Measuring human behavioral patterns has broad application across different sciences. An individuals social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure.<p> This thesis presents a data collection system using tiny sensors which is capable of recording humans proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information.<p> In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the models contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection.

Measuring human behavioral patterns has broad application across different sciences. An individuals social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure.<p> This thesis presents a data collection system using tiny sensors which is capable of recording humans proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information.<p> In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the models contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection.

The central thesis of this dissertation is that insight from queueing analysis can effectively guide standard (combinatorial) scheduling algorithms in dynamic environments. Scheduling is generally concerned with complex combinatorial decisions for static problems, whereas queueing theory simplifies the combinatorics and focuses on dynamic systems. We examine a queueing network with flexible servers under queueing and scheduling techniques. Based on the strengths of queueing analysis and scheduling, we develop a hybrid model that guides scheduling with results from the queueing model. In order to include setup times, we create a logic-based Benders decomposition model for a static representation of the queueing network. Our model is able to find optimal schedules up to 5 orders of magnitude faster than the only other model in the literature. A hybrid model is then developed for the dynamic problem and shown to achieve the best mean flow time while also guaranteeing maximal capacity.

The central thesis of this dissertation is that insight from queueing analysis can effectively guide standard (combinatorial) scheduling algorithms in dynamic environments. Scheduling is generally concerned with complex combinatorial decisions for static problems, whereas queueing theory simplifies the combinatorics and focuses on dynamic systems. We examine a queueing network with flexible servers under queueing and scheduling techniques. Based on the strengths of queueing analysis and scheduling, we develop a hybrid model that guides scheduling with results from the queueing model. In order to include setup times, we create a logic-based Benders decomposition model for a static representation of the queueing network. Our model is able to find optimal schedules up to 5 orders of magnitude faster than the only other model in the literature. A hybrid model is then developed for the dynamic problem and shown to achieve the best mean flow time while also guaranteeing maximal capacity.

Made available in DSpace on 2014-06-12T15:51:58Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Diversos trabalhos na área de redes dinâmicas têm sido propostos na literatura com o objetivo de prover à arquitetura da Internet o suporte à mobilidade. O problema dessas redes instáveis na Internet consiste em oferecer um conjunto de mecanismo, tais como endereçamento, gerenciamento da informação e encaminhamento da informação, que suportem informação e entidade (Sistema Autônomo, Provedor de Serviços na Internet e Clientes) móveis. Nesse contexto, alguns trabalhos para arquitetura da Internet têm proposto uma maneira de separar a localização (atualmente o IP) e o nome identificador, devido ao forte relacionamento entre o IP e o nome. Em geral, eles propõem uma abordagem de roteamento na camada overlay para separar essas informações. Outros trabalhos acreditam que este desacoplamento não é suficiente para solucionar os problemas de mobilidade, desde que a dinamicidade gera muitas mensagens de controle e atualizações do vínculo entre o IP e o nome. Por essa razão, os pesquisadores também têm proposto novos modelos para gerenciar a camada overlay. Uma das contribuições deste trabalho é a proposta de uma solução para arquitetura da Internet denominada Stable Society que adota a abordagem de papéis. Um papel é uma unidade funcional que é utilizada para organizar a comunicação. Um importante diferencial da proposta é que além de desvincular o nome e a localização, ela também oferece soluções para os problemas relacionados a estruturação e manutenção da camada overlay. Além disso, este trabalho define quatro papéis: o mensageiro encaminha os dados dentro da sociedade; o guarda é a entidade mais estável para encaminhar mensagens entre as sociedades; o operário armazena informações; e o líder estrutura e gerencia a rede overlay. Reduzindo o escopo para a implementação desta dissertação de mestrado, o mensageiro e o guarda foram considerados como a camada de rede sem distinção de estabilidade, desde que o fornecimento de um mecanismo de gerenciamento do overlay de roteamento foi o objetivo do trabalho. Portanto, como prova do conceito apresentado pela proposta, os líderes e operários foram implementados, porque eles agem de maneira independente de tecnologia de acesso e são fundamentais para solucionar o problema da instabilidade nos processos de armazenamento e descoberta da informação. Como resultado, um novo algoritmo denominado Stable Society model over Distributes Hash Table (SSDHT) foi proposto. Além disso, este algoritmo foi comparado com outras soluções DHT (Chord). Os resultados mostraram que o SSDHT é um bom algoritmo, principalmente quando se aumenta a instabilidade (carga do tráfego, grau de mobilidade e tamanho da rede). Por exemplo, a taxa de mensagens entregue com sucesso foi acima de 90% quando a carga de tráfego, o grau de mobilidade e o tamanho da rede foram variados

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In this paper we have introduced a concept of analyzing the performance of dynamic network organization (NO) in response to multiple input objectives of network organization (NO) and input set of unpredictable external environment, and we have correlated plasticity of NO with this performance measure. A three stage conceptual model of the process has been described which comprised of dynamic system of multi-agent network, multiple objectives and input goals of network driver (S n), unpredictable external environment (ϵ). Dynamic system of the network organization takes through its two phase and processes this based on time and system response variables. Processing of input information by the system variables gives a ratio of system response variable and input. Further analysis is based on the value of this performance values. This model suggests a performance measuring technique which takes an input set of objectives from network driver of the NO, a set of input from external environment (ϵ) state and processes this input based on the existing state of the dynamic system in the NO. This process output shows dynamic system performance in dynamic environment and how this performance coefficient correlates with the Plasticity of network organization. We have presented several examples, mathematical models and graph by analyzing true scenarios on UAV patrolling zone.

Variable bit rate (VBR) compressed video traffic, such as live video news, is expected to account for a large portion of traffic in future integrated networks. This real-time video traffic has strict delay and loss requirements, and exhibits burstiness over multiple time scales, thus imposing a challenge on network resource allocation and management. The renegotiated VBR (R-VBR) scheme, dynamically allocating resources to capture the burstiness of VBR traffic, substantially increases network utilization while satisfying any desired quality of service (QoS) requirements. This thesis focuses on the performance evaluation of R-VBR in the context of different R-VBR approaches. The renegotiated deterministic VBR (RED-VBR) scheme, proposed by Dr. H. Zhang et al., is thoroughly investigated in this research using a variety of real-world videos, with both high quality and low quality. A new Virtual-Queue-Based RED-VBR is then developed to reduce the implementation complexity of RED-VBR. Simulation results show that this approach obtains a comparable network performance as RED-VBR: relatively high network utilization and a very low drop rate. A Prediction-Based R-VBR based on a multiresolution learning neural network traffic predictor, developed by Dr. Y. Liang, is studied and the use of binary exponential backoff (BEB) algorithm is introduced to efficiently decrease the renegotiation frequency. Compared with RED-VBR, Prediction-Based R-VBR obtains significantly improved network utilization at a little expense of the drop rate. This work provides evaluations of the advantages and disadvantages of several R-VBR approaches, and thus provides a clearer big picture on the performance of the studied R-VBR approaches, which can be used as the basis to choose an appropriate R-VBR scheme to optimize network utilization while enabling QoS for the application tasks. / Master of Science

We create a dynamic network model to replicate more closely the population network structures of interest. Network, Norms and HIV/STI Risk Among Youth (NNAHRAY) is a community relationship survey data set, which provides a rare sample of a human risky-behavior contact network. Combining disease compartmental models with our dynamic network model, we simulate the spread of Human Immunodeficiency Virus (HIV) and Herpes Simplex Type 2 Virus (HSV2) with consideration of HSV2's synergistic impact on HIV's transmission. Our model reproduces HIV prevalence, HSV-2 prevalence, and the contact network close to those observed in NNAHRAY, with HIV annual prevalence closer to the estimated values from the literature than those of any disease spread model based on static networks. The success of fitting our model to the target data shows the importance of considering the data sampling process, contact dynamics, and contact network structures. Our model, under certain conditions, has prevalence prediction results that are insensitive to changes in network size. The analysis of various prevention/intervention strategies targeting different risky groups gives important insights into strategy prioritization and illustrates how our model can be used to assist in making public health policy decisions in practice, both for individual diseases and in the more-recent area of study that considers synergy between two diseases.

Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), I investigate the influence of adolescent friendship network on the likelihood of college enrollment, and whether and how this influence is affected by stratification factors (e.g., gender, race/ethnicity, and socioeconomic status). However, there is a challenge in evaluating this influence process since adolescents usually non-randomly select their friends. A selection process needs to be taken into consideration simultaneously with the influence process of adolescents’ friendship network on their likelihood of college enrollment. Previous research on peer effects has methodological issues and limitations. Traditional methods (e.g., multivariate regression, multilevel modeling, or propensity score matching) using limited data (e.g., cross-sectional) and measures of friendship network (e.g., one best friend) could not solve the problem of integrating selection process and influence process in one model. In addition, the dyadic and triadic (or even higher level) dependency among friends in the network makes it more difficult to estimate selection and influence processes using traditional methods. To address these concerns, I employ longitudinal network analysis with stochastic actor-based models (SABMs) to account for the influence of friendship network on adolescent college enrollment when simultaneously considering the selection of friendship. The co-evolution model of network dynamics (selection) and behavioral dynamics (influence) also addresses the problem of endogeneity between network change and behavioral change. However, the co-evolution model requires network data and behavioral data measured in multiple time points, so in the first stage of this research, I generate the predicted probability of college enrollment at three time points of Add Health using traditional logistic regression. Then in the second stage of this research, I use the transformed likelihood of college enrollment, a statistical artifact, as the behavior variable in the co-evolution model to examine how the likelihood of college enrollment affect the friendship selection and in turn friend’s average likelihood of college enrollment in the network influences an adolescent’s own likelihood of college enrollment. In the first stage, I find that there are some levels of gender, race/ethnicity, and SES inequalities in the college enrollment, even after controlling for previous academic achievement, other individual characteristics, family backgrounds, and school level variables. In the second stage, the results of dynamic network analysis indicate significant selection (partial deselection) and influence effects of adolescent friendship networks on the likelihood of college enrollment. In the selection process, adolescents have high tendency to select friends who are similar to them in the likelihood of college enrollment, or terminate friendships with other students of dissimilar likelihood of college enrollment. In the influence process, the average alter effect is found consistently significant and positive across all models and schools, which indicates that there is strong social influence of friendship network on adolescents’ likelihood of college enrollment. The higher the average friends’ likelihood of college enrollment, the more likely the adolescent will increase own likelihood of college enrollment. I also discuss the significance of results and many important policy and practical implications.