Theses and Dissertations from UMD
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Item Immersive Visual Analytics of Wi-Fi Signal Propagation and Network Health(2023) Rowden, Alexander R; Varsnhney, Amitabh; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)e are immersed in waves of information. This information is typically transmitted as radio waves in many protocols and frequencies, such as WiFi, Bluetooth, and Near-Field Communications (NFC). It carries vital information such as health data, private messages, and financial records. There is a critical need for systematic and comprehensive visualization techniques to facilitate seamless, resilient, and secure transmission of these signals. Traditional visualization techniques are not enough because of the scale of these datasets. In this dissertation, we present three novel contributions that leverage advances in volume rendering and virtual reality (VR): (a) an outdoor volume-rendering visualization system that facilitates large-scale visualization of radio waves over a college campus through real-time programmable customization for analysis purposes, (b) an indoor, building-scale visualization system that enables data to be collected and analyzed without occluding the user's view of the environment, and (c) a systematic user study with 32 participants which shows that users perform analysis tasks well with our novel visualizations. In our outdoor system, we present the Programmable Transfer Function. Programmable Transfer Functions offer the user a way to replace the traditional transfer function paradigm with a more flexible and less memory-demanding alternative. Our work on indoor WiFi visualization is called WaveRider. WaveRider is our contribution to indoor-modeled WiFi visualization using a virtual environment. WaveRider was designed with the help of expert signal engineers we interviewed to determine the needs of the visualization and who we used to evaluate the application. These works provide a solid starting point for signal visualization as our networks transition to more complex models. Indoor and outdoor visualizations are not the only dichotomy in the realm of signal visualization. We are also interested in visualizations of modeled data compared to visualization of data samples. We have also explored designs for multiple sample-based visualizations and conducted a formal evaluation where we compare these to our previous model-based approach. This analysis has shown that visualizing the data without modeling improves user confidence in their analyses. In the future, we hope to explore how these sample-based methods allow more routers to be visualized at once.Item RADIO ANALYTICS FOR HUMAN ACTIVITY MONITORING AND INDOOR TRACKING(2018) Zhang, Feng; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the rapid development of the Internet of Things (IoT), wireless sensing has found wide applications from wellbeing monitoring, activity recognition, to indoor tracking. In this dissertation, we investigate the problem of wireless sensing for IoT applications using only ambient radio signals, e.g., WiFi, LTE, and 5G. In particular, our work mainly focuses on passive speed estimation, motion detection, sleep monitoring, and indoor tracking for wireless sensing. In this dissertation, we first study the problem of indoor speed estimation using WiFi channel state information (CSI). We develop the statistical electromagnetic (EM) wave theory for wireless sensing and establish a link between the autocorrelation function (ACF) of the physical layer CSI and the speed of a moving object. Based on the developed statistical EM wave theory for wireless sensing, we propose a universal low-complexity indoor speed estimation system leveraging CSI, which can work in both device-free and device-based situations. The proposed speed estimator differs from the other schemes requiring strong line-of-sight conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, it saves the users' efforts from large-scale training and fine-tuning of system parameters. The proposed speed estimator can enable many IoT applications, e.g., gait monitoring, fall detection, and activity recognition. Then, we also study the problem of indoor motion detection using CSI. The statistical behaviors of the CSI dynamics when motion presents can be characterized by the developed statistical EM theory for wireless sensing. We formulate the motion detection problem as a hypothesis testing problem and also derive the relationship between the detection rate and false alarm rate for motion detection, which is independent of locations, environments and motion types. Thus, the proposed motion detection system can work in most indoor environments, without any scenario-tailored training efforts. Extensive experiments conducted in several facilities show that the proposed system can achieve better detection performance compared to the existing CSI-based motion detection systems while maintaining a much larger coverage and a much lower false alarm rate. This dissertation also focuses on sleep monitoring using CSI. First, we build a statistical model for maximizing the signal-to-noise (SNR) ratio of breathing signal, which accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. Our results demonstrate that the proposed breathing estimator yields a median absolute error of 0.47 bpm and a 95%-tile error of only 2.92 bpm for breathing estimation, and detects breathing robustly even when a person is 10m away from the WiFi link, or behind a wall. Then, we apply machine learning algorithms on the extracted features from the estimated breathing rates to classify different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. Experimental results show that the proposed sleep monitoring system achieves sleep staging accuracy of 88%, outperforming advanced solutions using contact sensor or radar. The last work of this dissertation considers the problem of indoor tracking using CSI. First, we leverage a stationary and location-independent property of the time-reversal (TR) focusing effect of radio signals for highly accurate moving distance estimation, which plays a key role in the proposed indoor tracking system. Together with the direction estimation based on inertial measurement unit and location correction using the constraints from the floorplan, the proposed indoor tracking system is shown to be able to track a moving object with decimeter-level accuracy in different environments.Item Radio Analytics for Indoor Localization and Vital Sign Monitoring(2017) Chen, Chen; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Radio technology has been widely used for high-speed wireless communications. In the near future, radio technology would provide sensing capabilities to enable a diversified indoor applications in the era of Internet of Things (IoT). This is because that the electromagnetic (EM) wave, emitted from the transmitter propagates through multipath before arriving at the receiver, is varied by the environmental perturbations. Such variations in EM waves reveal important environmental changes useful for IoT applications. Thus, in IoT networks, radios are not only the ubiquitous communication interfaces but also exhibit augmented sensing potential. Despite the wide variety of IoT devices, most of them are equipped with WiFi which is a very mature and cost-effective connectivity solution and has evolved significantly ever since its standardization. Meanwhile, as people are spending more and more time indoors, most indoor spaces have been already equipped with WiFi infrastructures, which makes the IoT devices empowered by WiFi to blend into the existing WiFi infrastructures without efforts. Therefore, it is highly valuable to adopt radio analytics to analyze the WiFi radio signals to facilitate key IoT applications. In this dissertation, we explore the viability of using WiFi for two important IoT applications: indoor localization and vital sign monitoring. In the first part, we propose two indoor localization systems (IPSs) leveraging the time-reversal (TR) technique on off-the-shelf WiFi devices. The proposed IPSs utilize the location-specific features, i.e., the channel frequency response (CFR), which is a fine-grained information readily available on off-the-shelf devices that depicts the propagation of EM waves from the transmitter to different locations. The proposed IPSs consist of an offline phase which collects CFRs from locations-of-interest, and an online phase which compares the instantaneous CFRs with those captured in the offline phase. To calculate the similarities among locations, the TR focusing effect is evaluated quantitively between each pair of CFRs associated with these locations using the TR resonating strength (TRRS). Realizing that the bandwidth limit on mainstream WiFi devices could lead to location ambiguity, we exploit two diversities inherent in WiFi devices, i.e., frequency diversity and spatial diversity, to expand the effective bandwidth. Extensive experiments show a localization accuracy of 1 to 2 centimeters even under strong non-line-of-sight (NLOS) conditions as well as enhanced robustness against environmental dynamics. In the second part, we investigate the feasibility of high accuracy vital sign monitoring using CFRs. First of all, we present a highly accurate breathing monitoring system. Realizing that breathing injects tiny but periodic signals into the WiFi signal, we project the CFR time series onto the TRRS feature space to amplify such CFR perturbations. Integrated with machine learning techniques, the proposed scheme could distinguish breathing rates associated with different people. In addition, it could detect the presence of breathing and count the number of people. The performance is demonstrated by extensive experiments in multiple environments. Secondly, we present a lightweight vital sign monitoring solution with a much reduced computational complexity. Moreover, we supplement the proposed vital sign monitoring system with a finite state machine (FSM) to remedy the impact of motions on the monitoring performance. Extensive experimental results demonstrate the excellent performance of both breathing monitoring schemes.Item Connectivity and Data Transmission over Wireless Mobile Systems(2011) Frangiadakis, Nikolaos; Roussopoulos, Nick; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We live in a world where wireless connectivity is pervasive and becomes ubiquitous. Numerous devices with varying capabilities and multiple interfaces are surrounding us. Most home users use Wi-Fi routers, whereas a large portion of human inhabited land is covered by cellular networks. As the number of these devices, and the services they provide, increase, our needs in bandwidth and interoperability are also augmented. Although deploying additional infrastructure and future protocols may alleviate these problems, efficient use of the available resources is important. We are interested in the problem of identifying the properties of a system able to operate using multiple interfaces, take advantage of user locations, identify the users that should be involved in the routing, and setup a mechanism for information dissemination. The challenges we need to overcome arise from network complexity and heterogeneousness, as well as the fact that they have no single owner or manager. In this thesis I focus on two cases, namely that of utilizing "in-situ" WiFi Access Points to enhance the connections of mobile users, and that of establishing "Virtual Access Points" in locations where there is no fixed roadside equipment available. Both environments have attracted interest for numerous related works. In the first case the main effort is to take advantage of the available bandwidth, while in the second to provide delay tolerant connectivity, possibly in the face of disasters. Our main contribution is to utilize a database to store user locations in the system, and to provide ways to use that information to improve system effectiveness. This feature allows our system to remain effective in specific scenarios and tests, where other approaches fail.