Masters Presentations Computer Information Systems Winter 2020 - PDF document

Master�s Presentations Computer Information Systems Winter 2020 Thursday, April 23, 2020 9:00�10:00a

School of Computing and Information Systems Master�s Presentations Computer Information Systems Thursday, April 23, 2020 CIS Thesis Proposals: 8:30 am – Livestream Link: Kevin Kredit – MS Thesis Proposal, Advisor: Dr. Andrew Kalafut “Risk - Based Approaches to Extraordinary Access” Xiangyu Xu – MS Thesis Proposal, Advisor: Dr. Xiang Cao “ Intelligent Roadside Unit Deployment in Vehicular Network ” CIS Master�s Projects: CIS Master�s Project students have submitted their presentations via YouTube and will be presenting their posters via Google Meet on Thursday, April 23, 2020 from 9a-10a. Please support the students by watching their YouTube presentation and joining their Google Meet links under their abstracts. 9:00 am – Click on their Google Meet URL for their poster presentations Sanil Apte – MS Project, Advisor: Dr. Byron DeVries “ Frisbee Golf Score Keeper ” Zachary Hancock – MS Project, Advisor: Dr. Vijay Bhuse “ Keycard Access System for Equipment ” Soni Jha – MS Project, Advisor: Dr. Yonglei Tao “ Customs Management System ” Austin Latture – MS Project, Advisor: Dr. Jonathan Engelsma “ Backdrop: An Exploration of Flutter ” Vincenzo Pavano – MS Project, Advisor: Dr. Zachary Kurmas “ Language Vocabulary Builder ” Pooja Pokhrel – MS Project, Advisor: Dr. Yonglei Tao “ Creating Nutrition and Diet Application ” Brayden Scott – MS Project, Advisor: Dr. Andrew Kalafut “ Creating an Incident Response Plan ” Deepthi Sukumar – MS Project, Advisor: Dr. Yonglei Tao “ Real vs. Fake News Classifier ” Meshack Tanui – MS Project, Advisor: Dr. Andrew Kalafut “ Insecurity in the Internet of Things — Amazon Alexa ” William ten Haaf – MS Project, Advisor: Dr. Greg Wolffe “ Wifi Traffic Forwarding Client and Server ” Jeffery Wagner – MS Project, Advisor: Dr. Xiang Cao “ A Prototype for Distribute Computing Platform ” Thank you for supporting our graduate computing students.

Risk-Based Approaches to Extraordinary Access CIS Thesis Proposal Presented By: Kevin Kredit Advisor: Dr. Andrew Kalafut Abstract: The debate has resumed over whether encryption systems should support alternative means of decryption intended for law enforcement use, called exceptional access (EA). Recent events have renewed interest in EA from the U.S. legislature. Although EA would reduce digital security and privacy compared to free use of encryption, the quality of the technical and regulatory approach can make a substantial difference. By considering legislative action as part of the threat model, security researchers are obliged to engage the topic. In this presentation I will speak to the motivation and assumptions I bring to the topic, provide historical context, and introduce argument maps as a means of breaking down the debate and data flow diagrams as a tool to model threats and EA proposals.

Intelligent Roadside Unit Deployment in Vehicular Network CIS Thesis Proposal Presented By: Xiangyu Xu Advisor: Dr. Xiang Cao Abstract: Intelligent Transportation System (ITS) has been an important research area in building the foundational infrastructures of self-driving cars and improving safety and traffic efficiency of future transportation systems. ITS is a relatively novel field compared to conventional transportation systems, but it has already taken vital roles worldwide and will inevitably become a daily part of our future. According to National Highway Traffic Safety Administration (NHTSA), it has been reported that there are over 27,000 fatality caused by road accidents each year. In 2019, 2.35 million accidents caused injury or disability. Therefore, scientists have been hoping to incorporate intelligence and technology into traditional public transportation systems to help to reduce the risks, accident rate, traffic congestion, or even environmental emissions. There are many research works that have focused on the communication part of ITS, such as vehicular networks. In vehicular networks, there are various ways to collect data and send them to the cloud. Using RSUs is a popular solution. Roadside Unit (RSU) is a key infrastructure in vehicular networks. It is an important intermediate layer between vehicles and the cloud. RSUs can work as edge nodes between the cloud and the vehicles, buffer the raw data, and perform certain preprocessing and filtering before uploading data to the cloud. However, RSUs are not as powerful as cloud servers. They have limited data storage capacities and processing power. Each RSU has a limited signal coverage range as well. It is important to carefully deploy RSUs in the vehicular networks for better efficiency. A lot of research work have proposed insightful solutions for better placement strategies of RSUs. Most of them only focused on the perspective of communication, without considering the data storage capacities of RSUs, or the traffic density. The traffic density often indicates the potential volume of the data generated in a certain area. Therefore, we believe an effective and practical RSU deployment should consider these two factors. In this thesis, we plan to jointly consider these perspectives and offer more comprehensive solutions. Hence, this thesis will offer solutions to fill in the missing part of the RSU deployment optimization for a better, safer and more economical transportation system. In this thesis, two goals will be explored specifically. 1. With limited signal coverage range, data storage capacities of RSUs and traffic density, the minimal number of RSUs with their deployment locations will be found. 2. Given a fixed number of RSUs, the goal is to deploy RSUs in appropriate locations providing best coverage for signal and data storage, with the consideration of traffic density. This thesis research will propose solutions for these two goals by adopting and revising some common machine learning algorithms for better results.

Frisbee Golf Score Keeper CIS Master�s Pr oject Presented By: Sanil Apte Advisor: Dr. Byron DeVries Abstract: The project is a mobile application that will allow users to track scores while they play Frisbee Golf. The app is built for those like me who enjoy playing Frisbee/Disc Golf and would like to track their scores. In the mobile world there are two prevailing operating systems, iOS and Android. Both have their strengths and weakness, however, the problems that arise for developers is that both ecosystems require that applications on their format be in different coding languages. A solution to that is Flutter, which allows developers to build an app entirely in DART, which then automatically optimizes the app so that it can work and be published in both ecosystems. Flutter itself is a new UI software development kit made by Google. While this app has currently been tested in the Android environments, since it is built in Flutter, there is future pathway to iOS development already built in. This app uses Google Firebase, a cloud-based solution which allows hosting and many other features for mobile apps and websites, for authentication services and data storage. The Frisbee Golf app supports multiple pages including the play page and scores page. The play and scores pages allow users to record and review their scores at their leisure. YouTube Presentation: https://youtu.be/g-KFvOF9bOY Google Meet Poster Presentation: https://meet.google.com/nwm-eins-ijf