The goal of this study was to understand elementary school children’s perceptions of engineering. A total of 949 elementary school students were surveyed, individually or as a whole group, to examine gender and age differences in achievement-related beliefs (i.e., competency, interest, and importance) pertaining to engineering-related skills and activities. The results of this study found that specific skills and activities showed significant gender and age differences for each of the three measures. Significant findings showed that younger students (kindergarten through second grade) found many of the engineering-related skills and activities more interesting than the older students (third through fifth grade); however, the older students rated more of the skills and activities as being important. Gender differences showed that girls typically rated themselves as being more competent, more interested in, and valuing the skills and activities that pertained more to mindset ideas, such as learning from your mistakes and failures or not giving up, whereas boys rated themselves higher in more of the hands-on activities, such as building with things like legos, blocks, and k’nex.

Mobile devices have penetrated into every aspect of modern world. For one thing, they are becoming ubiquitous in daily life. For the other thing, they are storing more and more data, including sensitive data. Therefore, security and privacy of mobile devices are indispensable. This dissertation consists of five parts: two authentication schemes, two attacks, and one countermeasure related to security and privacy of mobile devices.

Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on the touchscreen of a mobile device. In Chapter 3, I focus on mobile app fingerprinting and propose a method based on analyzing the power profiles of targeted mobile devices. In Chapter 4, I mainly explore a novel liveness detection method for face authentication on mobile devices. In Chapter 5, I investigate a novel keystroke inference attack on mobile devices based on user eye movements. In Chapter 6, a novel authentication scheme is proposed, based on detecting a user’s finger gesture through acoustic sensing. In Chapter 7, I discuss the future work.

The Internet of Things (IoT) has become a more pervasive part of everyday life. IoT networks such as wireless sensor networks, depend greatly on the limiting unnecessary power consumption. As such, providing low-power, adaptable software can greatly improve network design. For streaming live video content, Wireless Video Sensor Network Platform compatible Dynamic Adaptive Streaming over HTTP (WVSNP-DASH) aims to revolutionize wireless segmented video streaming by providing a low-power, adaptable framework to compete with modern DASH players such as Moving Picture Experts Group (MPEG-DASH) and Apple’s Hypertext Transfer Protocol (HTTP) Live Streaming (HLS). Each segment is independently playable, and does not depend on a manifest file, resulting in greatly improved power performance. My work was to show that WVSNP-DASH is capable of further power savings at the level of the wireless sensor node itself if a native capture program is implemented at the camera sensor node. I created a native capture program in the C language that fulfills the name-based segmentation requirements of WVSNP-DASH. I present this program with intent to measure its power consumption on a hardware test-bed in future. To my knowledge, this is the first program to generate WVSNP-DASH playable video segments. The results show that our program could be utilized by WVSNP-DASH, but there are issues with the efficiency, so provided are an additional outline for further improvements.

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.

Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.

Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.

Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.

This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer.

The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol.

The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized.

The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA).

The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics.

While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints.

The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA).

Flexibility, reconfigurability and wearability technologies for antenna designs are presented, investigated and merged in this work. Prior to the design of these radiating elements, a study is conducted on several flexible substrates and how to fabricate flexible devices. Furthermore, the integration of active devices into the flexible substrates is also investigated. A new approach of designing inkjet-printed flexible reconfigurable antennas, based on the concept of printed slot elements, is proposed. An alternate technique to reconfigure the folded slot antenna is also reported. The proposed radiator works for both Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) applications. The flexible reconfigurable antenna is also redesigned to resonate at both (2.4/5.2 GHz) for WLAN devices and its Multiple-Input Multiple-Output (MIMO) configuration is reported. Two orthogonal elements are used to form the MIMO antenna system for better isolation.

The wearability of the proposed flexible reconfigurable radiator is also discussed. Since wearable antennas operate close to the human body, which is considered as a lossy tissue, an isolation between the radiating elements and human body is required to improve the radiation characteristics and to reduce the Specific Absorption Rate (SAR). The proposed antenna is redesigned on an Artificial Magnetic Conductor (AMC) surface that also functions as a ground plane to isolate the radiator from the human body. To examine its performance as a body-worn device, it is measured at different positions on the human body. Furthermore, simulations show that the SAR level is reduced when using the AMC surface. The proposed wearable antenna works for both Wireless Body Area Network (WBAN) and WiMAX body-worn wireless devices.

Electromagnetic bandgap (EBG) structures are used to suppress surface wave propagation in printed antennas. However, due to the presence of vias, not all of them can be utilized in flexible radiators. Thus, a Perforated High Impedance Surface (PHIS) is proposed which suppresses the surface waves without the need of vias, and it also serves as a ground plane for flexible antennas. The surface wave suppression and the antenna applications of the proposed PHIS surface are discussed.

The increasing presence and affordability of sensors provides the opportunity to make novel and creative designs for underserved markets like the legally blind. Here we explore how mathematical methods and device coordination can be utilized to improve the functionality of inexpensive proximity sensing electronics in order to create designs that are versatile, durable, low cost, and simple. Devices utilizing various acoustic and electromagnetic wave frequencies like ultrasonic rangefinders, radars, Lidar rangefinders, webcams, and infrared rangefinders and the concepts of Sensor Fusion, Frequency Modulated Continuous Wave radar, and Phased Arrays were explored. The effects of various factors on the propagation of different wave signals was also investigated. The devices selected to be incorporated into designs were the HB100 DRO Radar Doppler Sensor (as an FMCW radar), HC-SR04 Ultrasonic Sensor, and Maxbotix Ultrasonic Rangefinder \u2014 EZ3. Three designs were ultimately developed and dubbed the "Rad-Son Fusion", the "Tri-Beam Scanner", and the "Dual-Receiver Ranger". The "Rad-Son Fusion" employs the Sensor Fusion of an FMCW radar and Ultrasonic sensor through a weighted average of the distance reading from the two sensors. The "Tri-Beam Scanner" utilizes a beam-forming Digital Phased Array of ultrasonic sensors to scan its surroundings. The "Dual-Receiver Ranger" uses the convolved result from to two modified HC-SR04 sensors to determine the time of flight and ultimately an object's distance. After conducting hardware experiments to determine the feasibility of each design, the "Dual-Receiver Ranger" was prototyped and tested to demonstrate the potential of the concept. The designs were later compared based on proposed requirements and possible improvements and challenges associated with the designs are discussed.

Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.

Video capture, storage, and distribution in wireless video sensor networks

(WVSNs) critically depends on the resources of the nodes forming the sensor

networks. In the era of big data, Internet of Things (IoT), and distributed

demand and solutions, there is a need for multi-dimensional data to be part of

the Sensor Network data that is easily accessible and consumable by humanity as

well as machinery. Images and video are expected to become as ubiquitous as is

the scalar data in traditional sensor networks. The inception of video-streaming

over the Internet, heralded a relentless research for effective ways of

distributing video in a scalable and cost effective way. There has been novel

implementation attempts across several network layers. Due to the inherent

complications of backward compatibility and need for standardization across

network layers, there has been a refocused attention to address most of the

video distribution over the application layer. As a result, a few video

streaming solutions over the Hypertext Transfer Protocol (HTTP) have been

proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion

Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These

frameworks, do not address the typical and future WVSN use cases. A highly

flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)

are introduced. The platform's goal is to usher video as a data element that

can be integrated into traditional and non-Internet networks. A low cost,

scalable node is built from the ground up to be fully compatible with the

Internet of Things Machine to Machine (M2M) concept, as well as the ability to

be easily re-targeted to new applications in a short time. Flexi-WVSNP design

includes a multi-radio node, a middle-ware for sensor operation and

communication, a cross platform client facing data retriever/player framework,

scalable security as well as a cohesive but decoupled hardware and software

design.