In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solve the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput.

Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian Particle Filter for Vehicle State Estimation (nGPF-VSE) algorithm wherein we utilize the Genetic Operator Resampling (GOR) technique to enhance the efficiency of PF relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that (nGPF-VSE) outperforms existing methods in terms of vehicle state accuracy.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection systems (cameras, acoustics, etc.) have made significant progress, but various issues have prevented their widespread adoption, including privacy concerns, line-of-sight requirements, and cost issues. Therefore, there is a critical need for a simple and robust system that can detect eye blinks using common commercial equipment. In this paper, we propose BlinkRadar, which uses a low-cost customized impulse-radio ultra- wideband (IR-UWB) radar for non-contact and fine-grained blink detection. BlinkRadar can reliably detect driver blinks in driving conditions, making it possible to infer drowsy driving. To effectively extract the eye blink signal, we analyzed real experimental data to study the characteristics of the eye blink pattern and successfully used the multi-sequence variational mode decomposition (MS-VMD) algorithm to separate the blink signal from the noise signal. We conducted extensive experiments in two different environments (a quiet room and moving vehicles) and found that BlinkRadar had an average blink detection accuracy of over 96.2%. Our results demonstrate the feasibility of using UWB radar for non-contact eye blink detection.

Keyless entry systems (KES) have become popular due to their high user-friendliness, while fingerprint and digital password authentication are two of the most widely used unlocking ways. However, current KES are vulnerable to security threats, such as fingerprint films that deceive fingerprint sensors and stolen passcodes. To address these issues, this paper presents ${\sf Fingerbeat}$ , a two-factor authentication system to defend the security risks of the current widely deployed KES devices. ${\sf Fingerbeat}$ combines original credentials, such as fingerprints and passcodes, with unique and persistent finger-induced vibrations to create a two-factor secure authentication model, while ensuring user-friendliness. ${\sf Fingerbeat}$ leverages the fact that each person's finger structure is distinct and can be represented in distinct vibration patterns. During authentication, FIV is triggered and embodied in the mechanical vibration of the force-bearing body (i.e., KES panel), which can be captured by a low-cost accelerometer. We develop a proof-of-concept prototype of ${\sf Fingerbeat}$ , extracting FIV features from mixed vibration recordings and eliminating the impacts of variable behaviors and external disturbance. Finally, we conduct extensive experiments to demonstrate its security and effectiveness.