Využití bezdrátové komunikace pro lokalizaci: Za hranice fingerprintingu
Loading...
Date
Authors
ORCID
Advisor
Mark
P
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
Abstract
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
Description
Keywords
machine learning, indoor positioning, privacy, Wi-Fi, 802.11, data analysis, presence detection, occupancy estimation, memory optimization, radio map interpolation, machine learning, indoor positioning, privacy, Wi-Fi, 802.11, data analysis, presence detection, occupancy estimation, memory optimization, radio map interpolation
Citation
BRAVENEC, T. Využití bezdrátové komunikace pro lokalizaci: Za hranice fingerprintingu [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2023.
Document type
Document version
Date of access to the full text
Language of document
en
Study field
bez specializace
Comittee
Dr. Adriano Moreira (předseda)
Dr. Enrique Quintana Orti (místopředseda)
Dr. Antonio Crivello (člen)
Dr. Sergi Trilles Oliver (člen)
Dr. Maria Cristina Rodriguez Sanchez (člen)
Date of acceptance
2023-12-18
Defence
Defense report
The thesis defense took place on the morning of 18 December 2023 and consisted of two parts. Firstly, the candidate presented the work carried out within the framework of the doctoral thesis for 45 minutes. Afterwards, the members of the evaluation panel discussed with the candidate, asking questions and discussing the answers for about 60 minutes. The candidate made an adequate defense of his doctoral thesis, showing a good disposition and answering all the questions correctly. In his answers, the candidate demonstrated a good command of the subject matter of the dissertation as well as sufficient knowledge of the state of the art in the field.
Result of defence
práce byla úspěšně obhájena
Document licence
Standardní licenční smlouva - přístup k plnému textu bez omezení