Em redes de sensores sem fio (RSSFs), o nó sorvedouro geralmente é a única entidade confiável. Uma RSSF desassistida é aquela na qual o nó sorvedouro está indisponível por um período de tempo, sendo necessário armazenar os dados coletados ao invés de transmiti-los para a entidade segura. Portanto, até o nó sorvedouro estar novamente disponível para a recepção dos dados, um adversário pode comprometer nós sensores distribuídos em uma região geográfica com o objetivo de encontrar e apagar determinadas unidades de dado. Com o objetivo de evitar este tipo de ataque, fornecendo sobrevivência de dados, estratégias geralmente utilizam confidencialidade e redundância de dados de forma que a agregação de dados por nós sensores seja inviável. Além disso, o decorrer do tempo permite que um adversário tenha maior poder de ataque através do comprometimento de diferentes conjuntos de nós sensores. Apresenta-se um protocolo de segurança para RSSFs desassistidas que, através da renovação do estado da rede, reduz a vantagem de um adversário em montar ataques a partir de dados obtidos com o decorrer do tempo. O mecanismo proposto fornece segurança para múltiplas unidades de dados e permite o uso de agregação de dados por nós sensores. Como forma de avaliação da proposta, foram realizados experimentos em nós sensores reais, além da elaboração de um modelo analítico e de simulações. Resultados indicam como usar o protocolo proposto em diferentes cenários para maximizar a sobrevivência de dados. Adicionalmente, apresenta-se a aplicabilidade do método proposto em cenários de redes heterogêneas, caso das redes de segurança pública, utilizando-se do paradigma de redes definidas por software (SDN) para implantação da rede. / In Wireless Sensor Networks, the base station is usually the only unconditionally trusted entity. If it is not connected for a period of time, the network is left unattended and sensor nodes cannot offload data in real time. Thus, until the base station becomes available, adversaries can compromise some sensor nodes and selectively destroy data. In order to prevent such attacks, providing the so-called data survival, strategies generally employ the use of cryptography in a scenario where decryption keys are exclusively in possession of the base station, which restricts the use of in-network aggregation. In addition, as time increases, mobile adversaries become stronger due to the ability of compromising different sets of nodes. We present a secure protocol that refreshes the state of the network and prevents adversaries from taking advantage of data obtained in prior rounds. The scheme provides security to multiple units of data and allows data aggregation to be performed by nodes. In order to evaluate the performance of the proposed scheme, we carried out measurements on real devices. We analyzed the security of the scheme analytically and through simulations. Results indicate increased time epochs before an adversary succeeds the attack, leading to more probability of data survival. We also apply our protocol to scenarios of heterogeneous networks, namely a public safety network scenario, which is deployed under the paradigm of software-defined networking (SDN).

Em redes de sensores sem fio (RSSFs), o nó sorvedouro geralmente é a única entidade confiável. Uma RSSF desassistida é aquela na qual o nó sorvedouro está indisponível por um período de tempo, sendo necessário armazenar os dados coletados ao invés de transmiti-los para a entidade segura. Portanto, até o nó sorvedouro estar novamente disponível para a recepção dos dados, um adversário pode comprometer nós sensores distribuídos em uma região geográfica com o objetivo de encontrar e apagar determinadas unidades de dado. Com o objetivo de evitar este tipo de ataque, fornecendo sobrevivência de dados, estratégias geralmente utilizam confidencialidade e redundância de dados de forma que a agregação de dados por nós sensores seja inviável. Além disso, o decorrer do tempo permite que um adversário tenha maior poder de ataque através do comprometimento de diferentes conjuntos de nós sensores. Apresenta-se um protocolo de segurança para RSSFs desassistidas que, através da renovação do estado da rede, reduz a vantagem de um adversário em montar ataques a partir de dados obtidos com o decorrer do tempo. O mecanismo proposto fornece segurança para múltiplas unidades de dados e permite o uso de agregação de dados por nós sensores. Como forma de avaliação da proposta, foram realizados experimentos em nós sensores reais, além da elaboração de um modelo analítico e de simulações. Resultados indicam como usar o protocolo proposto em diferentes cenários para maximizar a sobrevivência de dados. Adicionalmente, apresenta-se a aplicabilidade do método proposto em cenários de redes heterogêneas, caso das redes de segurança pública, utilizando-se do paradigma de redes definidas por software (SDN) para implantação da rede. / In Wireless Sensor Networks, the base station is usually the only unconditionally trusted entity. If it is not connected for a period of time, the network is left unattended and sensor nodes cannot offload data in real time. Thus, until the base station becomes available, adversaries can compromise some sensor nodes and selectively destroy data. In order to prevent such attacks, providing the so-called data survival, strategies generally employ the use of cryptography in a scenario where decryption keys are exclusively in possession of the base station, which restricts the use of in-network aggregation. In addition, as time increases, mobile adversaries become stronger due to the ability of compromising different sets of nodes. We present a secure protocol that refreshes the state of the network and prevents adversaries from taking advantage of data obtained in prior rounds. The scheme provides security to multiple units of data and allows data aggregation to be performed by nodes. In order to evaluate the performance of the proposed scheme, we carried out measurements on real devices. We analyzed the security of the scheme analytically and through simulations. Results indicate increased time epochs before an adversary succeeds the attack, leading to more probability of data survival. We also apply our protocol to scenarios of heterogeneous networks, namely a public safety network scenario, which is deployed under the paradigm of software-defined networking (SDN).

Pharmacokinetic and pharmacodynamic models can be applied to clinical study data using various modelling approaches depending on the aim of the analysis. In population pharmacokinetics for instance, simple compartmental models can be employed to describe concentration-time data, identify prognostic factors and interpolate within well-defined experimental conditions. The first objective of this thesis was to illustrate such a ‘semi-mechanistic’ pharmacokinetic modelling approach using mavoglurant as an example of a compound under clinical development. In particular, methods to accurately characterise complex oral pharmacokinetic profiles and evaluate the impact of absorption factors were investigated. When the purpose of the model-based analysis is to further extrapolate beyond the experimental conditions in order to guide the design of subsequent clinical trials, physiologically-based pharmacokinetic (PBPK) models are more valuable as they incorporate information not only on the drug but also on the system, i.e. on mammillary anatomy and physiology. The combination of such mechanistic models with statistical modelling techniques in order to analysis clinical data has been widely applied in toxicokinetics but has only recently received increasing interest in pharmacokinetics. This is probably because, due to the higher complexity of PBPK models compared to conventional pharmacokinetic models, additional efforts are required for adequate population data analysis. Hence, the second objective of this thesis was to explore methods to allow the application of PBPK models to clinical study data, such as the Bayesian approach or model order reduction techniques, and propose a general mechanistic modelling workflow for population data analysis. In pharmacodynamics, mechanistic modelling of clinical data is even less common than in pharmacokinetics. This is probably because our understanding of the interaction between therapeutic drugs and biological processes is limited and also because the types of data to analyse are often more complex than pharmacokinetic data. In oncology for instance, the most widely used clinical endpoint to evaluate the benefit of an experimental treatment is survival of patients. Survival data are typically censored due to logistic constraints associated with patient follow-up. Hence, the analysis of survival data requires specific statistical techniques. Longitudinal tumour size data have been increasingly used to assess treatment response for solid tumours. In particular, the survival prognostic value of measures derived from such data has been recently evaluated for various types of cancer although not for pancreatic cancer. The last objective of this thesis was therefore to investigate different modelling approaches to analyse survival data of pancreatic cancer patients treated with gemcitabine, and compare tumour burden measures with other patient clinical characteristics and established risk factors, in terms of predictive value for survival.

This work introduces nonparametric models which are used in time to event data analysis. It is focused on applying these methods in medicine where it is called survival analysis. The basic techniques and problems, which can appear in survival analysis, are presented and explained here. The Kaplan -- Meier estimator of survival function is discussed in the main part. This is the most frequented method used for estimating the survival function in patients who have undergone a specific treatment. The Kaplan -- Meier estimator is also a common device in the statistical packets. In addition to estimation of survival function, the estimation of hazard function and cumulative hazard function is presented. The hazard function shows the intensity of an individual experiencing the particular event in a short time period. Special problems occur when analyzing time to event data. A distinctive feature, often present in such data, is known as censoring. That is the situation when the individual does not experience the event of interest at the time of study. The thesis covers also an empiric part, where the results of an analysis of patients with the larynx carcinoma diagnosis are shown. These patients were treated in a hospital located in České Budějovice. This analysis is based on a theory presented in the previous chapters.

This thesis introduces methods used in time-to-date analysis. It is written generally and so usable in dealing with any example. The thesis deals with problem of censoring, which means, that some observations occurred after the following, which is typical for the lifetime analysis. Methods mentioned in the thesis are nonparametric and parametric estimates of the survival function and their characteristics, and regression models, concretely Cox model and accelerated failure time model, which examine effect of the covariates on survival function. In the thesis is beside survival function presented hazard function, which express intensity of the analyzed event and cumulative hazard function, which is created as the name suggests by cumulative summation of the hazard function. Estimates of these functions are obtainable from survival function and for parametric estimate often exists formula resulting from parameters of used distribution. Empirical part of the thesis introduces influence of several different types and degrees of censoring on parametric and nonparametric estimates of the survival function, mean and median. The other empirical example is the usage of regression analysis on the data from the lungs cancer research made by Mayo Clinic.