Millimeter-wave radar offers a promising sensing modality for autonomous systems thanks to its robustness in adverse conditions and low cost. However, its utility is significantly limited by the sparsity and low resolution of radar point clouds, which poses challenges for tasks requiring dense and accurate 3D perception. Despite that recent efforts have shown great potential by exploring generative approaches to address this issue, they often rely on dense voxel representations that are inefficient and struggle to preserve structural detail. To fill this gap, we make the key observation that latent diffusion models (LDMs), though successful in other modalities, have not been effectively leveraged for radar-based 3D generation due to a lack of compatible representations and conditioning strategies. We introduce RaLD, a framework that bridges this gap by integrating scene-level frustum-based LiDAR autoencoding, order-invariant latent representations, and direct radar spectrum conditioning. These insights lead to a more compact and expressive generation process. Experiments show that RaLD produces dense and accurate 3D point clouds from raw radar spectrums, offering a promising solution for robust perception in challenging environments.

With the rapid development of Internet of Things (IoT) technologies, autonomous driving has become an integral part of the IoT ecosystem, where millimeter-wave radar plays a crucial role in ensuring robust perception under challenging weather and lighting conditions. However, its sparse and noisy data often require enhancement using high-end sensors such as LiDAR or RTK-GNSS, which are not common in commercial vehicles. This paper introduces mmEMP+, a self-supervised learning technique that leverages pervasive visual and inertial (VI) measurements to enhance radar sensing data. Using VI data to improve radar sensing introduces several challenges. First, moving objects in a scene are inaccurately reconstructed by VI structure-from-motion, which consequently fails to enhance radar sensing. Second, multipath effects generate spurious radar points that can distort the representation of the environment. Finally, the temporal misalignment between the camera, IMU, and mmWave radar results in mismatched data association, thereby degrading system performance. To address these issues, mmEMP+ first proposes a dynamic 3D reconstruction method to recover the positions of moving features accurately. Then, we develop a spatial-stability checking method to filter out spurious radar points. Finally, mmEMP+ devises a tightly coupled sensor fusion method to calibrate the multi-sensor temporal offset. Experiments on a real-world dataset show that mmEMP+ achieves performance comparable to high-channel LiDAR-supervised methods while using only low-cost sensors. We further validate its effectiveness in IoT-relevant applications such as object detection, localization, and mapping.

As efficiency and security requirements emerge in computing offloading fields, trusted computing offloading has grabbed tremendous sights, especially in Air-Ground Integrated Networks (AGINs). Although traditional computing offloading studies have attempted to employ trust to identify malicious devices without mobility, the integration of trust management and computing offloading has not been explored in a high-hazardous environment. Then, with the increasing requirement of adaptation and security in new-generation network architectures (i.e., AGINs), trust management plays a pivotal role in expanding the implementation of trusted computing offloading. Therefore, we propose a trusted time-aware computing offloading mechanism in AGINs based on communication, computing offloading, and trust modeling. Specifically, a maximum reward optimization formulation is designed to generate an optimal offloading strategy, considering service utility trust, opinion service trust, computing efficiency trust, time constraints, rewards, and task volumes. Based on problem analysis, a solution paradigm is condensed to improve the probability of seeking an efficient solution. Moreover, a greedy search algorithm is designed to find the possible efficient solution, including the default solution setting stage, the boundary search stage, and the greedy reallocation stage. Due to the trust threshold affecting identification results, a dichotomy-based dynamic trust threshold method is employed to empower trusted computing offloading mechanism with the adaptive capability in AGINs. Extensive experiments show that our mechanism outperforms other baselines in terms of accuracy, precision, recall, F-measure, task success rate, and average response time.

Automotive Millimeter-wave (mmWave) radar is becoming an essential modality for autonomous vehicles to enable all-weather perception, especially when LiDAR and camera fail in foggy, rainy, or snowy conditions. It is expected that the mutual interference among multiple radars becomes a critical issue in dense traffic scenarios, which can severely degrade the radar performance and lead to accidents. Despite extensive interference mitigation techniques, none can meet the less valid signal distortion while high robustness requirements for automotive radar perception in dense traffic scenarios. To overcome this predicament, we propose mmMic, a novel multiple mutual interference mitigation system that can accurately separate interference and recover valid signals to maintain the reliability of the radar measurements. The key insight is to design an interference estimator that can accurately localize the interference signal according to its linear frequency modulation features in the time-frequency (TF) domain. In addition, mmMic also fully exploits undisturbed valid signal information within an extended time-frequency domain to reconstruct the damaged signal. Our experiments on a real testbed show that mmMic can improve SINR to interference-free levels from multiple radars, achieving an average SINR improvement of 17% compared to the best-performing baseline.

Enhancing the quality of experience (QoE) in interactive video streaming (IVS) remains a persistent challenge due to the need for ultra-low latency and rising bandwidth demands. Conventional algorithms, whether rule-based or learning-based, are obsessed with achieving tight coupling between encoding and sending bitrate adaptations for low-latency guarantee. However, our measurement studies reveal alarming harms of tight coupling in suppressing throughput, encoding bitrates and smoothness, as application- and transport-layer bitrate adaptations inherently have different mechanisms and goals. To tackle this problem, we propose Octopus, the first loosely coupled cross-layer bitrate adaptation algorithm for IVS to maximize QoE. Instead of blind synchronization, Octopus promotes mutual cooperation and independence between encoding and sending bitrate adaptations by integrating a multi-head network with shortcut connections and auto-regressive action modules. Additionally, based on meta-imitation reinforcement learning, we design a network condition-aware online adaptation scheme that enables the loosely coupled policy to swiftly adapt to diverse and dynamic wireless networks. We implement Octopus on a testbed, a microcosm of real-world deployment, with transceiver pairs running WebRTC on the WeChat for Business dataset. Results show that Octopus outperforms state-of-the-art algorithms, either improving bitrates by 37.1%, or optimizing stalling rate and smoothness by 54.1% and 9.2%, or achieving all-around improvements.

We focus on joint multi-beam optimization (MBO) on both transmitter and receiver of 6G integrated sensing and communication (ISAC) systems, for achieving on-demand communication and sensing (C&S) performance tradeoff for diverse users. However, MBO is of great challenge due to inevitable self-interference (SI) of full-duplex antenna arrays and its non-convex optimization problem nature. Firstly, in order to address the SI challenge, we absorb SI alleviation requirements into problem modeling, and develop a novel SI-alleviated MBO framework. Secondly, in order to handle the non-convex optimization challenge, we resort to Lagrange dual transformation and fractional transformation for problem simplification, and extract structured models to yield an efficient alternating optimization-type MBO algorithm. We establish the convergence of the proposed MBO algorithm to justify our closed-form iterative optimization design. The proposed SI-alleviated MBO method can address different C&S requirements of diverse users, via joint transmitter and receiver beam steering, which paves the way for on-demand ISAC services. It is corroborated by simulations that our SI-alleviated MBO method outperforms state-of-the-art ISAC beamforming baselines, due to our problem-specific algorithm design.

Point cloud extraction (PCE) and ego velocity estimation (EVE) are key capabilities gaining attention in 3D radar perception. However, existing work typically treats these two tasks independently, which may neglect the interplay between radar's spatial and Doppler domain features, potentially introducing additional bias. In this paper, we observe an underlying correlation between 3D points and ego velocity, which offers reciprocal benefits for PCE and EVE. To fully unlock such inspiring potential, we take the first step to design a Spatial-Doppler Diffusion (SDDiff) model for simultaneously dense PCE and accurate EVE. To seamlessly tailor it to radar perception, SDDiff improves the conventional latent diffusion process in three major aspects. First, we introduce a representation that embodies both spatial occupancy and Doppler features. Second, we design a directional diffusion with radar priors to streamline the sampling. Third, we propose Iterative Doppler Refinement to enhance the model’s adaptability to density variations and ghosting effects. Extensive evaluations show that SDDiff significantly outperforms state-of-the-art baselines by achieving 59% higher in EVE accuracy, 4X greater in valid generation density while boosting PCE effectiveness and reliability. The code and dataset will be available on https://github.com/StellarEsti/SDDiff.

With global population growth, sustainable agriculture requires efficient soil moisture sensing for precise irrigation. While commercial soil moisture sensors are often limited by cost and durability, state-of-the-art RF-based sensing solutions require additional signal transmitter infrastructure, hindering widespread adoption. To fill this gap, we introduce GPSoil—a novel soil moisture sensing system that leverages pervasive Global Navigation Satellite System (GNSS) signals. On the hardware side, we employ two antennas along with a low-cost RF switch, enabling error mitigation for long propagation distances by comparing the signals captured from both antennas. On the software side, we leverage the inherent clock drift errors in commercial GNSS sensors and turn them into tools for fine-grained value extraction, enabling high-resolution sensing from otherwise coarse data. By integrating hardware and software innovations, we successfully achieve practical soil moisture sensing using GNSS signals. Built with off-the-shelf components, GPSoil achieves a soil moisture accuracy of 5.2% at a material cost of only $10.56, making it far more affordable than existing systems. GPSoil can sense moisture up to one meter underground, far surpassing other RF-based sensing systems and meeting the needs of most crops and irrigation systems. This work pioneers the use of GNSS signals in agriculture, offering a scalable, low-cost solution for advancing precision irrigation and promoting sustainable agriculture.

Bluetooth Low Energy (BLE) backscatter is a promising candidate for battery-free Internet of Things (IoT) applications. We propose PassiveBLE, a backscatter system that can establish authentic and fully compatible BLE connections on data channels. The key enabling techniques include (i) a synchronization circuit that can wake up tags and activate backscatter communications with symbol-level accuracy to facilitate BLE data packet generation; (ii) a distributed coding scheme that offloads the major encoding and processing burdens from tags to the excitation source while achieving high throughput; (iii) a BLE connection scheduler to enable fully compatible BLE connection interactions, including connection establishment, maintenance and termination for multiple backscatter tags. We prototype PassiveBLE tags with off-the-shelf components and also conduct comprehensive experiments to evaluate the performance. Results demonstrate that PassiveBLE achieves a success rate of over 99.9% in establishing commodity BLE connections. PassiveBLE also achieves commodity-compatible BLE communication with a high goodput of up to 974 kbps in LE 2M PHY mode and 532 kbps in LE 1M PHY mode, which is about 63.3× higher than existing commodity-level BLE backscatter system in the same mode.

Radio frequency (RF) fingerprinting techniques provide a promising supplement to cryptography-based approaches but rely on dedicated equipment to capture in-phase and quadrature (IQ) samples, hindering their wide adoption. Recent advances advocate easily obtainable channel state information (CSI) by commercial WiFi devices for lightweight RF fingerprinting, while falling short in addressing the challenges of coarse granularity of CSI measurements in an open-world setting. In this paper, we propose CSI2Q, a novel CSI fingerprinting system that achieves comparable performance to IQ-based approaches. Instead of extracting fingerprints directly from raw CSI measurements, CSI2Q first transforms frequency-domain CSI measurements into time-domain signals that share the same feature space with IQ samples. Then, we employ a deep auxiliary learning strategy to transfer useful knowledge from an IQ fingerprinting model to the CSI counterpart. Finally, the trained CSI model is combined with an OpenMax function to estimate the likelihood of unknown ones. We evaluate CSI2Q on one synthetic CSI dataset involving 85 devices and two real CSI datasets, including 10 and 25 WiFi routers, respectively. Our system achieves accuracy increases of at least 16% on the synthetic CSI dataset, 20% on the in-lab CSI dataset, and 17% on the in-the-wild CSI dataset.

With wide deployments of bluetooth low energy (BLE) infrastructures and recent advances in backscatter communications, BLE backscatter-based indoor localization becomes a promising solution for asset management and object tracking due to its low cost and near-zero power consumption. Despite extensive localization techniques, none can meet the high accuracy, protocol compatibility, and low-power consumption requirements in BLE backscatter localization. To fill this gap, this article presents B2Loc, the first BLE backscatter localization system that enables decimeter-level localization for low-power backscatter tags with existing BLE infrastructures. The key insight is to design a low-power backscatter modulation to create a frequency-constant and phase-continuous constant tone extension (CTE) field for backscatter localization while guaranteeing communication compatibility with the commodity BLE. In addition, B2Loc also fully exploits the signal propagation signatures and the BLE backscatter characteristic to improve localization performance. We fabricate$\mu $W-level backscatter tags with off-the-shelf components and evaluate the performance using commodity BLE infrastructures. The results show that B2Loc achieves decimeter-level median localization accuracy even deployed in multipath-rich indoor environments.

In this paper, we investigate the frequency domain phase noise estimation in millimeter wave filter-bank multicarrier with offset quadrature amplitude (mmWave FBMC-OQAM) systems. In the frequency domain, the primary impairment introduced by phase noise is the common phase error (CPE), whose estimation performance is significantly affected by the inter-carrier interference (ICI) and inter-symbol interference (ISI) experienced by OQAM symbols. To address the above problem, we propose novel frequency domain phase noise estimation and pilot symbol design methods to mitigate the impact of ICI and ISI. Firstly, we quantify the interferences affecting each pilot symbol and design appropriate weights to mitigate the impact of ICI and ISI on the phase noise estimation. Then, through analysis, we observe that the ICI and ISI originate from the interaction between the imaginary intrinsic interferences and the phase noise terms. Accordingly, we propose a pilot symbol design method by eliminating the primary imaginary intrinsic interferences, thereby reducing the ICI and ISI. Furthermore, we analyze the mean squared error (MSE) lower bound and the computational complexity. Simulation results demonstrate that the proposed phase noise estimation method outperforms the traditional method and the proposed pilot structure further improves the accuracy of the phase noise estimation.

Unmanned aerial vehicles (UAVs) are increasingly being integrated into next-generation networks to enhance communication coverage and network capacity. However, the dynamic and mobile nature of UAVs poses significant security challenges, including jamming, eavesdropping, and cyber-attacks. To address these security challenges, this paper proposes a federated learning-based lightweight network with zero trust for enhancing the security of UAV networks. A novel lightweight spectrogram network is proposed for UAV authentication and rejection, which can effectively authenticate and reject UAVs based on spectrograms. Experiments highlight LSNet’s superior performance in identifying both known and unknown UAV classes, demonstrating significant improvements over existing benchmarks in terms of accuracy, model compactness, and storage requirements. Notably, LSNet achieves an accuracy of over 80% for known UAV types and an Area Under the Receiver Operating Characteristic (AUROC) of 0.7 for unknown types when trained with all five clients. Further analyses explore the impact of varying the number of clients and the presence of unknown UAVs, reinforcing the practical applicability and effectiveness of our proposed framework in real-world FL scenarios.

Complementary to prevalent LiDAR and camera systems, millimeter-wave (mmWave) radar is robust to adverse weather conditions like fog, rainstorms, and blizzards but offers sparse point clouds. Current techniques enhance the point cloud by the supervision of LiDAR’s data. However, high-performance LiDAR is notably expensive and is not commonly available on vehicles. This paper presents mmEMP, a supervised learning approach that enhances radar point clouds using a low-cost camera and an inertial measurement unit (IMU), enabling crowd-sourcing training data from commercial vehicles. Bringing the visual-inertial (VI) supervision is challenging due to the spatial agnostic of dynamic objects. Moreover, spurious radar points from the curse of RF multipath make robots misunderstand the scene. mmEMP first devises a dynamic 3D reconstruction algorithm that restores the 3D positions of dynamic features. Then, we design a neural network that densifies radar data and eliminates spurious radar points. We build a new dataset in the real world. Extensive experiments show that mmEMP achieves competitive performance compared with the SOTA approach training by LiDAR’s data. In addition, we use the enhanced point cloud to perform object detection, localization, and mapping to demonstrate mmEMP’s effectiveness.

Wireless sensing is gaining increasing attention from both academia and industry. Various wireless signals, such as Wi-Fi, UWB, and acoustic signals, have been leveraged for sensing. While promising in many aspects, two critical limitations still exist: a) limited sensing coverage; and b) the requirement for dedicated sensing signals, which may interfere with the original function of the wireless technology. To address these issues, we propose to utilize GPS signals for sensing, as GPS signals are already pervasive and emitted from satellites 24/7 at pre-allocated frequency bands, causing no interference. To make GPS sensing possible, we reconstruct signals with amplitude and phase information which is critical for sensing using the raw measurements reported by commercial GPS receiver module. We also develop sensing models to tailor the unique properties of GPS signals such as extremely long transmission distance. Finally, we introduce the concept of distributed sensing and design signal processing methods to fuse signals from multiple satellites to improve sensing performance. With all these designs, we prototype the first GPS wireless sensing system on commercial GPS receiver modules. Comprehensive experiments demonstrate that the proposed system can realize meaningful sensing applications such as human activity sensing, passive trajectory tracking, and respiration monitoring.

The past decade has witnessed a surge of interest in human respiration sensing with various wireless signals to achieve at-home smart health. While promising in many aspects, two critical limitations still exist: a) dedicated sensing signal transmitters are needed; b) existing sensing schemes affect the original function of the wireless technology such as communication. In this demo, we present the GPSense Respiration system, a new kind of contact-free respiration sensing system that breaks the above limitations. GPSense Respiration operates by receiving and analyzing the GNSS signals reflected by the human body without additional pre-deployed signal transmitters. Also, GPSense Respiration system will not affect wireless communications because GNSS signals operate at different frequencies than communication signals. This demo enables respiration monitoring for different users in real-time without any calibration.

Metric localization is vital to autonomous driving where it corrects cumulative errors in a long-term run. Such errors are inevitable in real scenarios where GPS signals or some other drift-free exteroceptive measurements are not available, e.g., when an automobile goes through a tunnel. Using FMCW-based mmWave radars is an attractive metric localization technique with improved robustness as RF signals can traverse small particles in harsh weather conditions like snowing, foggy, and storming, but it faces a fundamental challenge of Doppler distortion. Existing works take spatial constraints to mitigate the Doppler distortion of point clouds from mechanical radars with limited accuracy. Modern single-chip mmWave radars that provide dynamic estimates, i.e., radial velocities, bring new opportunities to develop more accurate approaches. This paper presentsDC-Loc++, a robust metric localization framework by compensating Doppler distortions using a single-chip mmWave radar. It consists of an explicit velocity-assisted Doppler compensation module for each radar sub-map, an uncertainty-aware metric registration algorithm, and a failure recovery method that validates measurement constraints to generate a more confident pose graph for optimizing vehicle poses. Extensive experiments on both nuScenes dataset and a synthetic CARLA dataset show the effectiveness ofDC-Loc++, achieving 99.2% success rate and more than 20.0%, 30.2% error reductions in terms of translation and rotation estimates, respectively, compared with existing approaches.

Unmanned aerial vehicle (UAV) has received tremendous attention in the area of edge computing due to its flexible deployment and wide coverage accessibility. In weak infrastructure scenarios, multiple UAVs can form on-site edge computing clusters to handle the real-time tasks. Further, a multi-UAV enabled edge-cloud computing system is coined by cooperating the UAVs with remote cloud, which provides superior computing capability. However, the uneven distribution of tasks makes it difficult to meet the real-time requirements when load balancing is unavailable. To address above issue, a delay minimization problem for multi-UAV enabled edge-cloud cooperative offloading is investigated in this paper. The problem is formulated as a non-convex problem based on models that reflect characteristics of the system, such as ubiquitous network congestion, air-to-ground wireless channel and cooperative parallel computing. An efficient cooperative offloading algorithm is proposed to address the problem. Specifically, convex approximation is applied to make the original problem tractable, and Lyapunov optimization is utilized to make online task offloading decisions. Finally, the correctness of the models are verified through a practical UAV-edge computing platform. Simulations based on measurement results and real-world datasets indicate that, the proposed algorithm fully utilizes the available energy to significantly reduce the tasks' completion delay.

By utilizing on-board sensors and computing resources in intelligent vehicles, vehicular crowdsensing can collect a series of sensing data. Typically, sensing vehicles can be divided into opportunistic vehicles with fixed trajectories and participatory vehicles with changeable trajectories. Therefore, to complete sensing tasks more effectively, how to combine the advantages of the mobility characteristics of the two vehicles is a challenging problem. To solve this problem, this paper innovatively proposes a joint scheduling and incentive-driven two-stage hybrid sensing method. Specifically, the method is divided into two stages: opportunistic vehicle selection and participatory vehicle scheduling. In particular, both types of vehicles are managed through the Crowd Sensing Platform (CSP). For the first stage, this paper proposes a reverse auction-based incentive mechanism to select the lowest-cost set of vehicles to complete sensing tasks. This mechanism mainly consists of two steps: winning vehicle selection and reward payment. It is also verified that the proposed mechanism can ensure the individual rationality and truthfulness of opportunistic vehicles. For the second stage, based on the first-stage sensing results, this paper proposes a Soft Actor-Critic (SAC) based approach to scheduling participatory vehicle trajectories to complete sensing tasks. In addition, this paper also considers sensing fairness to ensure the balance of sensing task completion in different sub-regions. Through the two-stage hybrid sensing method, this paper aims to minimize the CSP overhead while ensuring sensing fairness. Finally, extensive evaluation results based on Roma taxi data sets demonstrate that the proposed method works effectively and outperforms other benchmark schemes in different working scenarios.

Real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7–75.2% of bandwidth or improve accuracy by 3.1–9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.

Maximizing quality of experience (QoE) for interactive video streaming has been a long-standing challenge, as its delay-sensitive nature makes it more vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has demonstrated great potential, existing works are either limited by fixed models or require enormous data/time for online adaptation, which struggle to fit time-varying and diverse network states. Driven by these practical concerns, we perform large-scale measurements on WeChat for Business’s interactive video service to study real-world network fluctuations. Surprisingly, our analysis shows that, compared to time-varying network metrics, network sequences exhibit noticeable short-term continuity, sufficient for few-shot learning requirement. We thus propose Fiammetta, the first meta-RL-based bitrate adaptation algorithm for interactive video streaming. Building on the short-term continuity, Fiammetta accumulates learning experiences through offline meta-training and enables fast online adaptation to changing network states through few gradient updates. Moreover, Fiammetta innovatively incorporates a probing mechanism for real-time monitoring of network states, and proposes an adaptive meta-testing mechanism for seamless adaptation. We implement Fiammetta on a testbed whose end-to-end network follows the real-world WeChat for Business traces. The results show that Fiammetta outperforms prior algorithms significantly, improving video bitrate by 3.6%-16.2% without increasing stalling rate.

Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.

Despite the prevalence of GPS services, they still suffer from intermittent positioning with poor accuracy in partially shadowed regions like urban canyons, flyover shadows, and factories' indoor areas. Existing wisdom relies on hardware modifications of GPS receivers or power-hungry infrastructures requiring continuous plug-in power supply which is hard to provide in outdoor regions and some factories. This paper fills the gap with GPSMirror, the first GPS-strengthening system that works for unmodified smartphones with the assistance of newly-designed GPS backscatter tags. The key enabling techniques in GPSMirror include: (i) a meticulous hardware design with microwatt-level power consumption that pushes the limit of backscatter sensitivity to re-radiate extremely weak GPS signals with enough coverage approaching the regulation limit; and (ii) a novel GPS positioning algorithm achieving meter-level accuracy in shadowed regions as well as expanding locatable regions under inadequate satellites where conventional algorithms fail. We build a prototype of the GPSMirror tags and conduct comprehensive experiments to evaluate them. Our results show that a GPSMirror tag can provide coverage up to 27.7 m. GPSMirror achieves median positioning accuracy of 3.7 m indoors and 4.6 m in urban canyon environments, respectively.

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 article 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-the-air 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 above 90% in untargeted attacks with small perturbation-to-signal ratios of about −18 dB.

Wi-Fi technology is becoming a promising enabler of device-free fitness tracking to provide reviews and recommendations for effective homely exercise. State-of-the-art Wi-Fi fitness assistants succeed in recognizing the simple meta-movements (e.g., Push-Up and Squat) with discrete and repeatable patterns. Unfortunately, these prior attempts can hardly scale to the combination movements of ever-growing interests in intensive fitness programs. Combination movements are composed of meta-movements that are mutually concatenated or inserted. They have a compound characteristic that inherits from the diversity of combination orders and continuity of meta-movements. The compound characteristic causes substantial training data collection costs and a challenge of combination decomposition that is a prerequisite for providing fine-grained fitness assessment. To this end, we proposeInFit, a Wi-Fi-based device-free fitness assistant system for combination movements. First, we design a novel data augmentation method, namelyStitching-based Virtual Sample Generation(SVSG), to reduce the training data collection costs by generating virtual combination movements. Second, a 2-stage combination movement recognition model is designed to learn temporal dependencies between movements and decompose combination movements. From its outputs, we can tell whether a combination movement is standard. Extensive experimental results show that InFit can achieve an average recognition accuracy of$94\%$. With zero training samples of combination movements, the average accuracy is$40\%$higher than the baselines. In addition, SVSG can provide a general enhancement on multiple competing schemes with similar sensing tasks.

Automotive mmWave radar has been widely used in the automotive industry due to its small size, low cost, and complementary advantages to optical sensors (e.g., cameras, LiDAR, etc.) in adverse weathers, e.g., fog, raining, and snowing. On the other side, its large wavelength also poses fundamental challenges to perceive the environment. Recent advances have made breakthroughs on its inherent drawbacks, i.e., the multipath reflection and the sparsity of mmWave radar's point clouds. However, the frequency-modulated continuous wave modulation of radar signals makes it more sensitive to vehicles’ mobility than optical sensors. This work focuses on the problem of frequency shift, i.e., the Doppler effect distorts the radar ranging measurements and its knock-on effect on metric localization. We propose a new radar-based metric localization framework, termed DC-Loc, which can obtain more accurate location estimation by restoring the Doppler distortion. Specifically, we first design a new algorithm that explicitly compensates the Doppler distortion of radar scans and then model the measurement uncertainty of the Doppler-compensated point cloud to further optimize the metric localization. Extensive experiments using the public nuScenes dataset and CARLA simulator demonstrate that our method outperforms the state-of-the-art approach by 25.2% and 5.6% improvements in terms of translation and rotation errors, respectively.

Deep learning has shown impressive performance in semantic segmentation, but it is still unaffordable for resource-constrained mobile devices. While offloading computation tasks is promising, the high traffic demands overwhelm the limited bandwidth. Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy. This paper introduces STAC, a DNN-driven compression scheme tailored for edge-assisted semantic video segmentation. STAC is the first to exploit DNN’s gradients as spatial sensitivity metrics for spatial adaptive compression and achieves superior compression ratio and accuracy. Yet, it is challenging to adapt this content-customized compression to videos. Practical issues include varying spatial sensitivity and huge bandwidth consumption for compression strategy feedback and offloading. We tackle these issues through a spatiotemporal adaptive scheme, which (1) takes partial strategy generation operations offline to reduce communication load, and (2) propagates compression strategies and segmentation results across frames through dense optical flow, and adaptively offloads keyframes to accommodate video content. We implement STAC on a commodity mobile device. Experiments show that STAC can save up to 20.95% of bandwidth without losing accuracy, compared to the state-of-the-art algorithm.

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects? In this article, we present TagLoc, a lightweight system that enables D2D localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82 m.

The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things (IoT) networks that provide ultra-low-power communication for numerous miniaturized devices. Although the research community has paid great attention to wireless protocol designs for these networks, researchers are handicapped by the lack of an energy-efficient software-defined radio (SDR) platform for fast implementation and experimental evaluation. Current SDRs perform well in battery-equipped systems, but fail to support miniaturized IoT devices with stringent hardware and power constraints. This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks. To achieve this goal, the core technique is the effective integration of µW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains. We carefully develop a novel circuit design for efficient energy harvesting and power control, and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware. We evaluate the proposed SDR using different modulation schemes, and it achieves a high data rate of 100 kb/s with power consumption less than 200 µW in the active mode and as low as 10 µW in the sleep mode. We also conduct a case study of railway inspection using our platform, achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment, and provide two case studies on smart factories and logistic distribution to explore the application of our platform.

In this paper, we propose a new framework for covert communication in hybrid active/passive networks, which explores the inherent uncertainty of the backscatter transmissions to achieve active and passive communication reciprocity in security. Under this framework, we first model the aggregate interference from the sporadic backscatter transmissions and derive the covert outage probability and the transmission success probability to capture the covertness and reliability. Then, we formulate a transmit power optimization problem to maximize the covert throughput subject to certain covertness and reliability requirements and derive a closed-form approximation of the maximum covert throughput. Particularly, we investigate the worst-case scenario of covert communication in which warden always uses the optimal detection thresholds. Finally, numerical results demonstrate that covert communication with the help of uninformed backscatters that are not coordinated with Alice in the hybrid network is feasible. Results also provide design guidelines for the optimal choice of interference parameters, which is capable of striking a balance between covertness and reliability.

Miniature radar has demonstrated its great potential in smart homes, such as understanding the wellness of the residents and providing ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication? With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3.4kbps data rate while it preserves the sensing resolution of the radar.

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic.

A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5× throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.

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 mobility-induced 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 on-board sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated 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\times $and$1.28\times $compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively.

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

The versatile nature of agile micro aerial vehicles (MAVs) poses fundamental challenges to the design of robust state estimation in various complex environments. Achieving high-quality performance in textureless scenes is one of the missing pieces in the puzzle. Previously proposed solutions either seek a remedy with visual loop closure or leverage RF localizability with inferior accuracy. None of them support accurate MAV state estimation in textureless scenes. This paper presents RFSift, a new state estimator that conquers the textureless challenge with RF-referenced monocular vision, achieving centimeter-level accuracy in textureless scenes. Our key observation is that RF and visual measurements are tied up with pose constraints. Mapping RF to feature quality and sift well-matched ones significantly improves accuracy. RFSift consists of 1) an RF-sifting algorithm that maps 3D UWB measurements to 2D visual features for sifting the best features; 2) an RF-visual-inertial sensor fusion algorithm that enables robust state estimation by leveraging multiple sensors with complementary advantages. We implement the prototype with off-the-shelf products and conduct large-scale experiments. The results demonstrate that RFSift is robust in textureless scenes, 10x more accurate than the state-of-the-art monocular vision system. The code of RFSift is available at https://github.com/weisgroup/RFSift.

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

In today's blockchain system, designing a secure and high throughput blockchain on par with a centralized payment system is a difficult task. Sharding is one of the most worthwhile emerging technologies for improving the system throughput while maintain high-security level. However, previous sharding-related designs have two main limitations. First, the security and throughput of their random-based sharding system are not high enough as they did not leverage the heterogeneity among validators. Second, to design an incentive mechanism that promotes cooperation could incur a huge overhead on their system. In this article, we propose RepChain, a reputation-based secure and fast blockchain system via sharding, which also provides high incentive to stimulate node cooperation. RepChain utilizes reputation to explicitly characterize the heterogeneity among the validators and lay the foundation for the incentive mechanism. We propose a new double-chain architecture-a transaction chain and a reputation chain. For the transaction chain, an efficient Raft-based synchronous consensus has been presented. For the reputation chain, the synchronous Byzantine fault tolerance consensus that combines collective signing has been utilized to prevent the attack on both reputation score and the related transaction blocks. It supports a high throughput transaction chain with moderate generation speed. Moreover, we propose a reputation-based sharding and leader selection scheme. To analyze the security of RepChain, we propose a recursive formula to calculate the epoch security within only $\mathcal {O}(km^{2})$ time. Furthermore, we implement and evaluate RepChain on the Amazon Web Service platform. The results show our solution can enhance both throughout and security level of the existing sharding-based blockchain system.

Bluetooth low energy (BLE) has been significantly contributed to the Internet of Things applications due to its advantages, such as low power consumption and scalability. To further expand its applications, BLE has adopted the Angle of Arrival (AoA) and the angle of departure (AoD) to improve the accuracy in direction finding (DF). The AoA/AoD is calculated using the phase difference (PD) between slots in the received BLE data. The PD performance of BLE receiver (RX) in all directions should be evaluated in detail because it directly determines the accuracy of AoA/AoD. In the traditional test method, the reference PD is converted by an antenna array with switches, which is inaccurate due to the conversion errors and path mismatches. Moreover, because of the mechanical rotation of the turntable, the test processing is very time consuming. This article proposes a BLE test waveform generation method that directly includes the reference PD. A commonly used vector signal generator (VSG) is the only required instrument in the evaluation. The compact test setup not only eliminates the requirement of antenna array, switch, turntable, and anechoic chamber but also eliminates the measurement errors caused by these devices. After creating and downloading all BLE test waveforms with reference PD in the range of −180° to 180° by user-defined steps, the PD performance of BLE RX can be quickly evaluated by switching the test waveforms using commands or running them in the sequence mode. Furthermore, three enhanced test cases with automatic test procedures are proposed to evaluate the detailed PD accuracy of BLE RX under the low signal-to-noise ratio (SNR), carrier frequency offset, and modulation frequency deviation environments. Experimental results show that the PD error of the proposed method is 0.19°, and it needs 360 ms to evaluate the PD accuracy of CC2640R2F in all directions in 1° steps.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. However, existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the restrictive relationship between the energy harvesting rate and achievable rate with estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max-min optimization problem by maximizing the minimum achievable rate for all backscatter tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the achievable rate for maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Our numerical results indicate that our scheme significantly outperforms state-of-the-art energy beamforming schemes. Additionally, the achievable rate of our scheme approaches more than 90% of the rate limit achieved via beamforming with perfect CSI for both receivers.

Due to the openness of wireless medium, robotic networks that consist of many miniaturized robots are susceptible to Sybil attackers, who can fabricate myriads of fictitious robots. Such detrimental attacks can overturn the fundamental trust assumption in robotic collaboration and thus impede widespread deployments of robotic networks in many collaborative tasks. Existing solutions rely on bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, making them unaffordable to miniaturized robots. To overcome this limitation, we present ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots for Sybil attack mitigation. Instead of passively “observing” signatures, ScatterID actively “manipulates” multipath propagation by exploiting backscatter tags to intentionally create rich multipath signatures obtainable to single-antenna robots. Particularly, these signatures are used to carefully construct similarity vectors to thwart advanced Sybil attackers, who further trigger power-scaling and colluding attacks to generate dissimilar signatures. Then, a customized random forest model is developed to accurately infer the identity legitimacy of each robot. We implement ScatterID on the iRobot Create platform and evaluate it under various Sybil attacks in real-world environments. The experimental results show that ScatterID achieves a high AUROC of 0.987 and obtains an overall accuracy of 95.4% under basic and advanced Sybil attacks. Specifically, it can successfully detect 96.1% of fake robots while mistakenly rejecting just 5.7% of legitimate ones.

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

A long-standing vision of backscatter communications is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatters toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further toward breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter links. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. We build a prototype PCB tag and test it in various outdoor and indoor environments. Our experiments show that our prototype achieves up to 10× throughput gain, or extends the range limit by 1.8× compared with the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

he advances in compact and agile micro aerial vehicles (MAVs) have shown great potential in replacing human for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight in such dim or smoky environments presents a conundrum: conventional GPS or computer vision based solutions only work in outdoors or well-lighted texture-rich environments. This paper takes the first step to overcome this hurdle by proposing Marvel, a lightweight RF backscatter-based state estimation system for MAVs in indoors. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion that does not require any infrastructure deployment, pre-trained signatures, or even without knowing the controller's location. The enabling techniques are a new backscatter-based pose sensing module and a novel backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial-off-the-shelf MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three brick walls, with an accuracy of 34 cm for localization and 4.99° for orientation estimation, outperforming commercial GPS-based approaches in outdoors.

Face authentication systems have gained widespread popularity because of their user-friendly usage and increasing recognition accuracy. Unfortunately, the boom in mobile social networks has bought with it media-based facial forgery; a critical threat where an adversary forges or replays the victim's photograph/video to fool the system. In this article, we propose EchoFace, an effective and robust liveness detection system to enhance face authentication in defending against media-based attacks, which works with today's smartphones/smartwatches without any hardware modification. EchoFace uses active acoustic sensing to differentiate the uneven stereostructure of the face and the flat forged media. Our proposed scheme effectively extracts the desired reflection profiles from the target. Moreover, we propose effective similarity measurements of reflection profiles to distinguish live users from forged media, which works robustly under various environmental conditions. EchoFace only requires low cost and universally equipped acoustic sensors without human intervention for liveness detection, which can be easily deployed in a variety of application scenarios. We implement EchoFace on commercial smartphones, and experiment results show that EchoFace achieves an average detection accuracy higher than 96% and false alarm rate lower than 4% across various media attacks and different levels of background noise. This shows its great potential to enhance the security of widely deployed face authentication systems in real scenarios.

Bluetooth low-energy (BLE) beacons have been used in a variety of IoT applications. This article proposes a compact and cost-effective BLE beacon, which only uses a 2.4-GHz Gaussian frequency-shift keying (GFSK) transceiver and an ultralow power 8-bit 16-MHz microcontroller unit (MCU). The minimum requirements and the data processing procedure for commonly used GFSK transceivers to generate the BLE-compatible advertising packages are also provided in this article. Benefited from the compact architecture and the simple generation procedure, the BLE-compatible packages can be directly controlled to broadcast at any given radio frequency (RF) channel or time. Based on the flexible characteristics of our proposed beacon, the multiprotocol and dynamic content advertising are developed in this article. With the help of multiprotocol and dynamic content advertising, the proposed beacons can be used to provide more sophisticated services. Since the broadcast channels and time are freely controlled, the proposed BLE beacons are also very suitable for anti-collision applications with custom-defined channels and time intervals. The experimental results show that the proposed beacon can broadcast BLE-compatible protocol data units (PDUs) at a minimum interval of about 1 ms. The measured average current is about 51 uA when the advertising interval is 800 ms. Different protocols and dynamic content PDUs generated by the proposed beacon can be properly received by BLE-enabled devices, proving that they are fully compatible with the BLE specification. The measured bit rate of the proposed beacon in dynamic content advertising is about 198.4 kb/s.

The design paradigm for backscatter tags is to avoid complex functionality and make tags as simple as possible. However, such a design principle leads to the prevalence of signal collision as tags cannot sense other tags' ongoing transmissions. The high probability of tag collision will result in low overall throughput. Although there are some existing efforts to resolve tag collisions, they either require good channel conditions or can only resolve a limited number of tags as channel capacity is deficient when SNR is low. In this article, to overcome this limitation, we bring in antenna arrays to boost the channel capacity. We propose FreeScatter, which can support scalable concurrent backscatter transmission using an antenna array. FreeScatter extracts the path that signals traveled and formulates the tags' channel coefficient using the path representations. FreeScatter further exploits the frequency agnostic property so that it can support more spatial streams than the number of antennas. The experimental results show that we can enable up to 20 tags transmitting concurrently. With the ubiquitous connectivity of battery-free tags in the near future, FreeScatter can significantly boost the network throughput.

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

In scalable video coding (SVC) multicast, videos are encoded into several layers that represent multiple quality levels. Mobile users with different wireless channel conditions can obtain different numbers of layers and have different quality of experience (QoE). To enhance the QoE of the users that suffer from the worse channel quality, it is beneficial to stimulate users' cooperation in relaying enhancement layers. However, potential relays may be unwilling to truthfully cooperate with receivers, which results in the asymmetric information problem in relay selecting. In this paper, we model the video relaying selection as a market with multiple receivers (principals) and relays (agents), and solve the problem according to the contract theory. The proposed solution is divided into following two steps: first, contract design and item preselection, and second, matching between each principal and agent. We propose a contract parameter determination method termed as the Matching-Aware strategy. Different from traditional strategies, the proposed Matching-Aware strategy makes the contract competitive in principal-agent matching without knowing the probability distribution of relays' types. The matching step is undertaken by the base station with the purpose of maximizing the social welfare. Numerical results corroborate that the contract-based video relaying scheme can tackle the asymmetric information problem. Besides, compared with other two baseline strategies, the proposed Matching-Aware strategy achieves higher QoE.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT's TX power when the AP sets 8× clock rate.

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 upper-layer 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 challenge-response 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.

The potential of multiuser MIMO (MU-MIMO) to increase network capacity has been intensively studied. To enable concurrent transmission in Frequency-Division Duplex (FDD) systems with limited feedback, users have to report the estimated channel state information (CSI) to the base station (BS) for interference elimination, which however has been proven to be vulnerable. In this paper, we reveal how to utilize the CSI feedbacks to launch sniffing attacks in a deterministic way, where thesniffing attackenables the attackers to eavesdrop other users in the same transmission group. We conduct a rigorous theoretical analysis on the construction of the forged CSI. In the proposed sniffing attacks, the malicious users can successfully sniff other users’ messages without knowing their own messages for signal cancellation. To make sniffing attacks more practical, we explore the possibilities of sniffing multiple users by a coalition of malicious users or a single malicious user. To thwart sniffing attacks, we design a novel secure CSI feedback (SCF) protocol based on the random masking technique, which requires simple modifications at the BS and incurs a little additional workload. Extensive theoretical analysis and experimental results are provided to further validate the effectiveness of our proposed sniffing attacks and the corresponding countermeasure.

Heterogeneous ultra dense network (UDN) emerges as one of the potential techniques to tackle the 1000x data challenge in 5G network. However, by cell densification, backhaul becomes one of the key issues in 5G heterogeneous UDN. In this paper, the cooperative wireless backhaul scheduling is concerned. First, a novel two-layers hierarchical model is proposed for the cooperative wireless backhaul, based on which energy efficiencies (EEs) of the cooperative orthogonal multiple access (C-OMA) and the cooperative non-orthogonal multiple access (C-NOMA) schemes are derived and analyzed. Moreover, based on the proposed model, a cooperative wireless backhaul optimization which maximizes EE is formulated. To solve this mixed integer non-linear optimization problem, the greedy algorithm-based C-OMA, C-NOMA and joint C-OMA and C-NOMA (JCC) schemes for cooperative wireless backhaul are proposed. Due to the benefits of both the C-OMA and C-NOMA, the proposed JCC scheme shows its superiority over C-OMA, C-NOMA and the traditional OMA schemes, which is validated by the simulation results.

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. Existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the trade-off between the energy harvesting rate and achievable rate using estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max- min optimization problem by maximizing the minimum achievable rate for all tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the ergodic achievable rate. Our numerical results indicate that our scheme can significantly outperform state-of-the-art energy beamforming schemes. Additionally, the proposed scheme achieves performance comparable to that obtained via beamforming with perfect CSI.

Multiple-antenna backscatter is emerging as a promising approach to offer high communication performance for the data-intensive applications of ambient backscatter communications (AmBC). Although much has been understood about multiple- antenna backscatter in conventional backscatter communications (CoBC), existing analytical models cannot be directly applied to AmBC due to the structural differences in RF source and tag circuit designs. This paper takes the first step to fill the gap, by exploring the use of spatial modulation (SM) in AmBC whenever tags are equipped with multiple antennas. Specifically, we present a practical multiple-antenna backscatter design for AmBC that exempts tags from the inter-antenna synchronization and mutual coupling problems while ensuring high spectral efficiency and ultra-low power consumption. We obtain an optimal detector for the joint detection of both backscatter signal and source signal based on the maximum likelihood principle. We also design a two-step algorithm to derive bounds on the bit error rate (BER) of both signals. Simulation results validate the analysis and show that the proposed scheme can significantly improve the throughput compared with traditional systems.

Recent years have witnessed the rapid proliferation of low- power backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such low-power backscatter tags is crucial for IoT-based smart services. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, increasing the deployment cost. To empower universal localization service, this paper presents Rover, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing WiFi-based positioning measurements with inertial measurements to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues such as the interference among multiple tags and the real- time processing for solving the SLAM problem. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.

Connected devices in Internet-of-Things (IoT) continuously generate enormous amount of data, which is transient and would be requested by IoT application users, such as autonomous vehicles. Transmitting IoT data through wireless networks would lead to congestions and long delays, which can be tackled by caching IoT data at the network edge. However, it is challenging to jointly consider IoT data-transiency and dynamic context characteristics. In this paper, we advocate the use of deep reinforcement learning (DRL) to solve the problem of caching IoT data at the edge without knowing future IoT data popularity, user request pattern, and other context characteristics. By defining data freshness metrics, the aim of determining IoT data caching policy is to strike a balance between the communication cost and the loss of data freshness. Extensive simulation results corroborate that the proposed DRL-based IoT data caching policy outperforms other baseline policies.

Recent years have seen major innovations in developing energy-efficient wireless technologies, such as the Bluetooth low energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, very few of these technologies directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying demodulator and channel state information. The artificial Doppler shifts can be compensated for by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf BLE chips and 802.11-compliant testbeds has demonstrated that DopplerFi can achieve a throughput of up to 6.5 Kb/s at the cost of merely less than 0.8% throughput loss.

Information leakage rate is an intuitive metric that reflects the level of security in a wireless communication system, however, there are few studies taking it into consideration. Existing work on information leakage rate has two major limitations due to the complicated expression for the leakage rate: 1) the analytical and numerical results give few insights into the trade-off between system throughput and information leakage rate; 2) and the corresponding optimal designs of transmission rates are not analytically tractable. To overcome such limitations and obtain an in-depth understanding of information leakage rate in secure wireless communications, we propose an approximation for the average information leakage rate in the fixed-rate transmission scheme. Different from the complicated expression for information leakage rate in the literature, our proposed approximation has a low-complexity expression, and hence, it is easy for further analysis. Based on our approximation, the corresponding approximate optimal transmission rates are obtained for two transmission schemes with different design objectives. Through analytical and numerical results, we find that for the system maximizing throughput subject to information leakage rate constraint, the throughput is an upward convex non-decreasing function of the security constraint and much too loose security constraint does not contribute to higher throughput; while for the system minimizing information leakage rate subject to throughput constraint, the average information leakage rate is a lower convex increasing function of the throughput constraint.

With the development of embedded sensors in smartphones and the ever-growing number of mobile users, more and more spatial crowdsourcing applications come into our daily lives. Food delivery, a specific application of spatial crowdsourcing, emerged and quickly proliferates in recent years. However, task selection and scheduling remains a challenging problem in food delivery. In this paper, we aim to address the task selection and scheduling problem from a game-theoretic perspective. Specifically, the riders are allowed to select a set of tasks as well as the task completion order, by taking account of their locations, speed, traveling costs and capacities. The objective of each rider is to maximize his own utility. We formulate a food delivery game and prove the existence of a Nash equilibrium. Experimental results show that our scheme outperforms existing solutions in terms of the social welfare, average utility, task completion ratio, and fairness. Moreover, the social welfare attained by our scheme comes close to that of the centralized optimal solution.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.

Since the mobile camera is often small in size and easy to conceal, existing anti-piracy solutions are inefficient in defeating mobile-camera-based piracy, thus it remains a serious threat to copyright of Intellectual property. This paper presents Rainbow, a low-cost lighting system which prevents mobile-camera-based piracy attacks on intellectual property in the physical world, e.g., art paintings. Through embedding invisible illuminance flickers and chromatic changes into the light, our system can significantly degrade the imaging quality of a camera while maintaining a good visual experience for human eyes. Extensive objective evaluations under different scenarios demonstrate that Rainbow is robust to different confounding factors and can effectively defeat piracy attacks on various mobile devices. Subjective tests on volunteers further evidence that our system can not only significantly pollute pirated photos but also be able to provide good lighting conditions.

The lightweight protocols and low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks due to the lack of sophisticated security protocols. Recent advances advocate the use of an antenna array to extract fine-grained physical-layer signatures to mitigate these active attacks. However, it adds burdens in terms of energy consumption and hardware cost that IoT devices cannot afford. To overcome this predicament, we present ShieldScatter, a lightweight system that attaches battery-free backscatter tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of backscatter tags. These signatures can be used to construct a sensitive profile to identify the location of the signals' arrival, and thus detect the threat. We prototype ShieldScatter with USRPs and ambient backscatter tags to evaluate our system in various environments. The experimental results show that even when the attacker is located only 15 cm away from the legitimate device, ShieldScatter with merely three backscatter tags can mitigate 97% of spoofing attack attempts while at the same time trigger false alarms on just 7% of legitimate traffic.

We present WINS, a state estimator that fuses commodity Wi-Fi and an inertial sensor (IMU) to enable indoor autonomous flight of MAVs. It overcomes the lighting and environmental texture limitations of current vision-based approaches. WINS incorporates two modules: First, a real-time AoA estimation algorithm that outputs drift-free measurements up to 20 Hz to confine the drift of IMU. Second, a novel WiFi-inertial state estimator to let our highly nonlinear system work without the need of prior initializations and without knowing the position of Wi-Fi infrastructure (APs). The preliminary results show that WINS achieves a mean MAV's location accuracy of 61.7 cm with a maximum flying velocity of 1.27 m/s.

On-body devices are an intrinsic part of the Internet-of-Things (IoT) vision to provide human-centric services. These on-body IoT devices are largely embedded devices that lack a sophisticated user interface to facilitate traditional pre-shared key-based security protocols. Motivated by this real-world security vulnerability, this paper proposes SecureTag, a system designed to add defense in depth against active attacks by integrating physical layer (PHY) information with upper-layer protocols. The underpinning of SecureTag is a signal processing technique that extracts the peculiar propagation characteristics of creeping waves to discern on-body devices. Upon overhearing a suspicious transmission, SecureTag initiates a PHY-based challenge-response protocol to mitigate attacks. We implement our system on different commercial off-the-shelf wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can mitigate 96.13 % of active attack attempts while triggering false alarms on merely 5.64 % of legitimate traffic.

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the users as long as being functioning. In this article, we propose a battery-free sensing platform for wearable devices in the form factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing, and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits, and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds’ walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

Securely pairing wearables with ano-ther device is the key to many promising applications. This paper presents Touch-And-Guard (TAG), a system that uses hand touch as an intuitive manner to establish a secure connection between a wristband wearable and the touched device. It generates secret bits from hand resonant properties, which are obtained using accelerometers and vibration motors. The extracted secret bits are used by both sides to authenticate each other and then communicate confidentially. The ubiquity of accelerometers and motors presents an immediate market for our system. We demonstrate the feasibility of our system using an experimental prototype and conduct experiments involving 12 participants with 1,440 trials. The results indicate that we can generate secret bits at a rate of 7.15 bit/s, which is 44 percent faster than conventional text input PIN authentication.

The ability to detect which wearables and smartphones are on the same body has the potential to support a wealth of applications, including user authentication, automatic data synchronization, and personalized profile loading. This paper brings this feature to commercial off-the-shelf (COTS) wearables and smartphones, by creating a virtual “on-body detection sensor” based on devices' inherent wireless capabilities. We investigate using the peculiar propagation characteristics of creeping waves to discern on-body wearables. To this end, we decompose signals into multiple independent components to exploit the variation features of creeping waves. We implement our system on COTS wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can identify on-body devices at 92.3% average true positive rate and 5% average false positive rate.

Dietary monitoring can provide valuable information for disease diagnosis, body weight control, and dietary habit management, and thus it is welcomed by patients, dieters, and nutritionists. While various techniques have been used for dietary monitoring in clinical trials and user studies, they are not ready for daily use. Existing solutions either require tedious manual recording or may impede normal daily activities. In this paper, a pair of diet-aware glasses is designed. The key idea here is that when people wear glasses, the temples of the glasses are in touch with the lower part of the temporalis muscle, one of the mastication muscles. By integrating an electromyography (EMG) sensor into glasses, the glasses can measure the muscle activity of the temporalis to detect intake-related events. This paper instantiates the idea by building a prototype equipped with an EMG sensor, a microcontroller, SD shield/card and a Bluetooth radio. When working together with a smartphone, the glasses can provide detailed information on intake schedule, the number of chewing cycles and broad food category. Extensive experiments are conducted on seven subjects and the results show appealing prospects for our diet-aware glasses. The prototype achieves 96% accuracy for counting the number of chewing cycles and up to 90.8% accuracy for classifying five types of food.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose SEER, a frame-level wideband spectrum adaptation solution which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the desired transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. SEER can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped SEER on the GNURadio/USRP platform to demonstrate its feasibility. Furthermore, using 1.6GHz channel measurements and trace-driven simulations, we have evaluated the merits of SEER over state-of-the-art approaches.

The high sampling rate in Wi-Fi is set to support bandwidth-hungry applications. It becomes energy inefficient in the post-PC era in which the emerging low-end smart devices increase the disparity in workloads. Recent advances scale down the receiver's sampling rates by leveraging the redundancy in the physical layer, which, however, requires packet modifications or very high signal-to-noise ratio. To overcome these limitations, we propose Sampleless Wi-Fi, a standard compatible solution that allows energy-constrained devices to scale down their sampling rates regardless of channel conditions. Inspired by rateless codes, Sampleless Wi-Fi recovers under-sampled packets by accumulating redundancy in packet retransmissions. To harvest the diversity gain as rateless codes without modifying legacy packets, Sampleless Wi-Fi creates new constellation diversity by exploiting the time shift effect at receivers. Our evaluation using GNURadio/USRP platform and real Wi-Fi traces has demonstrated that Sampleless Wi-Fi significantly outperforms the state-of-the-art downclocking technique in both decoding performance and energy efficiency.

Securely pairing wearables with another device is the key to many promising applications, such as mobile payment, sensitive data transfer and secure interactions with smart home devices. This paper presents Touch-And-Guard (TAG), a system that uses hand touch as an intuitive manner to establish a secure connection between a wristband wearable and the touched device. It generates secret bits from hand resonant properties, which are obtained using accelerometers and vibration motors. The extracted secret bits are used by both sides to authenticate each other and then communicate confidentially. The ubiquity of accelerometers and motors presents an immediate market for our system. We demonstrate the feasibility of our system using an experimental prototype and conduct experiments involving 12 participants with 1440 trials. The results indicate that we can generate secret bits at a rate of 7.84 bit/s, which is 58% faster than conventional text input PIN authentication. We also show that our system is resistant to acoustic eavesdroppers in proximity.

With the increasing demand for higher performance wireless local area networks (WLANs), channel bonding was first proposed in the IEEE 802.11n protocol to offer a higher data rate by combining two 20 MHz channels into one 40 MHz channel. Although much has been understood about channel bonding management, hardly any of these innovations have made it into today's IEEE 802.11 WLANs in a distributed manner. This paper presents the first step to fill the gap, by proposing a channel bonding management solution that can be readily implemented in today's commercial 802.11 devices. We conduct a measurement with off-the-shelf 802.11 devices in a real WLAN to characterize channel bonding, and then propose a channel bonding scheme based on adaptive channel clear assessment (CCA). We conduction evaluation under the typical IEEE 802.11 enterprise scenario, and the results show that our scheme improves the throughput by 37% and 46% compared to the traditional channel bonding scheme and the default CSMA/CA, respectively.

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the people as long as being functioning. In this study, we propose a battery-free sensing platform for wearable devices in the form-factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds' walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

The high sampling rate in Wi-Fi is set to support bandwidth-hungry applications. It becomes energy inefficient in the post-PC era in which the emerging low-end smart devices increase the disparity in workloads. Recent advances scale down the receiver's sampling rates by leveraging the redundancy in the physical layer (PHY), which, however, requires packet modifications or very high signal-to-noise ratio (SNR). To overcome these limitations, we propose Sampleless Wi-Fi, a standard compatible solution that allows energy-constrained devices to scale down their sampling rates regardless of channel conditions. Inspired by rateless codes, Sampleless Wi-Fi recovers under-sampled packets by accumulating redundancy in packet retransmissions. To harvest the diversity gain as rateless codes without modifying legacy packets, Sampleless Wi-Fi creates new constellation diversity by exploiting the time shift effect at receivers. Our evaluation using GNURadio/USRP platform and real Wi-Fi traces have demonstrated that Sampleless Wi-Fi significantly outperforms the state-of-the-art downclocking technique in both decoding performance and energy efficiency.

Owing to the proliferation of mobile devices and their corresponding app ecosystems, more and more people are accessing the internet via various mobile apps, which generates tremendous volume of mobile data. Despite the growing importance of these mobile apps, we have a rather sparse understanding of how they are accessed and what issues affect their usage patterns. To address this problem, we perform a comprehensive measurement on large-scale anonymized network data collected from a tier-1 cellular carrier in China. In this measurement, we characterize the usage pattern of mobile apps and exhibit how the mobility, geospatial properties and behaviours of subscribers affect their mobile app usage at a fine-grained level.

User identification is an essential problem for security protection and data privacy preservation of wearable devices. With proper user identification, wearable devices can adopt personalized settings for different users, automatically label the corresponding data to protect user privacy, and help prevent illegal user spoofing attacks. Current user identification solutions proposed for wearable devices either rely on dedicated devices with high cost or require user intervention which is not convenient. In this work, we leverage the bio-vibrometry to enable a novel user identification solution for wearable devices in small-scale scenarios, e.g., household scenario. Unlike existing user identification solutions, our system only uses the low-cost sensors that are already available for most wearable devices. The key idea is that, when human body is exposed to a vibration excitation, the vibration response reflects the physical characteristics of user, i.e., the mass, stiffness and damping. Meanwhile, due to users' biological diversity, such physical characteristics of different users are quite distinctive. Therefore, we can leverage the discrepancy in users' vibration responses as an identifier. Based on this idea, we propose VibID, which only uses a low-cost vibration motor and accelerometer to generate an unobtrusive vibration to users' arms and capture the corresponding responses. By examining the vibration patterns at different frequencies, VibID builds an ensemble machine learning model to recognize who is using the device. Extensive experiments are conducted on human subjects to demonstrate that our system is reliable in small- scale scenarios and robust to various confounding factors, e.g., arm position, muscle state, user mobility and wearing location. We also show that, in an uncontrolled scenario of 8 users, our system can still ensure a identification accuracy above 91%.

Forming secure pairing between wearable devices has become an important problem in many scenarios, such as mobile payments and private data transmission. This paper presents EMG-KEY, a system that can securely pair wearable devices by leveraging the electrical activity caused by human muscle contraction, that is, Electromyogram (EMG), to generate a secret key. Such a key can then be used by devices to authenticate each other's physical proximity and communicate confidentially. Extensive evaluation on 10 volunteers under different scenarios demonstrates that our system can achieve a competitive bit generation rate of 5.51 bit/s while maintaining a matching probability of 88.84%. Also, the evaluation results with the presence of adversaries demonstrate our system is secure to strong attackers who can eavesdrop on proximate wireless communication, capture and imitate legitimate pairing process with the help of camera.

A typical scenario for public WLANs is large audience environment where Wi-Fi hotspots serve scores of mobile devices. The performance of those Wi-Fi hotspots is extremely poor in terms of low goodput and severe delay due to heavy contention and MAC inefficiency. After carefully investigating the traffic patterns in public WLANs, we proposeCarpool, a practical design that facilitates transmission sharing among multiple receivers, to tackle this problem. The key idea is to reduce contention by feeding frames for multiple destinations into one transmission at physical layer (PHY). As such, each downlink transmission carries payloads for multiple receivers, which reduces contention overhead and enables in-time response to concurrent requests from multiple users. To achieve efficient and reliable transmission in Carpool, we propose i) a lightweight frame structure to support multiple receivers, and ii) a real-time channel estimation scheme to continuously calibrate channel estimation during the transmission of a Carpool frame. We have implemented the entire PHY of Carpool on the GNURadio/USRP platform and tested it in various indoor environments. Furthermore, our trace-driven MAC evaluation shows that Carpool achieves up to$3.2 \times$goodput gain and reduces up to$75$percent delay compared to the IEEE 802.11n MAC frame aggregation scheme.

The proliferation of sensor-equipped smartphones has enabled an increasing number of context-aware applications that provide personalized services based on users' contexts. However, most of these applications aggressively collect users' sensing data without providing clear statements on the usage and disclosure strategies of such sensitive information, which raises severe privacy concerns and leads to some initial investigation on privacy preservation mechanisms design. While most prior studies have assumed static adversary models, we investigate the context dynamics and call attention to the existence of intelligent adversaries. In this paper, we identify the context privacy problem with consideration of the context dynamics and malicious adversaries with capabilities of adjusting their attacking strategies. Then, we formulate the interactive competition between users and adversaries as a competitive Markov decision process (MDP), in which the users attempt to preserve the context-based service quality and their context privacy in the long-term defense against the strategic adversaries with the opposite interests. In addition, we propose an efficient minimax learning algorithm to obtain the optimal policy of the users and prove that the algorithm quickly converges to the unique Nash equilibrium point. Our evaluations on real smartphone context traces of 94 users demonstrate that the proposed algorithm largely improves the convergence speed by three orders of magnitude compared with traditional algorithm and the optimal policy obtained by our minimax learning algorithm outperforms the baseline algorithms.

A recent measurement reveals that a large portion of the reported locations is either forged or superfluous, which raises security issues such as bogus alibis and illegal usage of restricted resources. However, most prior approaches leak users’ location information or rely on external devices. To overcome these limitations, we proposePriLA, a privacy-preserving location authentication system that verifies users’ location information based on physical layer (PHY) information available in legacy Wi-Fi preambles. The crux of PriLA is to turn detrimental features in wireless systems, namely carrier frequency offset (CFO) and multipath, into useful signatures for privacy protection and authentication. In particular, PriLA exploits CFO and channel state information (CSI) to secure wireless transmissions starting from the handshake phase between mobile users and the access point (AP), and meanwhile verify the truthfulness of users’ reported locations based on users’ multipath profiles. We have implemented PriLA on GNURadio/USRP platform and commercial off-the-shelf Intel 5300 NICs, and the experimental results show that PriLA achieves the authentication accuracy of 93.2% on average, while leaking merely 45.7% information in comparison with the state-of-the-art approach.

Device-to-device (D2D) communication is known as a promising way to cope with the growing mobile video traffic, which may suffer from the short duration caused by the limited energy supply. In this paper, we propose a practical D2D cooperation framework based on distributed optimization to extend the video transmission duration. In the multi-path multi-hop D2D communication scenario, we effectively schedule the routes and video traffic workloads to avoid low-battery D2D outages due to non-uniform energy consumption. Specifically, we formulate the D2D cooperation as a consensus problem among the network operator and cooperative devices, and then solve it in a flexible distributed fashion. Numerical results show that the proposed framework could reach the optimality quickly in time division duplex communication systems and significantly increase the duration of the cooperative video transmission.

The proliferation of WiFi hotspots in public places enables ubiquitous Internet access. These public WiFi hotspots usually serve scores of mobile devices and suffer from extremely poor performance in terms of low good put and severe delay. In this paper, we first study the traffic characteristics in public WiFi networks, and demonstrate that the main causes of such poor performance are media access control (MAC) inefficiency and downlink-uplink traffic asymmetry. To cope with these issues, we call attention to transmission carpool, which facilitates an access point (AP) to send multiple frames for different mobile stations (STAs) in a single transmission. It reduces contention and conveys more frames in each channel access. As such, each downlink transmission carries more payload and thus improves efficiency and solves traffic asymmetry simultaneously.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose Seer, a frame-level wideband spectrum adaptation system which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the intended transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. Seer can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped Seer on the GNURadio/USRP platform and tested it under various environments. Furthermore, our evaluation using 1.6GHz spectrum measurements shows that Seer largely improves system throughput over fixed channel configuration and state-of-the-art spectrum adaptation approaches.

In cognitive radio networks (CRNs), collaborative sensing has been considered an attractive means to improving spectrum sensing performance. However, privacy issues arise when multiple service providers (SPs) collaborate on learning the spectrum availabilities. Specifically, sharing sensing data may enable malicious SPs or secondary users (SUs) to geolocate an SU using existing localization techniques. These malicious entities could be untrusted SPs/SUs or external attackers that compromise SPs/SUs. To incentivize SUs to contribute their sensing data, the privacy of each SU should be guaranteed. In this paper, we propose a privacy preservation framework called PrimCos for multi-SP collaborative sensing, which addresses several competing challenges not yet considered in the literature, that is, being compatible with general collaborative sensing schemes, providing privacy guarantee for each SU and ensuring worst case privacy protection under collusion. Both analytical and numerical results show that the proposed framework provides privacy protection for each SU with controllable impact on the sensing performance under different types of attacks.

The surging deployment of WiFi hotspots in public places drives the blossoming of location-based services (LBSs) available. A recent measurement reveals that a large portion of the reported locations are either forged or superfluous, which calls attention to location authentication. However, existing authentication approaches breach user's location privacy, which is of wide concern of both individuals and governments. In this paper, we propose PriLA, a privacy-preserving location authentication protocol that facilitates location authentication without compromising user's location privacy in WiFi networks. PriLA exploits physical layer information, namely carrier frequency offset (CFO) and multipath profile, from user's frames. In particular, PriLA leverages CFO to secure wireless transmission between the mobile user and the access point (AP), and meanwhile authenticate the reported locations without leaking the exact location information based on the coarse-grained location proximity being extracted from user's multipath profile. Existing privacy preservation techniques on upper layers can be applied on top of PriLA to enable various applications. We have implemented PriLa on GNURadio/USRP platform and off-the-shelf Intel 5300 NIC. The experimental results demonstrate the practicality of CFO injection and accuracy of multipath profile based location authentication in a real-world environment.

The proliferation of sensor-equipped smartphones has enabled an increasing number of context-aware applications that provide personalized services based on users' contexts. However, most of these applications aggressively collect users sensing data without providing clear statements on the usage and disclosure strategies of such sensitive information, which raises severe privacy concerns and leads to some initial investigation on privacy preservation mechanisms design. While most prior studies have assumed static adversary models, we investigate the context dynamics and call attention to the existence of intelligent adversaries. In this paper, we first identify the context privacy problem with consideration of the context dynamics and malicious adversaries with capabilities of adjusting their attacking strategies, and then formulate the interactive competition between users and adversaries as a zero-sum stochastic game. In addition, we propose an efficient minimax learning algorithm to obtain the optimal defense strategy. Our evaluations on real smartphone context traces of 94 users validate the proposed algorithm.

The vision of Self-Organizing Networks (SON) has been drawing considerable attention as a major axis for the development of future networks. As an essential functionality in SON, cell outage detection is developed to autonomously detect macrocells or femtocells that are inoperative and unable to provide service. Previous cell outage detection approaches have mainly focused on macrocells while the outage issue in the emerging femtocell networks is less discussed. However, due to the two-tier macro-femto network architecture and the small coverage nature of femtocells, it is challenging to enable outage detection functionality in femtocell networks. Based on the observation that spatial correlations among users can be extracted to cope with these challenges, this paper proposes a Cooperative femtocell Outage Detection (COD) architecture which consists of a trigger stage and a detection stage. In the trigger stage, we design a trigger mechanism that leverages correlation information extracted through collaborative filtering to efficiently trigger the detection procedure without inter-cell communications. In the detection stage, to improve detection accuracy, we introduce a sequential cooperative detection rule to process spatially and temporally correlated user statistics. Numerical studies for a variety of femtocell deployments and configurations demonstrate that COD outperforms the existing scheme in both communication overhead and detection accuracy.

With the increasingly rapid growth of micro-blogging services, influence analysis is becoming a very important topic in this area. Sina Weibo, one of the largest mirco-blogging services in China, has provided a new operation comment-only, which allows users to give feedback on a post without forwarding. However, most of existing works focus on Twitter, which fail to consider this new operation. In paper, we propose a new influence measurement method called WeiboRank on Sina Weibo which applies comment-only operation to help researchers to find ignored influential users who have big influential in comment dimension. Furthermore we analyze why a particular user is influential based on tracing the source of influence to find out which aspects contribute to influence. Our experiments based on a subset of a whole Sina Weibo datatset, which includes three-month records of 22,514,394 users. We conduct extensive experiments and results show that we can accurately find the most influential individuals among entire social networks, while the running time of the algorithm increases linearly with any increase in data size, which is suitable for large scale networks.

With the prosperity of the Internet, many advertisers choose to deliver their advertisements by online targeting, where the ad broker is responsible for matching advertisements with users who are likely to be interested in the underlying products or services. However, this online advertisement targeting system requires user profile information and may fail due to privacy issues. In light of growing privacy concerns, we propose a privacy-aware framework for online advertisement targeting, where users are compensated for their privacy leakage and motivated to click more advertisements. In the framework, an ad broker pays a varying amount of money to users for clicking different advertisements due to distinct privacy leakage. Meanwhile advertisers send advertisements to the ad broker and determine the price per user click they need to pay. We model the interactions among advertisers, the ad broker and users as a three-stage game, where every player aims at maximizing its own utility, and Nash Equilibrium is achieved by backward induction. We further analyze the optimal strategies for advertisers, the ad broker and users. Numerical results have shown that the proposed privacy-aware framework is effective as it enables all advertisers, the ad broker and users to maximize their utilities in case of different levels of user privacy sensitivities. In addition, the proposed framework produces higher profits for advertisers and the ad broker than the traditional “paid to click” system.

The vision of Self-Organizing Networks (SON) has been drawing considerable attention as a major axis for the development of future networks. As an essential functionality in SON, cell outage detection is developed to autonomously detect macrocells or femtocells that are inoperative and unable to provide service. Previous cell outage detection approaches have mainly focused on macrocells while the outage issue in the emerging femtocell networks is less discussed. However, due to the two-tier macro-femto network architecture and the small coverage nature of femtocells, it is challenging to enable outage detection functionality in femtocell networks. Based on the observation that spatial correlations among users can be extracted to cope with these challenges, this paper proposes a Cooperative femtocell Outage Detection (COD) architecture which consists of a trigger stage and a detection stage. In the trigger stage, we design a trigger mechanism that leverages correlation information extracted through collaborative filtering to efficiently trigger the detection procedure without inter-cell communications. In the detection stage, to improve the detection accuracy, we introduce a sequential cooperative detection rule to process the spatially and temporally correlated user statistics. In particular, the detection problem is formulated as a sequential hypothesis testing problem, and the analytical results on the detection performance are derived. Numerical studies for a variety of femtocell deployments and configurations demonstrate that COD outperforms the existing scheme in both communication overhead and detection accuracy.

Self-healing functionality is developed to allow Self-Organizing Networks (SON) autonomously recover from outage. The dynamic topology and configurations of femtocell access points (femto APs) bring significant challenges for enabling self-healing functionality in femtocell networks. Observing that outage in femtocells only has local impacts, this paper proposes a local grouping architecture (LOGA). To recover from outage, LOGA forms the Inner Group to make reconfigurations of femtocells as local as possible. Furthermore, to prevent these reconfigurations from causing extra outages, LOGA sets additional constraints on the Outer Group, which consists of femtocells in the vicinity of the reconfigured femtocells. To properly form local groups in this architecture, a sequential grouping algorithm is proposed. Numerical results demonstrate that LOGA outperforms the existing self-healing scheme both in the number of reconfigured femtocells and in the recovered SINR.

Diagnosis for configuration troubleshooting in femtocell networks is extremely important for end users and network operators. However, because the small-size femtocell only serves several users, the historical data are very scarce. The data scarcity makes traditional cellular troubleshooting solutions which require a large amount of historical data not applicable. In this paper, we propose a new framework based on transfer learning technology to address the data scarcity so as to enhance the accuracy of the diagnosis model. The proposed framework extracts additional diagnosis knowledge by transferring data information from other femtocells. Based on this framework, we design a Cell-Aware Transfer scheme (CAT), which splits data for each femtocell to further enhance the diagnosis accuracy. Extensive evaluations show that our approach can achieve higher accuracy than traditional methods in self-organizing femtocell network scenarios.