Peer-to-Peer Localization for Single-Antenna Devices

Zhang, Xianan; Wang, Wei; Xiao, Xuedou; Yang, Hang; Zhang, Xinyu; Jiang, Tao

doi:10.1145/3411833

Cited by 16 publications

(6 citation statements)

References 55 publications

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“…Indoor Localization. Recent years have witnessed the emerging needs of person or device locations in indoor environments, such as homes and office buildings [26], [27], [28]. Generally, indoor localization can be realized by exploiting various sensing modalities, among which Wi-Fi signals are one of the most promising ones thanks to their high ubiquity in indoor scenarios.…”

Section: Related Workmentioning

confidence: 99%

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Xiao

Huang

Zuo

et al. 2023

IEEE Internet Things J.

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Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-theair perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18 dB.

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Section: Related Workmentioning

confidence: 99%

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Xiao

Huang

Zuo

et al. 2023

IEEE Internet Things J.

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“…Other techniques such Phase of Arrival (PoA) and Channel State Information (CSI) are not in the scope of this paper. For detailed information about PoA and CSI localization techniques, the interested reader is referred to [17], [21], [22].…”

Section: A Localization Techniquesmentioning

confidence: 99%

Constrained Localization: A Survey

Savić¹,

Vilajosana

Watteyne³

2022

IEEE Access

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Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

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“…The UAV periodically broadcasts its 3D velocity and 3D position. If the ground client is equipped with IMU and GPS, it can directly calculate v and p. However, for the client without IMU or GPS, we can further employ the technique in [34] For each p along UAV trajectories, there are two representations of γ in the horizontal or vertical direction, which are…”

Section: Sensor-assisted Prediction Algorithmmentioning

confidence: 99%

“…In order to verify the compatibility of SensRate to the moving clients, we further conduct two sets of experiments, in which the clients are stationary or being held by persons who walk randomly. As the ground clients in our experiment (NUC) do not have IMU or GPS, we employ the wirelessassisted technique in [34] to estimate velocity v in the directpath direction and UAV-to-ground distance d d . This process is detailed in Section III-C. We can see the results in Table I.…”

Section: F Moving Clientsmentioning

confidence: 99%

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Xiao¹,

Wang²,

Jiang³

2022

Preprint

Self Cite

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Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobilityinduced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensorestimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24× and 1.28× compared with the bestknown RA algorithm for 2-and 3-antenna APs, respectively.

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Peer-to-Peer Localization for Single-Antenna Devices

Cited by 16 publications

References 55 publications

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Constrained Localization: A Survey

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

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