published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Hidden terminals are a key reason for performance degradation in wireless networks. When transmitting nodes cannot carrier sense each other, their packets can collide at receiving nodes, causing packet loss. In the IEEE 802.11 protocol, the RTS/CTS mechanism was introduced to combat the hidden terminal problem. While RTS/CTS can help improve network performance when hidden terminals exist, it can decrease the performance in the absence of hidden terminals due to the overhead of sending additional control traffic. For this reason, RTS/CTS is usually disabled in default driver settings. In this thesis, we present an algorithm for dynamically adjusting the use of RTS/CTS. The algorithm, called SmartRTS, continuously monitors traffic feedback in order to decide whether RTS/CTS should be used. The goal is to enable RTS/CTS in the face of hidden terminals and disable RTS/CTS when hidden terminals do not exist. We find SmartRTS to be effective and easily employed. With extensive simulations using both simple and random topologies, we demonstrate the effectiveness of SmartRTS, especially over static RTS/CTS configurations (ie- RTS/CTS enabled or RTS/CTS disabled). SmartRTS can adapt to the appearance and disappearance of hidden terminals and substantially improve overall network throughput by as much as 11-35%. / text

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The increased deployment of ubiquitous wireless networks has exponentially increased the complexity to detect wireless network attacks and protect against them. In this research, we investigated the vulnerabilities in wireless networks, developed a comprehensive taxonomy of wireless attacks that has been used to guide our approachto develop, and successfully implement a self-protection wireless system capable of detecting and protecting wireless networks from a wide range of attacks.In the past few years, more security improvements took place, but the network is still vulnerable to complex, dynamic, and knowledgeable attacks; in addition, a large number of last-generation unsecured network cards are still available on the market. This dissertation presents an anomaly-based wireless intrusion detection and response system, which is capable of detecting complex malicious attacks. Our approach is based on multi-channel online monitoring and analysis of wireless network features with respect to multiple observation time windows. These features are related to Data Link Layer framebehaviors and the mobility of stations. We have successfully designed and implemented A Wireless Self Protection System (WSPS) that has the following significant features: it monitors wireless networks, generates network features, tracks wireless-network-state machine violations, generates wireless network flows (WNetFlows) for multiple time windows, and uses the dynamically updated anomaly and misuse rules to detect complex known and unknown wireless attacks and take appropriate proactive actions. To evaluate the performance of WSPS and compare it with other wireless intrusion detection systems, we present an evaluation approach that uses different metrics such as adaptability, scalability, accuracy, overhead, and latency.We validate the WSPS approach by experimenting with normal traffic and more than 20 different types of wireless attacks; and compare the WSPS performance with several well-known intrusion protection systems. Our experimental results show that the WSPS approach can protect from wireless network attacks with an average detection rate of 99.13% for all the experimented attacks.

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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

The Global Positioning System (GPS) is the most popular positioning system among some operational Global Navigation Satellite Systems (GNSS). However, GNSS suffer from accuracy deterioration and interruption of services in dense urban areas and are almost unavailable indoors. Although high-sensitivity receivers improve signal acquisition indoors, multipath is still be a challenging problem that affects accuracy especially indoors where a direct line of sight between transmitter and receiver almost never exist. Moreover, the wireless signal features are significantly jeopardized by obstacles and constructions indoors. To address these challenges, this research came in the context of proposing an alternative positioning system that is designed for GPS-denied environment and especially for indoors. Cramer-Rao Lower-Bound (CRLB) analysis was used to estimate the lower bound accuracy of different positioning methods indoors. Based on CRLB analysis, this research approached the wireless positioning problem indoors utilizing received signal strength (RSS) to achieve the following: 1) Developing new estimation methods to model the wireless RSS patterns in indoors. 2) Designing adaptive RSS-based wireless positioning methods for indoors. 3) Establishing a consistent framework for indoor wireless positioning systems. 4) Developing new methods to integrate inertial/odometer-based navigation systems with the developed wireless positioning methods for further improvements. The theoretical basis of the work was built on nonlinear stochastic estimation techniques including Particle Filtering, Gaussian Process Regression, Fast Orthogonal Search, Least-Squares, and Radial Basis Functions Neural Networks. All the proposed wireless positioning methods were developed and physically realized on Android-based smart-phones using the IEEE 802.11 WLANs (WiFi). In addition, successful integration with inertial/odometer sensors of mobile robots has been performed on embedded systems. Both theoretical analysis and experimental results showed significant improvements in modeling RSS indoors dynamically without offline training achieving a positioning accuracy of 1-3 meters. Sub-meter accuracy was achieved via integration with inertial/odometer sensors. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-03-25 16:11:59.518