Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
ORCID
https://orcid.org/0000-0001-6567-9921
Date of Graduation
12-19-2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Department of Computer Science
Advisor(s)
M. Hossain Heydari
Samy El-Tawab
Brett Tjaden
Abstract
The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community.
Recommended Citation
Yorio, Zachary, "Improving a wireless localization system via machine learning techniques and security protocols" (2020). Masters Theses, 2020-current. 66.
https://commons.lib.jmu.edu/masters202029/66
