Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Very close* Not relevant

   Analyzing passive Wi-Fi fingerprinting for privacy-preserving indoor-positioning

   Lorenz Schauer, Florian Dorfmeister, F. Wirth · 2016 · International Conference on Localization and Global Navigation Satellite System · cited 16 times · Not summarized yet
2. Very close Cited by this paper Used in depth by this paper Not relevant

   Privacy-Preserving Positioning in Wi-Fi Fine Timing Measurement

   Domien Schepers, Aanjhan Ranganathan · 2022 · Proceedings on Privacy Enhancing Technologies · cited 21 times · Not summarized yet

   Abstract

   Abstract With the standardization of Wi-Fi Fine Timing Measurement (Wi-Fi FTM; IEEE 802.11mc), the IEEE introduced indoor positioning for Wi-Fi networks. To date, Wi-Fi FTM is the most widely supported Wi-Fi distance measurement and positioning system. In this paper, we perform the first privacy analysis of Wi-Fi FTM and evaluate devices from a wide variety of vendors. We find the protocol inherently leaks location-sensitive information. Most notably, we present techniques that allow any client to be localized and tracked by a solely passive adversary. We identify flaws inWi-Fi FTM MAC address randomization and present techniques to fingerprint stations with firmware-specific granularity fur…

3. Very close Not relevant

   LocFree: WiFi RTT-based Device-Free Indoor Localization System

   Mohamed Mohsen, Hamada Rizk, Hirozumi Yamaguchi et al. · 2023 · GeoIndstry@SIGSPATIAL · cited 17 times · Not summarized yet

   Abstract

   Gaining knowledge of a person's location in the environment without needing a specialized device raises the need for device-free indoor localization systems that could be leveraged in many applications including IoT, security, etc. Wi-Fi is one of the most widely adopted technologies for indoor location determination tasks due to its ubiquity. Existing device-free indoor localization systems rely on the utilization of the Received Signal Strength Indicator (RSSI) and the Wi-Fi Channel State Information (CSI). However, RSSI is highly sensitive to environmental noise such as multi-path interference and fading which causes degradation in the system's performance. In addition, CSI suffers a lack…

4. Very close Not relevant

   Indoor Localization with Wi-Fi Fine Timing Measurements Through Range Filtering and Fingerprinting Methods

   Sami Huilla, Chrysanthos Pepi, M. Antoniou et al. · 2020 · 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications · cited 16 times · Not summarized yet

   Abstract

   Wi-Fi technology has been thoroughly studied for indoor localization. This is mainly due to the existing infrastructure inside buildings for wireless connectivity and the uptake of mobile devices where Wi-Fi location-dependent measurements, e.g., timing and signal strength readings, are readily available to determine the user location. To enhance the accuracy of Wi-Fi solutions, a two-way ranging approach was recently introduced into the IEEE 802.11 standard for the provision of Fine Timing Measurements (FTM). Such measurements enable a more reliable estimation of the distance between FTM-capable Wi-Fi access points and user-carried devices; thus, promising to deliver meter-level location ac…

5. Very close Not relevant

   An Adaptive Wi-Fi Trilateration-Based Indoor Localization

   Gan-Heng Yi, Gunawan Witjaksono bin Djaswadi, Mohd Haris bin Md Khir et al. · 2018 · International Conference on Intelligent and Advanced Systems · cited 7 times · Not summarized yet

   Abstract

   Global Positioning System (GPS) is widely used as the outdoor position system because of it provides the satisfactory results especially for outdoor navigation. However, it is not suitable for the circumstances of non-line of sight (NLoS) and complex indoor environments. To fill the gap, Wi-Fi signal has grasped the great attention being purposed as the solution for Indoor Positioning System (IPS). Existing enormous efforts have been exerted to focus on the Wi-Fi fingerprinting to achieve high accuracy. Through deep learning and machine learning, the current Wi-Fi fingerprinting can localize the object with the accuracy better than GPS. Nonetheless, it involves huge labor-cost calibration an…

6. Very close Not relevant

   Enhancing Indoor Positioning Accuracy: A Comprehensive Study on Euclidean Distance, Trilateration, Wi-Fi RTT and FTM Protocol Integration

   Khanista Namee, Nimit Srikamta, Taveechai Kaewkajone et al. · 2023 · International Conference on Computational Intelligence and Intelligent Systems · cited 2 times · Not summarized yet

   Abstract

   Indoor positioning is a critical technology with a broad spectrum of applications spanning from navigation systems in smart buildings to asset tracking in industrial environments. This research paper explores the effectiveness of four prominent techniques in indoor positioning: Euclidean Distance, Trilateration, Wi-Fi Round Trip Time (RTT), and the Fast Time Measurement (FTM) Protocol. In this study, we conducted extensive experiments and analysis to evaluate the accuracy and performance of these methods within diverse indoor settings. Our findings reveal that Euclidean Distance, when coupled with fingerprinting techniques, achieved an average positioning accuracy of ±1.5 meters in a real-wo…

7. Very close Not relevant

   WhereArtThou: A WiFi-RTT-Based Indoor Positioning System

   Rebal Jurdi, Hao Chen, Yuming Zhu et al. · 2024 · IEEE Access · cited 37 times · Not summarized yet

   Abstract

   Accurate indoor positioning is critical to a variety of use cases including tracking, proximity, mapping, and navigation. Existing positioning methods are either inaccurate (e.g. RSSI-based ranging), impractical (e.g. fingerprinting), or uncommon (e.g. Ultra-Wide Band/UWB). WiFi Round-Trip Time (RTT) marries the ubiquity of WiFi infrastructure with the accuracy of such ranging mechanisms as those used in UWB. We present WhereArtThou, a commercial-grade, plug-and-play indoor positioning solution (IPS) based on WiFi RTT. Our base algorithm uses an extended Kalman filter with a random walk motion model (EKF-RW) which relies solely on RTT distance measurements. We propose two EKF components to e…

8. Very close Not relevant

   Embracing Spatial Awareness for Reliable WiFi-Based Indoor Location Systems

   Jin-Gao Xu, Zheng Yang, Hengjie Chen et al. · 2018 · IEEE International Conference on Mobile Adhoc and Sensor Systems · cited 23 times · Not summarized yet

   Abstract

   Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementati…

Prior work it builds on

1. Close Used in depth by this paper Not relevant

   A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI

   Hamada Rizk, Ahmed M. Elmogy, Hirozumi Yamaguchi · 2022 · Italian National Conference on Sensors · cited 64 times · Not summarized yet

   Abstract

   Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-base…

2. Close Used in depth by this paper Not relevant

   DeepNar: Robust Time-based Sub-meter Indoor Localization using Deep Learning

   Omar Hashem, Khaled A. Harras, M. Youssef · 2020 · Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks · cited 27 times · Not summarized yet

   Abstract

   We propose DeepNar, a deep learning-based indoor localization system that leverages WiFi signals time of flight (ToF) as environment features, to provide accurate and robust indoor localization. DeepNar leverages the fine-time measure¬ment (FTM) protocol in the recent IEEE 802.11-2016 standard to measure WiFi signal round trip time (RTT). Our system combines the advantages of fingerprinting and ranging-based techniques by providing a deep learning model along with a probabilistic framework that captures the complex relation between the propagation times of the WiFi signals heard by the mobile phone and its location. By leveraging the signals RTT, collected using commercial-off-the-shelf acce…

3. Close Used in depth by this paper Not relevant

   WiNar: RTT-based Sub-meter Indoor Localization using Commercial Devices

   Omar Hashem, M. Youssef, Khaled A. Harras · 2020 · Annual IEEE International Conference on Pervasive Computing and Communications · cited 85 times · Not summarized yet

   Abstract

   WiFi time of flight (ToF) measurement has been supported recently by the wireless LAN protocols to improve WiFi localization. Specifically, the IEEE 802.11-2016 standard has a fine-time measurement (FTM) protocol that can be used to measure the WiFi signal round trip time (RTT). In this paper, we present the design and implementation of WiNar, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments. Using commercial-off-the-shelf access points and mobile phones, WiNar leverages the propagation time of the wireless signal with a fingerprinting model to addres…

4. Close Used in depth by this paper Not relevant

   WiDeep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning

   Moustafa Abbas, Moustafa Elhamshary, Hamada Rizk et al. · 2019 · Annual IEEE International Conference on Pervasive Computing and Communications · cited 289 times · Not summarized yet

   Abstract

   Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Due to the ubiquitous availability of WiFi indoors, many indoor localization systems have been proposed relying on WiFi fingerprinting. However, due to the inherent noise and instability of the wireless signals, the localization accuracy usually degrades and is not robust to dynamic changes in the environment.We present WiDeep, a deep learning-based indoor localization system that achieves a fine-grained and robust accuracy in the presence of noise. Specifically, WiDeep combines a stacked denoising autoencoders deep learning model and a probabilistic framework to handle the noise in th…

5. Close Not relevant

   Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System

   Hamada Rizk, Hirozumi Yamaguchi, Maged A. Youssef et al. · 2022 · ACM Trans. Spatial Algorithms Syst. · cited 34 times · Not summarized yet

   Abstract

   Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++, an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing ca…

Papers that cite it

1. Close Not relevant

   TimeSense: Multiperson Device-Free Indoor Localization via RTT

   Mohamed Mohsen, Hamada Rizk, Hirozumi Yamaguchi et al. · 2024 · IEEE Internet of Things Journal · cited 20 times · Not summarized yet

   Abstract

   Locating the persons moving through an environment without the necessity of them being equipped with special devices has become vital for many applications, including security, IoT, healthcare, etc. Existing device-free indoor localization systems commonly rely on the utilization of received signal strength indicator (RSSI) and WiFi channel state information (CSI) techniques. However, the accuracy of RSSI is adversely affected by environmental factors like multipath interference and fading. Additionally, the lack of standardization in CSI necessitates the use of specialized hardware and software. In this article, we present TimeSense, a deep learning-based multiperson device-free indoor loca…

2. Close Not relevant

   A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation

   Hamada Rizk, Ahmed M. Elmogy, Mohamed Rihan et al. · 2024 · Italian National Conference on Sensors · cited 8 times · Not summarized yet

   Abstract

   User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access poi…

3. Close Not relevant

   Indoor Localization System for Seamless Tracking Across Buildings and Network Configurations

   Hamada Rizk, Ahmed Sakr, Ali Ghazal et al. · 2023 · Global Communications Conference · cited 6 times · Not summarized yet

   Abstract

   WiFi localization systems have become increasingly popular owing to the ubiquity of WiFi-enabled devices. However, traditional WiFi networks were not originally designed for localization purposes, and changes to the Access Point locations can significantly drop localization performance. Moreover, these systems are unable to generalize models trained in one building to other buildings due to dependencies on building characteristics and their WiFi configurations. To address this challenge, we propose GlobLoc: the first global indoor positioning system that can function in any environment in a plug-and-play manner without requiring recalibration. Towards this end, we introduce a novel concept c…

4. Close Not relevant

   WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

   Chun-Xi Chen, Jingwen Zhang, Chaopeng Yu et al. · 2026 · Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks · Not summarized yet

   Abstract

   It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is desig…

5. Related Not relevant

   Privacy-Preserving Taxi-Demand Prediction Using Federated Learning

   Yumeki Goto, Tomoya Matsumoto, Hamada Rizk et al. · 2023 · International Conference on Smart Computing · cited 10 times · Not summarized yet

   Abstract

   Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learning for taxi-demand prediction that allows multiple parties to train a machine learning model on their own data while keeping the data private and secure. This can enable organizations to build models on data they otherwise would not be able to access. Evaluation with real-world data collected from 16 taxi service providers in Japan over …