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Wireless Sensor Networks (WSN) is an important technology that can be used to provide new data sets for many applications ranging from healthcare monitoring to military surveillance. Due to the increasing popularity of WSNs, user demands have evolved as well. To achieve the end-to-end goals and requirements of the applications, managing the resources of the network becomes a critical task. Cognitive networking techniques for resource management have been proposed in recent years to provide performance gains over traditional design methodologies. However, even though several tools have been considered in cognitive network design, they show limitations in their adaptability, complexity, and their ability to consider multiple conflicting goals. Thus, this thesis proposes novel cognitive solutions for WSNs that include a reasoning machine and a learning protocol. Weighted Cognitive Maps (WCM) and Q-Learning are identified as suitable tools for addressing the aforementioned challenges and designing the cognitive solutions due to their ability to consider conflicting objectives with low complexity. WCM is a mathematical tool that has powerful inference capabilities. Thus, they are used to design a reasoning machine for WSNs. Two case studies are proposed in this thesis that illustrate the capabilities of WCMs and their flexibility in supporting different application requirements and network types. In addition, an elaborate theoretical model based on Markov Chains (MC) is proposed to analyze the operation of the WCM system. Extensive computer simulations and analytical results show the ability of the WCM system to achieve the end-to-end goals of the network and find compromises between conflicting constraints. On the other hand, Q-Learning is a well known reinforcement learning algorithm that is used to evaluate the actions taken by an agent over time. Thus, it is used to design a learning protocol that improves the performance of the WCM system. Furthermore, to ensure that the learning protocol operates efficiently, methods for improving the learning speed and achieving distributed learning across multiple nodes are proposed as well. Extensive computer simulations show that the learning protocol improves the performance of the WCM system in several metrics. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-02-04 16:24:55.385

Cooperative Spectrum Sensing (CSS) allows Cognitive Radio Networks (CRNs) to locate vacant spectrum channels and to protect active Primary Users (PUs). However, the achieved detection accuracy is proportional to the duration of the CSS process which, unfortunately, reduces the time of useful communication as well as increases the Co-Channel Interference (CCI) perceived by an active PU. To overcome this, this thesis proposes three CSS strategies, namely the Dual-Threshold CSS (DTCSS), the Maximum CSS (MCSS), and the Max-Min CSS (MMCSS). These strategies reduce the number of reporting terminals while maintaining reliable performance and minimal CCI e ect. The performance of these three methods is analyzed, and the numerical and simulations results illustrate the accuracy of the derived results as well as the achieved performance gains. The second part of this thesis studies the impact of CCI on the performance of a number of transmission techniques used by CRNs. These are Chase combining Hybrid Automatic Repeat Request (HAQR), Fixed Relaying (FR), Selective Relaying (SR), Incremental Relaying (IR), and Selective Incremental Relaying (SIR). The performance of these techniques is studied in terms of the average spectral e ciency, the outage probability, and the error probability. To obtain closed forms for the error probabilities, this thesis proposes a novel accurate approximation of the exponential integral function using a sum of exponentials. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-02-05 13:39:22.35

<p> This dissertation couples the growing corpus of human subjects and behavioral research in information security with large-scale data and robust quantitative methods. Linking human subject experimentation with theoretical models enables the information security community to reason more effectively about the system-wide effects of user behavior. I examine how users interact with the digital environment, how those interactions affect decision-making, and how aggregate decision-making affects system-wide vulnerabilities. This interdisciplinary challenge requires a combination of techniques from cognitive neuroscience, social network analysis, human-subjects research, dynamical systems, network theory, and agent-based models. </p><p> In the first section, eye-tracking data demonstrates the relationships between expertise and online perceptual awareness of security cues. Expertise is shown to be only a small factor in attention to security cues, and task-type proves to be much larger indicator of attention, with tasks requiring the use of personal accounts driving attention to cues. This section uses Bayesian ANOVA to evaluate users' perceptual awareness of security cues as they complete common online tasks, as it relates to user sophistication and task type. </p><p> The second section uses a theoretical epidemiological model of malware spread to investigate factors that might mitigate the prevalence of malware in a coupled, two-population model. This both demonstrates that cost is the largest factor for affecting malware prevalence, outside of malware infection rates, and identifies appropriate strategies for system-wide botnet mitigation. </p><p> The final section utilizes an agent-based model of mobile application adoption combined with social network data and mobile marketplace policy. The result is an examination of the dynamic effects of user and market behavior on the spread of mobile malware and the second order effects, such as privacy loss, due to that spread. This model reveals that well-regulated markets are effective at limiting malware spread, but user behavior grows in importance as markets become less restricted. </p><p> Each study examines ways in which users interact with their technology, the aggregate effects of those behaviors, and identifies possible inflection points to change system-wide behaviors. This dissertation integrates empirical behavioral studies to develop a better understanding of digital behavior, thus enabling a more holistic approach to information security.</p>