containing the full text of laboratory task. The second is DVD disc. Disc is containing ready to boot aplication SDSKSI.

Wireless Sensor Networks (WSN) offer a flexible low-cost solution to the problem of event monitoring, especially in places with limited accessibility or that represent danger to humans. WSNs are made of resource-constrained wireless devices, which require energy efficient mechanisms, algorithms and protocols. One of these mechanisms is Topology Control (TC) composed of two mechanisms, Topology Construction and Topology Maintenance. This dissertation expands the knowledge of TC in many ways. First, it introduces a comprehensive taxonomy for topology construction and maintenance algorithms for the first time. Second, it includes four new topology construction protocols: A3, A3Lite, A3Cov and A3LiteCov. These protocols reduce the number of active nodes by building a Connected Dominating Set (CDS) and then turning off unnecessary nodes. The A3 and A3-Lite protocols guarantee a connected reduced structure in a very energy efficient manner. The A3Cov and A3LiteCov protocols are extensions of their predecessors that increase the sensing coverage of the network. All these protocols are distributed -they do not require localization information, and present low message and computational complexity. Third, this dissertation also includes and evaluates the performance of four topology maintenance protocols: Recreation (DGTRec), Rotation (SGTRot), Rotation and Recreation (HGTRotRec), and Dynamic Local-DSR (DLDSR). Finally, an event-driven simulation tool named Atarraya was developed for teaching, researching and evaluating topology control protocols, which fills a need in the area of topology control that other simulators cannot. Atarraya was used to implement all the topology construction and maintenance cited, and to evaluate their performance. The results show that A3Lite produces a similar number of active nodes when compared to A3, while spending less energy due to its lower message complexity. A3Cov and A3CovLite show better or similar coverage than the other distributed protocols discussed here, while preserving the connectivity and energy efficiency from A3 and A3Lite. In terms of network lifetime, depending on the scenarios, it is shown that there can be a substantial increase in the network lifetime of 450% when a topology construction method is applied, and of 3200% when both topology construction and maintenance are applied, compared to the case where no topology control is used.

<div><p>Predictive modeling allows us to analyze historical patterns to forecast future events. When the data available for this analysis is imbalanced or skewed, many challenges arise. The lack of sensitivity towards the class with less data available hinders the sought-after predictive capabilities of the model. These imbalanced datasets are found across many different fields, including medical imaging, insurance claims and financial frauds. The objective of this thesis is to identify the challenges, and means to assess, the application of machine learning to transportation data that is imbalanced and using only one independent variable. </p><p>Airlines undergo a decision-making process on air route addition or deletion in order to adjust the services offered with respect to demand and cost, amongst other criteria. This process greatly affects the topology of the network, and results in a continuously evolving Air Traffic Network (ATN). Organizations like the Federal Aviation Administration (FAA) are interested in the network transformation and the influence airlines have as stakeholders. For this reason, they attempt to model the criteria used by airlines to modify routes. The goal is to be able to predict trends and dependencies observed in the network evolution, by understanding the relation between the number of passengers per flight leg as the single independent variable and the airline’s decision to keep or eliminate that route (the dependent variable). Research to date has used optimization-based methods and machine learning algorithms to model airlines’ decision-making process on air route addition and deletion, but these studies demonstrate less than a 50% accuracy. </p><p>In particular, two machine learning (ML) algorithms are examined: Sparse Gaussian Classification (SGC) and Deep Neural Networks (DNN). SGC is the extension of Gaussian Process Classification models to large datasets. These models use Gaussian Processes (GPs), which are proven to perform well in binary classification problems. DNN uses multiple layers of probabilities between the input and output layers. It is one of the most popular ML algorithms currently in use, so the results obtained using SGC were compared to the DNN model. </p><p>At a first glance, these two models appear to perform equally, giving a high accuracy output of 97.77%. However, post-processing the results using a simple Bayes classifier and using the appropriate metrics for measuring the performance of models trained with imbalanced datasets reveals otherwise. The results in both SGC and DNN provided predictions with a 1% of precision and 20% of recall with an score of 0.02 and an AUC (Area Under the Curve) of 0.38 and 0.31 respectively. The low score indicates the classifier is not performing accurately, and the AUC value confirms the inability of the models to differentiate between the classes. This is probably due to the existing interaction and competition of the airlines in the market, which is not captured by the models. Interestingly enough, the behavior of both models is very different across the range of threshold values. The SGC model captured more effectively the low confidence in these results. In order to validate the model, a stratified K-fold cross-validation model was run. </p>The future application of Gaussian Processes in model-building for decision-making will depend on a clear understanding of its limitations and the imbalanced datasets used in the process, the central purpose of this thesis. Future steps in this investigation include further analysis of the training data as well as the exploration of variable-optimization algorithms. The tuning process of the SGC model could be improved by utilizing optimal hyperparameters and inducing inputs.<br></div><div><div><br></div></div>

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Stream fish assemblages exist within stream networks defined by the size and proximity of connected streams (i.e., stream network topology). The spatial position of sites within stream networks may therefore regulate opportunities for fish dispersal to access distant resources or colonize "new" habitats. Such inter-stream dispersal dynamics will influence local fish assemblage structure and the vulnerability of local assemblages to anthropogenic stressors. In this dissertation, I explored the effects of stream network topology on fish assemblage structure in the mid-Atlantic highlands, USA and tested the hypothesis that dispersal would affect the sensitivity of fish-based environmental quality assessments (i.e., bioassessments). In chapter 1, I evaluated the effects of stream networks by comparing fish assemblages between sites with and without large downstream confluences (>3rd order) in western Virginia, USA (i.e., mainstem tributaries and headwater tributaries, respectively). I found that local species richness was higher in mainstem tributaries than headwater tributaries and that these effects could not be explained by variation in local environmental habitat conditions. In chapter 2, I developed and applied a continuous model of stream network topology to explore the effects of downstream size and proximity on local fish assemblage structure within the mid-Atlantic highlands. I found that fish assemblage structure (i.e., Bray-Curtis distances in species abundance) was significantly related to variation in stream network topology up to approximately 9 fluvial km from sites. Chapters 3 and 4 explored the implications of inter-stream dispersal for fish bioassessments. In Chapter 3, I identified 10 fish metrics that corresponded predictably to environmental stressors in the mid-Atlantic highlands. However, headwater tributary assemblages showed stronger relations to local environmental quality than mainstem tributaries, consistent with the hypothesis of riverine dispersal. In Chapter 4, I compared the effects of stream network topology on fish and benthic macroinvertebrate assemblages. Fish metrics were influenced by the size and proximity of connected streams but benthic macroinvertebrate metrics were not. This finding suggests that stream fishes may complement benthic macroinvertebrate bioassessments by indicating environmental conditions at larger spatial grains. / Ph. D.

Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience. / Doctor of Philosophy / Transportation infrastructure systems are among the most fundamental facilities and systems in urban areas due to the role they play in mobility, economy, and environmental sustainability. Due to this importance, it is crucial to ensure their resilience to regular disruptions such as traffic congestions as a priority for engineers and policymakers. The resilience of transportation systems has often been studied when disasters or extreme events occur. However, minor disturbances such as everyday operational traffic situations can also play an important part in reducing the efficiency of transportation systems and should be considered in the overall resilience of the systems. Current literature does not consider traffic performance from the lens of resilience despite its importance in evaluating the overall performance of roads. This research addresses this gap by proposing to leverage the concept of resilience for evaluation of roadways performance and identifying the role of urban characteristics in the enhancement of resilience. We first characterized resilience considering the performance of the roadways over time, ranging from the occurrence of disruptions to the time point when the system performance returns to a stable state. Through a case study on some of the major highways in the Los Angeles metropolitan area and by leveraging the data from the Performance Measurement System (PeMS), we have investigated how accounting for a proposed multi-dimensional approach for quantification of resilience could add value to the process of road network performance assessment and the corresponding decision-making. In the second and third parts of this dissertation, we looked at the urban infrastructure elements and how they affect resilience to regular disruptive congestion events. Specifically, in the second study, we focused on alternative transit modes such as bus, metro, or bike presence in the urban areas. We utilized diversity and equity concepts for assessing the opportunities they provide for people as alternative mobility modes. The proposed metrics not only capture the physical attributes of the multi-modal transportation systems (i.e. availability and distribution of transit modes in urban areas) but also consider the socio-economic factors (i.e. the number of people that could potentially use the transit mode). In the third study, we investigated how urban road networks' form and topology (i.e., the structure of roadway networks) could affect its resilience to recurrent congestions. We presented our findings as a case study in the Washington DC area. Results indicated a strong correlation between resilience and resourcefulness as well as topology features. The findings allow decision-makers to make more informed design and operational decisions and better incorporate the urban characteristics during the priority setting process.