The coverage and energy issues are the fundamental problems which prevent the development of wireless sensor networks. In order to accurately evaluate the monitoring quality (coverage), one needs to model the interactive of sensors, phenomenons and the environment. Furthermore, in collaborative with scheduling algorithm and computer optimization, protocols can improve the overall monitoring quality and prolong the lifetime of network. This thesis is an investigation of coverage problem and its relative applications in the wireless sensor networks. We first discuss the realistic of current boolean sensing model and propose an irregular sensing model used to determine the coverage in the area with obstacles. We then investigate a joint problem of maintaining the monitoring quality and extending the lifetime of network by using scheduling schemes. Since the scheduling problem is NP hard, genetic algorithm and Markov decision process are used to determine an achievable optimal result for the joint problem of coverage-preserving and lifetime-prolong. In order to avoid the cost of centralized or distributed scheduling algorithms, a localized coverage-preserving scheduling algorithm is proposed by exploring the construction process of Voronoi diagram. Besides exploring the coverage characteristic in a static wireless sensor network, we investigate the coverage problem when the mobile elements are introduced into network. We consider the single-hop mobile data gathering problem with the energy efficiency and data freshness concerns in a wireless sensor network where the connectivity cannot be maintained. We first investigate the upper/lower bound of the covering time for a single collector to cover the monitoring area. Through our investigation we show that for a bounded rectangle area a hexagon walk could explore the area more efficiently than a random walk when the edges of area are known. We then propose a virtual force mobile model (VFM) in which the energy consumption for data transmission is modeled as a virtual elastic force and used to guide of mobile collectors to move to optimal positions for energy saving.

Contemporary networking in Canada, like most of the developed world, involves significant use of media to maintain relationships. This is not the use of media for faraway alters where in person contact is difficult, but media use within the very fabric of everyday life alongside in person contact. Past debates about the effects of new media have frequently focused on a medium's potential for social isolation. These debates have resulted in ambiguous, muted or contradictory findings. So instead of suggesting another response to the issue of social isolation, this thesis reorients the focus towards a different question: under what conditions are alters accessible and how does multiple media use affect this accessibility? Rather than suggest that new media simply offer "more" social accessibility, I contend that they complicate social accessibility by offering individuals increasingly differentiated ways to habitually maintain contact with each other. The result of this differentiation is that while individuals might be able to maintain contact with more alters (or at least just as many) in the abstract sense, they end up maintaining contact with the most accessible alters rather than alters with whom one has the strongest ties. This is the conundrum of multiple media use: how is it that each individual medium offers increased convenience but the sum total of media use makes life less convenient, more planned and more complicated? I suggest it is because media use cuts across longstanding social norms of public and private spaces (or public and private time) without offering a coherent normative framework as a substitute. Instead, individuals are differentially accessible via each medium. Moreover, this accessibility is related more to emergent personal habits than to tie strength. Data for this study comes from 350 random-sample surveys and 86 follow-up social network-oriented interviews in East York, a former borough on the east side of downtown Toronto, Canada. The data were collected in 2005, before the widespread adoption of social networking software, but after the widespread adoption of cellular telephones, instant messaging services and email.

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.

<p>Neural networks are mathematical formulations that can be "trained" to perform certain functions. One particular application of these networks of interest in this thesis is to "model" a physical system using only input-output information. The physical system and the neural network are subjected to the same inputs. The neural network is then trained to produce an output which is the same as the physical system for any input. This neural network model so created is essentially a "blackbox" representation of the physical system. This approach has been used at the University of Saskatchewan to model a load sensing pump (a component which is used to create a constant flow rate independent of variations in pressure downstream of the pump). These studies have shown the versatility of neural networks for modeling dynamic and non-linear systems; however, these studies also indicated challenges associated with the morphology of neural networks and the algorithms to train them. These challenges were the motivation for this particular research.</p> <p>Within the Fluid Power Research group at the University of Saskatchewan, a "global" objective of research in the area of load sensing pumps has been to apply dynamic neural networks (DNN) in the modeling of loads sensing systems.. To fulfill the global objective, recurrent generalized neural network (RGNN) morphology along with a non-gradient based training approach called the complex algorithm (CA) were chosen to train a load sensing pump neural network model. However, preliminary studies indicated that the combination of recurrent generalized neural networks and complex training proved ineffective for even second order single-input single-output (SISO) systems when the initial synaptic weights of the neural network were chosen at random.</p> <p>Because of initial findings the focus of this research and its objectives shifted towards understanding the capabilities and limitations of recurrent generalized neural networks and non-gradient training (specifically the complex algorithm). To do so a second-order transfer function was considered from which an approximate recurrent generalized neural network representation was obtained. The network was tested under a variety of initial weight intervals and the number of weights being optimized. A definite trend was noted in that as the initial values of the synaptic weights were set closer to the "exact" values calculated for the system, the robustness of the network and the chance of finding an acceptable solution increased. Two types of training signals were used in the study; step response and frequency based training. It was found that when step response and frequency based training were compared, step response training was shown to produce a more generalized network.</p> <p>Another objective of this study was to compare the use of the CA to a proven non-gradient training method; the method chosen was genetic algorithm (GA) training. For the purposes of the studies conducted two modifications were done to the GA found in the literature. The most significant change was the assurance that the error would never increase during the training of RGNNs using the GA. This led to a collapse of the population around a specific point and limited its ability to obtain an accurate RGNN.</p> <p>The results of the research performed produced four conclusions. First, the robustness of training RGNNs using the CA is dependent upon the initial population of weights. Second, when using GAs a specific algorithm must be chosen which will allow the calculation of new population weights to move freely but at the same time ensure a stable output from the RGNN. Third, when the GA used was compared to the CA, the CA produced more generalized RGNNs. And the fourth is based upon the results of training RGNNs using the CA and GA when step response and frequency based training data sets were used, networks trained using step response are more generalized in the majority of cases.</p>

As the number of cores integrated on a single chip continues to increase, communication has the potential to become a severe bottleneck to overall system performance. The presence of thread sharing and the distribution of data across cache banks on the chip can result in long distance communication. Long distance communication incurs substantial latency that impacts performance; furthermore, this communication consumes significant dynamic power when packets are switched over many Network-on-Chip (NoC) links and routers. Thread migration can mitigate problems created by long distance communication. This thesis presents Moths, which stands for Mobile Threads. Moths is an efficient run-time algorithm that responds automatically to dynamic NoC traffic patterns, providing beneficial thread migration to decrease overall traffic volume and average packet latency. Moths reduces latency by up to 28.4% (18.0% on average) and traffic volume by up to 24.9% (20.6% on average) across a variety of commercial and scientific benchmarks.

User desire for ubiquitous applications on-board a vehicle motivates the necessity for Network Mobility (NEMO) solutions for Vehicular Ad-Hoc Networks (VANETs). Due to the dynamic topology of VANETs, this approach incurs excessive infrastructure cost to maintain stable connectivity and support these applications. Our solution to this problem is focused on a novel NEMO-based Network Architecture where vehicles are the main network infrastructure. Within this Architecture, we present a Network Criticality-based clustering algorithm, which adapts to mobility changes to form stable self-organizing clusters of vehicles and dynamically builds on vehicle clusters to form more stable Mobile Networks. Simulation results show that the proposed method provides more stable clusters, lower handoffs and better connectivity compared to popular density-based vehicle clustering methods. In addition, they confirm the validity of the proposed Network Architecture. The proposed method is also robust to channel error and exhibits better performance when the heterogeneity of vehicles is exploited.

As the number of cores integrated on a single chip continues to increase, communication has the potential to become a severe bottleneck to overall system performance. The presence of thread sharing and the distribution of data across cache banks on the chip can result in long distance communication. Long distance communication incurs substantial latency that impacts performance; furthermore, this communication consumes significant dynamic power when packets are switched over many Network-on-Chip (NoC) links and routers. Thread migration can mitigate problems created by long distance communication. This thesis presents Moths, which stands for Mobile Threads. Moths is an efficient run-time algorithm that responds automatically to dynamic NoC traffic patterns, providing beneficial thread migration to decrease overall traffic volume and average packet latency. Moths reduces latency by up to 28.4% (18.0% on average) and traffic volume by up to 24.9% (20.6% on average) across a variety of commercial and scientific benchmarks.

User desire for ubiquitous applications on-board a vehicle motivates the necessity for Network Mobility (NEMO) solutions for Vehicular Ad-Hoc Networks (VANETs). Due to the dynamic topology of VANETs, this approach incurs excessive infrastructure cost to maintain stable connectivity and support these applications. Our solution to this problem is focused on a novel NEMO-based Network Architecture where vehicles are the main network infrastructure. Within this Architecture, we present a Network Criticality-based clustering algorithm, which adapts to mobility changes to form stable self-organizing clusters of vehicles and dynamically builds on vehicle clusters to form more stable Mobile Networks. Simulation results show that the proposed method provides more stable clusters, lower handoffs and better connectivity compared to popular density-based vehicle clustering methods. In addition, they confirm the validity of the proposed Network Architecture. The proposed method is also robust to channel error and exhibits better performance when the heterogeneity of vehicles is exploited.

Problems related to web application security comes in many ways, one example is inexperience programmers but not only in the way they code and program but also which language and structure they use to code. Not only programmers but Software companies left holes in the software they developed of course without intention.Because is proven that most of the vulnerabilities start in the web application side, as developers we need to follow certain principles, test our code and learn as much as possible about the subject, as a foundation of web application security in order to know how to prevent issues to the most significant treats.The penetration test aimed to help the IT business to discover vulnerabilities in their system ensure their integrity and continue further in the web application security process. The vulnerability research perform in this report is the introduction of a big work that is under continuity for the company.Finally the success of following security standards, process and methodologies applied on this field is considered the best approach to ensure web application security and priceless information you can benefit from.

Contemporary networking in Canada, like most of the developed world, involves significant use of media to maintain relationships. This is not the use of media for faraway alters where in person contact is difficult, but media use within the very fabric of everyday life alongside in person contact. Past debates about the effects of new media have frequently focused on a medium's potential for social isolation. These debates have resulted in ambiguous, muted or contradictory findings. So instead of suggesting another response to the issue of social isolation, this thesis reorients the focus towards a different question: under what conditions are alters accessible and how does multiple media use affect this accessibility? Rather than suggest that new media simply offer "more" social accessibility, I contend that they complicate social accessibility by offering individuals increasingly differentiated ways to habitually maintain contact with each other. The result of this differentiation is that while individuals might be able to maintain contact with more alters (or at least just as many) in the abstract sense, they end up maintaining contact with the most accessible alters rather than alters with whom one has the strongest ties. This is the conundrum of multiple media use: how is it that each individual medium offers increased convenience but the sum total of media use makes life less convenient, more planned and more complicated? I suggest it is because media use cuts across longstanding social norms of public and private spaces (or public and private time) without offering a coherent normative framework as a substitute. Instead, individuals are differentially accessible via each medium. Moreover, this accessibility is related more to emergent personal habits than to tie strength. Data for this study comes from 350 random-sample surveys and 86 follow-up social network-oriented interviews in East York, a former borough on the east side of downtown Toronto, Canada. The data were collected in 2005, before the widespread adoption of social networking software, but after the widespread adoption of cellular telephones, instant messaging services and email.