Digital Communications and Networking Commons^™

Ecodrone: Autonomous Environmental Monitoring, Belsen Lee

Student Scholar Symposium Abstracts and Posters

This project presents EcoDrone, an autonomous aerial drone designed for continuous and automated environmental monitoring. Current environmental monitoring methods rely on stationary sensors or manual data collection, limiting real-time response capabilities. This reliance leads to delayed, incomplete, and spatially limited data and restricts the ability to capture real-time changes. Another challenge includes the difficulty of environmental monitoring in challenging terrain, whether it be wildfire areas, dense forestry, or mountainous terrain. EcoDrone overcomes these challenges by autonomously navigating difficult terrain to collect real-time data, offering more flexible and timely monitoring than stationary or manual methods. The central research question investigates integrating …

Ddos Detection And Mitigation Using Multiple Programmable Switches For Industrial Network Security, Seth Howard

Electrical Engineering and Computer Science Undergraduate Honors Theses

Given the exponential growth in our infrastructure’s reliance on digital systems and large interconnected networks to operate, cybersecurity efforts have not been able to keep up with vulnerabilities attackers can exploit. Specifically, their reliance on industrial network protocols in Supervisory Control and Data Acquisition (SCADA) systems. These protocols are great in terms of the functionality they provide, but are severely lacking in terms of security, leaving the networks vulnerable to cyberattacks. One of the more common protocols, Distributed Network Protocol 3 (DNP3), is particularly vulnerable to Denial-of-Service type attacks. In this work we explore the use of P4-based programmable networks …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.

Towards A Configurable Platform For The Design And Evaluation Of P4 Network Systems And Applications, Joseph B. Wilkin

Electrical Engineering and Computer Science Undergraduate Honors Theses

Due to the difficulties of running experiments on production networks, researchers often use platforms such as network emulators and testbeds to test their applications before applying them to the real world. There exists a need for a networking platform of this sort designed specifically for running P4 code and for evaluating P4-based experiments. In this work, we design and develop such a platform for use within our lab group based on the Mininet network emulator and its corresponding graphical user interface, MiniEdit. We have successfully integrated the BMv2 software switch into MiniEdit to allow users to build topologies for running …

Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar

Master's Theses

The development of electric Vertical Take-Off and Landing (eVTOL) drones signifies a substantial advancement in urban air mobility, ready to transform transportation models in densely populated regions. These advanced drones, distinguished by their capacity to function in limited spaces and their minimized environmental impact, are set to transform individual, shipping, emergency services, and public safety activities. Nonetheless, like any transformational technology, the implementation of eVTOL systems presents many challenges, especially in the realm of cybersecurity. Adding many devices and entities to an eVTOL network increases the risk of privacy and security attacks. This paper proposes a key-based authentication scheme that …

Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …

Lightlink: Visible Light Communication For Batteryless Devices With Variable Computational Load, Charles Phillips

All Theses

Visible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables.

Blockchain-Integrated Version Control For Secure And Transparent Software Supply Chains, Iwinosa W. Aideyan

All Theses

The software supply chain encompasses all stages of software development and delivery from initial coding and version control to integration and deployment. As development environments become increasingly distributed and reliant on external dependencies, ensuring the integrity, auditability, and consistency of code changes has become a pressing challenge. Traditional version control systems like Git, while effective for collaboration and tracking revisions, do not inherently provide tamper-evident commit histories. Features such as history rewriting (e.g., git rebase, git push --force) can be exploited to manipulate commit logs without detection, posing risks in security-sensitive domains. This thesis proposes a blockchain-integrated version control framework …

Privacy Implications Of Data Collection In Android Automotive Os, Bulut Gözübüyük

All Theses

Modern vehicles have become sophisticated computational and sensor systems, as evidenced by advanced driver assistance systems (ADAS), in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various onboard subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100 million vehicles and has become a dominant force in the in-vehicle infotainment (IVI) market. With this extensive data collection, privacy concerns have become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a …

Design Of A Teleoperabletendon-Driven Robotic Hand, Zachary Giese, Joseph Gober, Samuel Grisham, Avery Mahan, Aspen White, Brayden May, Aspen White

ATU Scholars Symposium

This work presents the design, implementation, and validation of a 3D-printed tendon-driven robotic hand with a bi-directional wireless control system. The robotic hand emulates human anatomical principles, focusing on replicating flexion and extension mechanisms of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints via a tendon-pulley system. Iterative prototyping resolved initial challenges in joint stiffness, tendon routing, and servo interference, culminating in a design where a single servo drives both flexion and extension for each joint using a bidirectional tendon control mechanism. A reliable bidirectional wireless communication framework, using Arduino Nano Every microcontrollers and NRF24L01 modules, enables …

The Hidden Cost Of Intelligence: Environmental Impacts And The Rise Of Green Ai Solutions, Hayin Tamut

ATU Scholars Symposium

Artificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.

Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted …

An Empirical Evaluation Of Communication Technologies And Quality Of Delivery Measurement In Networked Microgrids, Ruairí De Fréin, Yasin Emir Kutlu

Articles

Networked microgrids (NMG) are gaining popularity as an example of smartgrids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware such as Cisco 4331 Integrated Services Routers (ISR). …

Why Do Different Llms Give Different Answers To The Same Question? Model Uncertainty And Variability In Llm-Based Intrusion Detection Systems Ranking, Charlise Calloway

Cybersecurity Undergraduate Research Showcase

Large Language Models (LLMs) are increasingly applied across business, education, and cybersecurity domains. However, LLMs can yield varied outputs for the same query due to differences in architecture, training data, and response generation mechanisms. This paper examines model variability and uncertainty by comparing the responses of three LLMs—ChatGPT-4o, Gemini 2.0 Flash, and DeepSeek-V3--to a query on ranking practical intrusion detection systems (IDS). The analysis highlights key similarities and differences in the models’ outputs, offering insight into their respective reasoning and consistency.

Evaluating Hyper-V Vs Proxmox: Performance Comparison For Virtualization*, Ivan Vakal, Edwin Regalado

Campus Research Month

Virtual environments play a significant role in the IT industry, with many companies relying on this technology. With VMware’s increasing licensing costs following its acquisition by Broadcom, many businesses are seeking alternative virtualization solutions. This study evaluates the performance of Proxmox and Hyper-V by implementing a three-node high-availability cluster for each platform and conducting benchmarking tests on CPU performance, storage efficiency, and network throughput. Our results indicate that Hyper-V performs better with Windows-based virtual machines, while Proxmox demonstrates superior performance with Linux-based workloads. Additionally, Proxmox offers a more user-friendly cluster setup, whereas Hyper-V requires greater technical expertise.

Comparing Ai And Human Self-Assessments In Memorization Performance*, Meg Ermer, Abishur Moses-Pakkianathan

Campus Research Month

Many students in higher education use flashcard applications for learning large amounts of information in limited amounts of time. Many of these applications rely on spaced-repetition algorithms for memorization, which are proven to be more efficient than traditional study methods. We compared the effects of studying with a spaced-repetition application that utilizes a NLU model to calculate a user's understanding of material against the effects of studying with a spaced-repetition model that did not use NLU. We used our results to determine if replacing the self-assessment component of flashcard studying applications with a NLU model led to better memorization and …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

Every Relevant Detail, Salomé Viljoen

Michigan Law Review

A review of The Ordinal Society. By Marion Fourcade and Kieran Healy.

Aimr-Brainstorm: Ai-Enhanced Interactive Mixed Reality For Collaborative Ideation, Yuchuan Ma

Dartmouth College Master’s Theses

This study investigates the potential of an AI-enhanced Mixed Reality (MR) brainstorming system, named AIMR-Brainstorm, in comparison to traditional sticky notes for creative ideation. By integrating real-time idea extraction through ChatGPT with immersive, physics-based visualizations, the system aims to transform analog brainstorming workflows into dynamic, interactive digital experiences. Using a within-subject experimental design, 30 participants engaged in paired brainstorming sessions with both AIMR and sticky notes. Quantitative measures of efficiency, engagement, creativity, and user satisfaction were collected through between-session and post-study surveys, while qualitative feedback provided additional insights into user experiences. Conclusively, while traditional sticky notes were generally preferred for …

Comparative Performance Analysis Of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study On Faas And Iaas Platforms Dataset, Jeremiah Webb

Doctoral Dissertations and Master's Theses

Cloud computing has become a relatively new paradigm for the delivery of compute resources, with key management services (KMS) playing a crucial role in securely handling cryptographic operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, including SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryption/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction with KMS offerings from Ama- zon Web Services (AWS) and Microsoft Azure to conduct a comparative performance analysis. The methodology involves the AWS Cloud Development Kit (CDK) and the Bicep language to deploy AWS Lambda Functions and Azure Functions, respectively, to …

From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose: The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program. Design/methodology/approach: This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former …