Smart antenna technique has emerged as one of the leading technologies for enhancing the quality of service in wireless networks. Because of its ability to concentrate transmit power in desired directions, it has been widely adopted by academia and industry to achieve better coverage, improved capacity and spectrum efficiency of wireless communication systems. In spite of its popularity in applications of performance enhancement, the smart antenna's capability of improving wireless network security is relatively less explored. This dissertation focuses on exploiting the smart antenna technology to develop physical layer solutions to anti-eavesdropping and location security problems. We first investigate the problem of enhancing wireless communication privacy. A novel scheme named "artificial fading" is proposed, which leverages the beam switching capability of smart antennas to prevent eavesdropping attacks. We introduce the optimization strategy to design a pair of switched beam patterns that both have high directional gain to the intended receiver. Meanwhile, in all the other directions, the overlap between these two patterns is minimized. The transmitter switches between the two patterns at a high frequency. In this way, the signal to unintended directions experiences severe fading and the eavesdropper cannot decode it. We use simulation experiments to show that the artificial fading outperforms single pattern beamforming in reducing the unnecessary coverage area of the wireless transmitter. We then study the impact of beamforming technique on wireless localization systems from the perspectives of both location privacy protection and location spoofing attack. For the location privacy preservation scheme, we assume that the adversary uses received signal strength (RSS) based localization systems to localize network users in Wireless LAN (WLAN). The purpose of the scheme is to make the adversary unable to uniquely localize the user when possible, and otherwise, maximize error of the adversary's localization results. To this end, we design a two-step scheme to optimize the beamforming pattern of the wireless user's smart antenna. First, the user moves around to estimate the locations of surrounding access points (APs). Then based on the locations of the APs, pattern synthesis is optimized to minimize the number of APs in the coverage area and degenerate the localization precision. Simulation results show that our scheme can significantly lower the chance of being localized by adversaries and also degrade the location estimation precision to as low as the coverage range of the AP that the wireless user is connected to. As personal privacy preservation and security assurance at the system level are always conflictive to some extent, the capability of smart antenna to intentionally bias the RSS measurements of the localization system also potentially enables location spoofing attacks. From this aspect, we present theoretical analysis on the feasibility of beamforming-based perfect location spoofing (PLS) attacks, where the attacker spoofs to a target fake location by carefully choosing the beamforming pattern to fool the location system. The PLS problem is formulated as a nonlinear feasibility problem, and due to its intractable nature, we solve it using semidefinite relaxation (SDR) in conjunction with a heuristic local search algorithm. Simulation results show the effectiveness of our analytical approach and indicate the correlation between the geometry of anchor deployment and the feasibility of PLS attacks. Based on the simulation results, guidelines for guard against PLS attacks are provided. / Ph. D.

This work deals with implementation of an agent platform, which is able to run agent code in wireless sensor networks. Implementation has been done for MICAz platform, which uses TinyOS operating system for developing applications. This work contains list of chosen TinyOS parts and illustrates, how such a platform can be used for our purposes. We will describe main features of ALLL language and we will also demonstrate some examples of agents.

Traditional wireless site surveys produce a heat-map of link strength or quality over a target area, usually on the ground plane. In recent years research has gone into using aerial drones in network attack and surveillance, making three dimensional awareness of wireless coverage areas of interest. A multi-rotor drone and data collection module were built and tested as part of this research. Site assessments were conducted both in open space and near structures. Collected data was interpolated across the target area, and visualized as points and contours. These visualizations were exported to a Keyhole Markup Language (KML) for visualization in context. Resulting visualizations proved to be beneficial in identifying the coverage area of both authorized and rogue access points.

In the case of an emergency in an underground facility, the harsh environment make the rescue missions a difficult and taxing task for the first responders. Disorientation, stress and lack of communication are fatal in that territory. In order to overcome all this difficulties and provide a system to coordinate and help locate emergency responders, a new Indoor Positioning System (IPS) is proposed. The system shall be scalable so it can expand its coverage over the site, it would adapt and remain reliable in the harsh conditions of the environment. The main goal of this project is to present an analysis of the current wireless technologies, their advantages and disadvantages and a comparison between them. Build a new solution and present the results of the performance of the network. The tests recreate the characteristics of the underground territory and present a good analysis of the system. This thesis project report the process, to build a scalable, adaptable and reliable wireless network to be used as the framework of a positioning system. The system is constructed using the ZigBee protocol stack and the nRF52840 hardware. A graphical user interface is developed to facilitate the configuration of the network. At the end the results gives proof that the system can be used in the underground facilities as long as the network is deployed carefully. / Vid en nödsituation i en underjordisk anläggning, på grund av den svåra miljön, räddningsuppdragen blir en svår och beskattande uppgift för de första svararna. Desorientering, stress och brist på kommunikation är dödliga inom det området. För att övervinna alla dessa svårigheter och tillhandahålla ett system för att samordna och hjälpa till att hitta nödlägen, föreslås ett nytt inomhuspositioneringssystem (IPS). Systemet ska vara skalbart så att det kan utöka sin täckning över webbplatsen, det skulle anpassa sig och förbli tillförlitligt under de svåra miljöerna. Detta arbete har huvudmål att presentera en analys av den nuvarande trådlösa tekniken, deras fördelar och nackdelar och en jämförelse mellan dem samtidigt bygga en ny lösning och presentera resultaten av nätverkets prestanda. Testen modellerar egenskaperna hos det underjordiska territoriet och presenterar en bra analys av systemet. Detta arbete rapporterar processen för att bygga ett skalbart, anpassningsbart och pålitligt trådlöst nätverk som ska användas som ett ramverk för ett positioneringssystem. Systemet är konstruerat med ZigBee-protokollstacken och hårdvaran nRF52840. Ett grafiskt användargränssnitt utvecklas för att underlätta konfigurationen av nätverket. Resultatet bevisar att systemet kan användas vid de underjordiska anläggningarna så länge nätverket distribueras noggrant.

This dissertation employed a two prong approach, whereby the survey and case study methods were used to investigate security issues regarding wireless networks. The survey portion draws together two previously unrelated research streams. Given the recent increased concern for security in the computing milieu, Innovation Diffusion Theory and security factor constructs were merged and synthesized to form a new instrument. This instrument is useful in an effort to understand what role security concerns play in the adoption and diffusion of technology. In development of the new instrument, 481 usable surveys were collected and analyzed. Factor analysis revealed favorable factor loadings in the data. Further analysis was then conducted utilizing multiple regression analysis. This analysis led to the discovery that the constructs of Susceptibility and Severity of Threat, Improvement Potential, and Visibility are significant predictors in regard to level of concern when using wireless networks. Case studies were conducted with a goal to gain a deep knowledge of IT professionals? concerns, attitudes, and best practices toward wireless security. To this end, seven IT professionals were personally interviewed regarding their perceptions and attitudes toward wireless security. In an effort to compare IT professional and end user opinions, 30 IT professionals also completed a paper based survey regarding their perceptions about security. Findings indicate that security professionals are very optimistic for the future of wireless computing. However, that optimism is tempered by a realization that there are a myriad of potential threats that might exploit weakness in wireless security. To determine differences and similarities between users? perspectives and managers? perspectives regarding wireless network security, the results from the survey and case study were synthesized. Most IT professionals (76.19%) reported that, all factors considered, they prefer to use wired networks as opposed to wireless networks; whereas, substantially fewer (44.86%) of the end user respondents reported that they preferred wired over wireless networks. Overall, results suggest that IT professionals are more concerned about security than are end users. However, a challenge remains to make administrators and users aware of the full effect of security threats present in the wireless computing paradigm.

Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges.

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Resource allocation in modern wireless networks is constrained by increasingly stringent real-time requirements. Such real-time requirements typically come from, among others, the short coherence time on a wireless channel, the small time resolution for resource allocation in OFDM-based radio frame structure, or the low-latency requirements from delay-sensitive applications. An optimal resource allocation solution is useful only if it can be determined and applied to the network entities within its expected time. For today's wireless networks such as 5G NR, such expected time (or real-time requirement) can be as low as 1 ms or even 100 μs. Most of the existing resource optimization solutions to wireless networks do not explicitly take real-time requirement as a constraint when developing solutions. In fact, the mainstream of research works relies on the asymptotic complexity analysis for designing solution algorithms. Asymptotic complexity analysis is only concerned with the growth of its computational complexity as the input size increases (as in the big-O notation). It cannot capture the real-time requirement that is measured in wall-clock time. As a result, existing approaches such as exact or approximate optimization techniques from operations research are usually not useful in wireless networks in the field. Similarly, many problem-specific heuristic solutions with polynomial-time asymptotic complexities may suffer from a similar fate, if their complexities are not tested in actual wall-clock time. To address the limitations of existing approaches, this dissertation presents novel real- time solution designs to two types of optimization problems in wireless networks: i) problems that have closed-form mathematical models, and ii) problems that cannot be modeled in closed-form. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, which breaks an original optimization problem into a large number of small and independent sub-problems, (ii) search intensification, which identifies the most promising problem sub-space and selects a small set of sub-problems to match the available GPU processing cores, and (iii) GPU-based large-scale parallel processing, which solves the selected sub-problems in parallel and finds a near-optimal solution to the original problem. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi in the unlicensed spectrum. The real-time requirement for finding the optimal channel division and LTE resource allocation solution is on 1 ms time scale. This problem involves the optimal division of transmission time for LTE and Wi-Fi across multi- ple unlicensed bands, and the resource allocation among LTE users within the LTE's "ON" periods. We formulate this optimization problem as a mixed-integer linear pro- gram and prove its NP-hardness. Then by exploiting the unique problem structure, we propose a real-time solution design that is based on problem decomposition and GPU-based parallel processing techniques. Results from an implementation on the NVIDIA GPU/CUDA platform demonstrate that the proposed solution can achieve near-optimal objective and meet the 1 ms timing requirement in 4G LTE. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal (with respect to the PF objective) resource allocation and MCS selection solution is 125 μs (under 5G numerology 3). In this problem, we need to allocate frequency-time resource blocks on an operating channel and assign modulation and coding scheme (MCS) for each active user in the cell. We present GPF+ — a GPU based real-time PF scheduler. With GPF+, the original PF optimization problem is decomposed into a large number of small and in- dependent sub-problems. We then employ a cross-entropy based search intensification technique to identify the most promising problem sub-space and select a small set of sub-problems to fit into a GPU. After solving the selected sub-problems in parallel using GPU cores, we find the best sub-problem solution and use it as the final scheduling solution. Evaluation results show that GPF+ is able to provide near-optimal PF performance in a 5G cell while meeting the 125 μs real-time requirement. For the second type of problems, where there is no closed-form mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under DL/DRL, we employ deep function approximators (neural networks) to learn the unknown objective function of an optimization problem, approximate an optimal algorithm to find resource allocation solutions, or discover important mapping functions related to the resource optimization. To meet the real-time requirement, we propose to augment DL or DRL methods with optimization techniques at the input or output of the deep function approximators to reduce their complexities and computational time. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms (under 5G numerology 0). A major challenge in solving this problem is that it cannot be modeled using closed-form mathematical expressions. To address this issue, we develop a model-free DRL approach which employs a deep neural network to learn an optimal algorithm to allocate the URLLC puncturing over the operating channel, with the objective of minimizing the adverse impact from URLLC traffic on eMBB. Our contributions include a novel learning method that exploits the intrinsic properties of the URLLC puncturing optimization problem to achieve a fast and stable learning convergence, and a mechanism to ensure feasibility of the deep neural network's output puncturing solution. Experimental results demonstrate that our DRL-based solution significantly outperforms state-of-the-art algorithms proposed in the literature and meets the 1 ms real-time requirement for dynamic multiplexing. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs (under 5G numerology 3). The objective is to have eMBB meet a given block-error rate (BLER) target under the adverse impact of URLLC puncturing. Since this problem cannot be mathematically modeled in closed-form, we proposed a DL-based solution design that uses a deep neural network to learn and predict the BLERs of a transmission under each MCS level. Then based on the BLER predictions, an optimal MCS can be found for each transmission that can achieve the BLER target. To meet the 5G real-time requirement, we implement this design through a hybrid CPU and GPU architecture to minimize the execution time. Extensive experimental results show that our design can select optimal MCS under the impact of preemptive puncturing and meet the 125 μs timing requirement. / Doctor of Philosophy / In modern wireless networks such as 4G LTE and 5G NR, the optimal allocation of radio resources must be performed within a real-time requirement of 1 ms or even 100 μs time scale. Such a real-time requirement comes from the physical properties of wireless channels, the short time resolution for resource allocation defined in the wireless communication standards, and the low-latency requirement from delay-sensitive applications. Real-time requirement, although necessary for wireless networks in the field, has hardly been considered as a key constraint for solution design in the research community. Existing solutions in the literature mostly consider theoretical computational complexities, rather than actual computation time as measured by wall clock. To address the limitations of existing approaches, this dissertation presents real-time solution designs to two types of optimization problems in wireless networks: i) problems that have mathematical models, and ii) problems that cannot be modeled mathematically. For the first type of problems, we propose a novel approach that consists of (i) problem decomposition, (ii) search intensification, and (iii) GPU-based large-scale parallel processing techniques. The efficacy of this approach has been illustrated by our solutions to the following two problems. • Real-Time Scheduling to Achieve Fair LTE/Wi-Fi Coexistence: We investigate a resource optimization problem for the fair coexistence between LTE and Wi-Fi users in the same (unlicensed) spectrum. The real-time requirement for finding the optimal LTE resource allocation solution is on 1 ms time scale. • An Ultrafast GPU-based Proportional Fair Scheduler for 5G NR: We study the popular proportional-fair (PF) scheduling problem in a 5G NR environment. The real-time requirement for determining the optimal resource allocation and modulation and coding scheme (MCS) for each user is 125 μs. For the second type of problems, where there is no mathematical formulation, we propose to employ model-free deep learning (DL) or deep reinforcement learning (DRL) techniques along with judicious consideration of timing requirement throughout the design. Under this approach, we study the following two problems: • A DRL-based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR: We study the problem of dynamic multiplexing of eMBB and URLLC on the same channel through preemptive resource puncturing. The real-time requirement for determining the optimal URLLC puncturing solution is 1 ms. • A DL-based Link Adaptation for eMBB/URLLC Multiplexing in 5G NR: We investigate MCS selection for eMBB traffic under the impact of URLLC preemptive puncturing. The real-time requirement for determining the optimal MCSs for all eMBB transmissions scheduled in a transmission interval is 125 μs.