Index Catalog // ScholarsArchive@OSU

Delay-Aware Resource Management Frameworks for Intelligent Vehicular Networks

Creator:: Almasari, Shahad A.
Abstract:: Autonomous vehicles bring great societal benefits but also potential impact and disruption to road safety, traffic congestion, and driving behaviors. One important technology that is indispensable to the success of such systems is vehicular networks. Vehicular networks provide the backbone for ensuring communication and connectivity among vehicles, all crucial to...
Resource Type:: Masters Thesis
Full Text:: Fog. Figure 3.1 shows the following types of communications between nodes: • V2V communication, such

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Wireless Device Identification

Creator:: Basha, Nora
Abstract:: Enabling accurate and automated identification of wireless devices is critical for ensuring secure and authenticated data communication in large-scale networks such as IoT networks. In the aim of devising practical identification techniques that are immune to spoofing, hardware-driven RF fingerprinting using deep neural networks, which leverages the inevitable presence of...
Resource Type:: Masters Thesis
Full Text:: for ensuring secure and authenticated data communication in large-scale networks such as IoT

Network Traffic-Driven Hybrid Learning for Classifying Seen and Unseen IoT Device Types

Creator:: Bao, Jiaqi
Abstract:: IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks,...
Resource Type:: Masters Thesis
Full Text:: - jects, embedded with sensor, actuator, computation, storage and communication com- ponents, that are

Source-Routed Multicast Schemes for Large-Scale Cloud Data Center Networks

Creator:: Alqahtani, Jarallah Ali m
Abstract:: Data centers (DCs) have been witnessing unprecedented growth in size, number and complexity in recent years. They consist of tens of thousands of servers interconnected by fast network switches, hosting and enabling numerous applications with various traffic characteristics and requirements. As a result, DC networks have been presented with several...
Resource Type:: Dissertation
Full Text:: Hamdaoui In Proceedings of IEEE International Global Communications Conference, Dec. 2018 11 Chapter 2

Deep Learning Based Automatic Modulation Classication for Wideband Access Using Cyclostationarity Analysis

Creator:: Mejri, Seifeddine
Abstract:: RF-based signal identification and classification has received growing attention during recent years due to its potential use in many application domains. Of particular interest is Automatic Modulation Classification (AMC), which has been useful in addressing various spectrum related challenges such as signal jamming, policy enforcement, and spectrum sharing. Adopting AMC...
Resource Type:: Capstone Project
Full Text:: , AMC has been an important topic in wireless communication due to the prospective use of

Leveraging Hardware-Impaired Out-of-Band Information Through Deep Neural Networks for Robust Wireless Device Classification

Creator:: Elmaghbub, Abdurrahman A.
Abstract:: Wireless device classification techniques play a key role in promoting emerging wireless applications such as allowing spectrum regulatory agencies to enforce their access policies and enabling network administrators to control access and prevent impersonation attacks to their wireless networks. Leveraging spectrum distortions of transmitted RF signals, caused by transceiver hardware...
Resource Type:: Masters Thesis
Full Text:: MATLAB’s Communications toolbox is used to design a simulation model of full wire- less communications

On the domain generalizability of RF fingerprints through multifractal dimension representation

Creator:: Johnson, Benjamin
Abstract:: RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data from one domain performs badly when tested on data from a...
Resource Type:: Masters Thesis
Full Text:: generation,” IEEE Wireless Communications, vol. 26, no. 5, pp. 92–98, 2019. [2] O. M. Gul, M

Towards Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting

Creator:: Gaskin, Jared
Abstract:: In recent years, RF (Radio Frequency) device fingerprinting using deep learning has emerged as a method of identifying devices solely by their RF transmissions. Conventional approaches to this type of device fingerprinting are not portable to different domains. That is, if a model for this purpose is trained on data...
Resource Type:: Masters Thesis
Full Text:: security that is of great importance is authentication. If a communication system ensures authentication

Evaluating the Efficiency, Capabilities, and Scalability of a LoRaWAN Network in a Fenceless Grazing Implementation

Creator:: Alder, Ryan
Abstract:: LoRaWAN networks are becoming more popular, and it is becoming common for developers to look at solutions utilizing Internet of Things concepts. In this paper, I introduce a Fenceless Grazing System utilizing the LoRaWAN network stack and discuss the limitations of this theorized network to better understand the scalability prior...
Resource Type:: Honors College Thesis
Full Text:: grazing area, communication frequency, and more by using NS-3 to estimate the average values of these

ScholarsArchive@OSU

Delay-Aware Resource Management Frameworks for Intelligent Vehicular Networks

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Wireless Device Identification

Network Traffic-Driven Hybrid Learning for Classifying Seen and Unseen IoT Device Types

Source-Routed Multicast Schemes for Large-Scale Cloud Data Center Networks

Deep Learning Based Automatic Modulation Classication for Wideband Access Using Cyclostationarity Analysis

Leveraging Hardware-Impaired Out-of-Band Information Through Deep Neural Networks for Robust Wireless Device Classification

On the domain generalizability of RF fingerprints through multifractal dimension representation

Towards Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting

Evaluating the Efficiency, Capabilities, and Scalability of a LoRaWAN Network in a Fenceless Grazing Implementation

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