State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

He, Shiyue; Wang, Wei; Yang, Hang; Cao, Yang; Jiang, Tao; Zhang, Qian

doi:10.1109/tvt.2019.2950285

Cited by 29 publications

(9 citation statements)

References 33 publications

(43 reference statements)

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“…Note that we focus on a static scenario where UAVs stay static or quasi-static performing various sensing tasks. The dramatical fluctuation of channels caused by the dynamic flight states of the UAV [24], [25] is beyond the scope of our study. The impact of mobility and flight states of the UAV on physical layer security is an interesting research topic and it may be our future work.…”

Section: A Scenario Descriptionmentioning

confidence: 98%

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

Chen

Wang

2020

Preprint

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The multi-UAV network is promising to extend conventional networks by providing broader coverage and better reliability. Nevertheless, the broadcast nature of wireless signals and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs) and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm and an OCF algorithm are proposed to solve these two sequential games whose convergences and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms and the effectiveness of our two-stage game framework in the terms of secrecy performance.

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Section: A Scenario Descriptionmentioning

confidence: 98%

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

Chen

Wang

2020

Preprint

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“…θ R can be trivially derived from the rotation matrix R i i+1 , which is obtained by Eqn. (4). But θ t needs to know the distance r between the antenna array and the AP as the arc length l i i+1 = θ t × r. Here we assume that the translation is small between two consecutive AoAs.…”

Section: B Real-time Positioning Aoa Estimationmentioning

confidence: 99%

“…M ICRO aerial vehicles (MAVs) are great robotic platforms for a wide range of applications in indoors such as warehouse inventory, search and rescue, and hazard detection [1]- [4], thanks to their low cost, small size, and agile mobility. Autonomous flight is essential to these application.…”

Section: Introductionmentioning

confidence: 99%

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Zhang

Wang

Jiang

2021

IEEE Trans. Ind. Electron.

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This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initializationfree for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of 61.7 cm and the attitude error of 0.92°. Index Terms-Micro aerial vehicle, pose estimation, WiFi.

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“…Wireless sensing with machine learning. Despite other applications with radio frequency (RF) signals [26], [27], machine learning approaches have been widely applied in wireless sensing tasks. In [28], unsupervised learning approaches are used to facilitate signal classification in spectrum sensing.…”

Section: Related Workmentioning

confidence: 99%

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

Huang¹,

Wang²,

Wang³

et al. 2020

Preprint

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By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upperlayer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challengeresponse protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.

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State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Cited by 29 publications

References 33 publications

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

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