abstract: A Virtual Private Network (VPN) is the traditional approach for an end-to-end secure connection between two endpoints. Most existing VPN solutions are intended for wired networks with reliable connections. In a mobile environment, network connections are less reliable and devices experience intermittent network disconnections due to either switching from one network to another or experiencing a gap in coverage during roaming. These disruptive events affects traditional VPN performance, resulting in possible termination of applications, data loss, and reduced productivity. Mobile VPNs bridge the gap between what users and applications expect from a wired network and the realities of mobile computing. In this dissertation, MobiVPN, which was built by modifying the widely-used OpenVPN so that the requirements of a mobile VPN were met, was designed and developed. The aim in MobiVPN was for it to be a reliable and efficient VPN for mobile environments. In order to achieve these objectives, MobiVPN introduces the following features: 1) Fast and lightweight VPN session resumption, where MobiVPN is able decrease the time it takes to resume a VPN tunnel after a mobility event by an average of 97.19\% compared to that of OpenVPN. 2) Persistence of TCP sessions of the tunneled applications allowing them to survive VPN tunnel disruptions due to a gap in network coverage no matter how long the coverage gap is. MobiVPN also has mechanisms to suspend and resume TCP flows during and after a network disconnection with a packet buffering option to maintain the TCP sending rate. MobiVPN was able to provide fast resumption of TCP flows after reconnection with improved TCP performance when multiple disconnections occur with an average of 30.08\% increase in throughput in the experiments where buffering was used, and an average of 20.93\% of increased throughput for flows that were not buffered. 3) A fine-grained, flow-based adaptive compression which allows MobiVPN to treat each tunneled flow independently so that compression can be turned on for compressible flows, and turned off for incompressible ones. The experiments showed that the flow-based adaptive compression outperformed OpenVPN's compression options in terms of effective throughput, data reduction, and lesser compression operations. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 GustavoFS.pdf: 1361196 bytes, checksum: fe1a67dcdb84a334e6c49247c8c68a06 (MD5) Previous issue date: 2005-01-21 / Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required / Comprimir uma imagem consiste, basicamente, em represent?-la atrav?s de uma menor quantidade de dados, sem para tanto comprometer a qualidade da imagem. A grande import?ncia da compress?o de dados fica evidente quando se utiliza quantidade muito grande de informa??es e espa?os pequenos para armazenamento. Com esse objetivo ? que se apresenta esse trabalho no qual desenvolveu-se um m?todo para a compress?o de imagens coloridas e multiespectrais baseado na quantidade de informa??o contida em cada banda ou planos da imagem. Este m?todo foi chamado de Compress?o Auto-Adaptativa (C.A.A.), no qual cada banda da imagem ? comprimida com uma taxa de compress?o diferente, buscando um melhor resultado de forma a manter a maior parte da informa??o. A t?cnica baseia-se na compress?o com maior taxa para a banda com maior redund?ncia, ou seja, menor quantidade de informa??o e com taxas mais amenas ?s bandas com informa??o mais significativa. O CAA utiliza duas transformadas de imagens como elementos ativos da compress?o. A Transformada Cosseno Discreta (DCT) e a An?lise de Componentes Principais (PCA). A Imagem original (sem compress?o) ? processada pelo sistema CAA no espa?o RGB, sob o qual ? aplicado a transformada PCA, que leva a imagem para um novo espa?o (ou planos de dados), no qual as informa??es est?o descorrelacionadas. Neste espa?o gerado pela PCA, realiza-se a DCT em cada um dos planos individualmente, e, atrav?s de um limiar calculado em fun??o do resultado da PCA e de um par?metro de compress?o fornecido pelo usu?rio, ? que alguns elementos da matriz gerada pela DCT s?o descartados. Por fim realiza-se, respectivamente, a DCT e PCA inversas, reconstruindo assim uma aproxima??o da imagem. Quando comparada com a compress?o realizada pela tradicional JPEG (Joint Photographic Experts Group), a CAA apresenta, em m?dia, resultados cerca de 10 % melhores no que diz respeito a MSE (Mean Square Root), com duas grandes vantagens, por ser adaptativa, ? sens?vel ao tipo de imagem, ou seja, apresenta bons resultados em diversos tipos de imagens (sint?tica, paisagens, pessoas, e etc.), e, necessita apenas um par?metro de compress?o determinado pelo usu?rio