Systems and Communications Commons^™

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

A Study Of 5g Cellular Connectivity To Unmanned Aerial Vehicles, Jackson Murrin

All Theses

The market of unmanned aerial vehicles (UAVs) has seen significant growth in the past ten years on both the commercial and military sides. The applications for UAVs are endless and options by manufacturers allow users to modify their drones for their specific goals. This industry has opened up the excitement of piloting vehicles in the air, photography, videography, exploration of nature from a different point of view and many other hobbies assisted by the emergence of UAVs. The growth of this industry coincides with the roll out of new 5G cellular network technology. This upgrade in cellular network infrastructure allows …

Analyzing The Influence Of Stale Data On Autonomous Intelligent Transportation Systems, August St. Louis

All Theses

Intelligent transportation has been at the forefront of recent technological advancement. Individuals have developed a number of algorithms intended to automate and improve essential intelligent transportation functions. New developments include the incorporation of vehicle platooning and path planning algorithms within a number of use cases. Data perturbation can affect both algorithms significantly. We define data perturbation as any natural or unnatural phenomenon that causes the data to be skewed in any way. Perturbations within either system can cause its respective algorithm to operate with stale or incorrect data. This can significantly affect performance. This paper conducts a fault injection campaign …

Optical Control System For Atmospheric Turbulence Mitigation, Martyn Lemon

All Theses

Propagation of laser light is distorted in the presence of atmospheric turbulence. This poses an issue for sensing, free-space optical communications, and transmission of power. With an ever-increasing demand for high-speed data communications, particularly between satellites, unmanned vehicles, and other systems that benefit from a point-to-point link, this issue is critical for the field. A variety of methods have been proposed to circumvent this issue. Some major categories include the manipulation of the light’s structure, an adaptive scheme at the optical receiver, scanning mirror systems, or a transmission of simultaneous signals with a goal to improve robustness.

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Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information, Dylan Kensler

All Theses

As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a …

An Evaluation Of Wi-Fi 802.11b Backscatter, Anthony Chen

All Theses

Internet of Things (IoT) devices are in need of low-power communications systems with longevity and reliability. With the use of backscatter technology, IoT devices can communicate at the cost of almost no power and can last for up to a decade. Furthermore, backscatter technology is compatible with everyday wireless signals such as Wi-Fi and Bluetooth, allowing for easy communication without specific hardware constraints. This thesis aims to evaluate a Wi-Fi backscatter system and analyze its ease in triggering off of such ambient signals and sources. The system will utilize Wi-Fi 802.11b as a backscatter source to trigger the backscatter system …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …