Due to the ability to penetrate darkness, smoke, and fog, wireless sensing technologies offer unique advantages for real-world deployments. Recent studies have mounted wireless sensing devices on mobile systems (such as drones and wheeled robots). Despite their promising potential, these approaches are predominantly designed for non-human platforms that exhibit limited mobility, typically involving translational movement with minimal rotation. When deployed on human carriers, however, the frequent body rotations and complex motion patterns significantly degrade their performance, due to the unpredictable changes in target position. In this paper, we propose Mobi2Still, which utilizes mobile human-equipped millimeter wave (mmWave) radars for people detection and tracking. The core idea is to match recurring environment information across different times to estimate radar motion in reverse, and based on this, enable accurate people detection and tracking. Specifically, Mobi2Still extracts geometric structure of scene objects which is radar-motion-independent to match the recurring environmental objects, thereby adapting both rotation and translation of mmWave radars. Furthermore, to improve Mobi2Still's generalization, we design a velocity-aware calibration mechanism to guide it to focus on scene-independent motion features. Experiments demonstrate that Mobi2Still can accurately detect and track people with 98.10% F1-score and 98.32% MOTA, achieving at least 7.54% F1-score (and 11.20% MOTA) improvements compared with state-of-the-art approaches.

Trace heavy metals in water represent a significant health hazard due to their bioaccumulation in the human body. Current detection methods for these contaminants typically require sophisticated laboratory equipment and procedures, limiting their applicability in the field. In this paper, we show that Terahertz (THz), a prospective frequency band for wireless communication technologies, exhibits distinctive pine-like structures (termed “THz-Pine”) in the three-dimensional space when sensing liquids. Moreover, our measurements reveal that this structure possesses superior capabilities for characterizing trace physical information compared to conventional 2D spectrum methods. Leveraging above findings, we introduce Terahertz Trace Sensing,i.e.,TraSen, a contactless method for detecting and quantifying trace heavy metals in water.TraSenoperates by emitting Terahertz signals vertically above a water sample, capturing the reflected signals, and analyzing the resultingTHz-Pineto determine the type and concentration of trace heavy metals present. To quantify trace heavy metals concentrations, we develop an embedded Physics-Informed 3D point cloud deep learning model. Our extensive dataset, comprising 102,500 Terahertz signal samples across 40 trace heavy metals, demonstrates thatTraSencan effectively identify trace heavy metals with 95.4% accuracy in type identification and 94.3% accuracy in concentration quantification. Specifically, our preliminary findings indicate that the detection limit of Terahertz signals for heavy metals in water is approximately 0.1$\mu g/dL$.

Estimating blood pressure (BP) plays a crucial role in healthcare. Traditionally, BP has been measured using a cuff, which is unsuitable for continuous BP monitoring. Recent advancements in cuffless wearable devices, such as graphene electronic tattoos and PPG sensors provide viable alternatives. However, existing BP estimation methods often overlook the importance of temporal modeling, despite the multi-periodicity and temporal dependencies inherent in cuffless BP signals. Moreover, continuous BP monitoring requires personalized modeling, which is further challenged by data scarcity. To address these two challenges, we introduce a novel Physics-Informed Temporal Network (PITN) with adversarial contrastive learning to enable precise BP estimation with very limited data for three different modalities (i.e, bioimpedance, PPG, millimeter wave). Specifically, we first introduce a novel Physics-Informed Temporal Network for investigating BP dynamics' multi-periodicity for cardiovascular cycle modeling and temporal variation. We then apply adversarial training to generate extra physiological time series data, improving PITN's robustness in the face of sparse subject-specific training data. Furthermore, we utilize contrastive learning to capture the discriminative variations of cardiovascular physiologic phenomena. This approach aggregates physiological signals with similar blood pressure values in latent space while separating clusters of samples with dissimilar blood pressure values. Experiments on three widely-adopted datasets with different modailties demonstrate the superiority and effectiveness of the proposed methods over previous state-of-the-art approaches. The code is available athttps://github.com/Zest86/ACL-PITN.

Optimizing user Quality of Experience (QoE) for live video streaming remains a long-standing challenge. The Bitrate Control Algorithm (BCA) plays a crucial role in shaping user QoE. Recent advancements have seen RL-based algorithms overtake traditional rule-based methods, promising enhanced QoE optimization. Nevertheless, our comprehensive study reveals a pressing issue: current RL-based BCAs are limited to the fixed and formulaic reward functions, rendering them ill-equipped to adapt to dynamic network environments and varied viewer preferences. In this work, we present AraLivePro, an automatically adaptive reward learning method that can be seamlessly integrated with any existing learning-based approach in live streaming contexts. To achieve this goal, we have three main designs. First, we construct a dedicated user QoE assessment dataset for live streaming, which includes thousands of videos with millisecond-level metrics. Second, we custom-design an adversarial model that skillfully aligns human feedback with actual network scenarios. Third, we incorporate a QoE-guaranteed reward calibration to deal with the tail-lag effect, which refers to the delayed human feedback caused by network fluctuations near the end of a video segment. We have deployed AraLivePro in practical video streaming systems and conducted massive experiments in comparison to a series of state-of-the-art BCAs. The experimental results demonstrate that AraLivePro not only elevates overall QoE but also exhibits remarkable adaptability to varied network conditions and users.

Human-to-human interaction understanding(HIU) plays a significant role in both physical and mental health of individuals in their daily lives. Nowadays, many of HIU tasks are based on visual information, which can compromise individuals’ privacy in daily life. In this paper, we propose mmHIU, a privacy-preserving HIU system based on mmWave sensing. To achieve mmHIU, we need to address two challenges: Extraction of interaction features and processing of raw data in a manner that preserves interaction features. We design multi-level feature extraction network mmHIU-STNet, and clustering-based frame normalization strategy to address these two challenges. We evaluate mmHIU on 10,811 point cloud sequences from 9 pairs of inter-actors, and results show that its accuracy reaches 90.56%, outperforming baseline methods, with a significant improvement in recognizing complex interaction behaviors.

Taste perception plays an essential role in promoting human health and maintaining nutritional balance. Current taste perception techniques usually require expensive equipment and delicate storage conditions, which restrict their use primarily to laboratory settings. In this paper, we show that terahertz (THz) signals generate unique fingerprint spectra when interacting with different taste molecules in aqueous solutions. Building on this finding, we propose Molecular-level Taste Sensing (MoleSen), a contact-free gustatory sensing method aimed at achieving wireless human-like perception. Specifically, MoleSen emits terahertz signals towards the aqueous solution, captures the reflected signals, and then determines the type and concentration of the tastes by analyzing the unique fingerprint spectra of the reflected signals influenced by the taste molecules. In MoleSen, we design a bio-inspired deep learning model (DTB, Digital Taste Bud) to identify the subtle taste features diluted by water molecules. Additionally, we incorporate domain adaptive learning to address the issue of feature distribution shifts when multiple tastes are mixed. Through extensive experiments involving over 247,000 samples, we demonstrate that MoleSen can accurately differentiate the five basic tastes—sour, bitter, salty, sweet, and umami—with an accuracy of 98.5% for taste type determination and 96.9% for concentration detection. Moreover, MoleSen outperforms the human's taste sensitivity and achieves a highly accurate perception even for mixed tastes.

Heart rate variability (HRV) is an essential indicator of cardiovascular and nervous system function, with wide applications in health monitoring and disease management. Traditional contact-based methods like electrocardiograms (ECG) and photoplethysmography (PPG), while effective, face significant limitations in user experience, such as discomfort during prolonged use, and challenges in long-term, continuous monitoring. Meanwhile, contactless wireless sensing based on mmWave radar offers a promising alternative but is hindered by issues of directional sensing and noise interference. In this paper, we propose mmCG (mmWave Cardiac Gram), a contactless HRV monitoring system. Specifically, it integrates a heartbeat spatial localization method for directional sensing, which significantly improves the SNR, and a dynamic peak search algorithm that leverages heartbeat temporal correlations to effectively mitigate the impact of artifacts. Experimental results show that mmCG achieves advanced performance, reducing the IBI error to 9.44ms, a 51.29% improvement over existing methods. With its lightweight design and enhanced accuracy, mmCG offers a practical solution for daily HRV monitoring, with potential applications in stress management, personalized healthcare, and cardiovascular disease monitoring.

On-demand video streaming continues to dominate the Internet, posing a formidable challenge in designing efficient adaptive bitrate (ABR) algorithms to enhance user quality-of-experience (QoE), particularly amplified by increasing video resolutions (e.g., from 1080P to 2K, 4K, and even 8K) and dynamic Internet conditions. Through a comprehensive study, we identify a common limitation in both existing throughput-based and hybrid-based ABR algorithms: they rely on coarse-grained network bandwidth estimation, missing detailed and accurate (i.e., millisecond-level) network variations. This often leads to misguided resolution (corresponding to bitrate level) decisions, resulting in unsatisfactory QoE. In this work, we propose SuperABR, a fine-grained throughput-driven ABR solution aimed at achieving the optimal bitrate adaptation. To accomplish this, SuperABR first incorporates a two-stage learning module, generating fine-grained future throughput to provide a near-Oracle network view. SuperABR then uses this fine-grained throughput to accurately calculate the download duration for a video chunk, transforming it into the optimal resolution decision via a custom-designed QoE benefit model. We have implemented SuperABR as a lightweight plug-in interface on a standard DASH framework and evaluate it over extensive real-world network traces. Extensive experiments demonstrate that SuperABR can generate accurate future throughput, resulting in a remarkable$1.21\sim 1.46\times $QoE improvement over classic ABR solutions.

Thoracoabdominal Asynchrony (TAA) is a key metric in respiration monitoring, which characterizes the non-parallel periodical motion of human's rib cage (RC) and abdomen (AB) during each breath. Long-term measurement of TAA plays a significant role in respiration health tracking. Existing TAA measurement methods including Respiratory Inductive Plethysmography (RIP) and Optoelectronic Plethysmography (OEP) all intrusive to subjects and have certain requirements on operation conditions, which limit their usage to hospital scenario. To address this gap, we proposemmTAA, the first mmWave-based, non-intrusive TAA measurement system ready for ubiquitous usage in daily-life. InmmTAA, we design a Two-stage RC-AB centroid finding module, aiming to identify the most probable location of RC-AB centroid, which can best represent RC and AB in mmWave sensing scenario. Subsequently, we design TAANet, a novel Convolutional Neural Network (CNN)-based architecture with residual modules, tailored for TAA measurement. Meanwhile, in order to address the imbalance of continuous data, we add imbalance information equalizer including feature and label equalizer during network training. We implementmmTAAon a commonly used multi-antenna mmWave radar. We prototype, deploy and evaluatemmTAAon 25 subjects and 25.7h data in total.mmTAAachieves 4.01$^{\circ }$MAE and 1.56$^{\circ }$average error, close to OEP method.

Mobile Edge Computing (MEC) is a key component of 5G ecosystem, designed to support applications with stringent latency requirements. The fundamental idea is to deploy servers closer to end-users, such as on the network edge, rather than in remote clouds. While conceptually sound, operational 5G networks often lack coordination with MEC, leading to intolerably long response latency. In this work, we propose Sonata, which tightly integrates 5G RAN and MEC at the user space to ensure the performance of latency-critical video analytics. To achieve this, Sonata precisely customizes users’ service demands by fusing application-layer content changes from MEC servers with instantaneous physical-layer dynamics from the 5G Radio Access Network (RAN). It then enforces a deadline-strict resource provision to meet these service demands through real-time interactions between the 5G RAN and MEC servers, in a lightweight and standard-compatible manner. We prototype and evaluate Sonata on a software-defined 5G MEC platform. Our results demonstrate that Sonata achieves an average reduction in response latency of 67.82% compared to conventional 5G edge systems.

Aortic stenosis (AS) is one of the most common and severe valvular heart diseases, which can cause sudden cardiac death. Early detection and diagnosis are the most effective ways to prevent the irreversible progression of AS. Existing methods mainly rely on large or complex devices such as echocardiography, 12-leads electrocardiogram, which are marred by too many medical resources like experimental experts. There has been recent research showing promising results using cardiomechanical signals, and the need for a daily, robust, convenient, low-cost, and user-friendly AS detection system has become more critical than ever. In this paper, we propose FinP-AS, an innovative AS detection system that uses photoplethysmogram (PPG) sensors to achieve fast and low-cost detection of AS. However, using simple and cost-effective PPG sensors does not necessarily make the data analysis process straightforward. Firstly, due to the inherent limitations of devices, PPG signals we captured are always fraught with complex noise and exhibit unclear periodic features. To overcome that, we utilize window slicing and embedding strategies on raw PPG signals, which enhance the periodic characteristics of the PPG signal. Additionally, the features of aortic valve activity are significantly attenuated by the time they are transmitted to the finger, which makes AS features more difficult to extract. To tackle the problem, we have refined our asymmetric convolutional network architecture by incorporating depthwise separable convolutions and residual connections, which allows the network to detect subtle features of AS symptoms in the PPG signal from various depths and orientations beneath the subject’s skin, while simultaneously reducing the parameter count by $50 \%$ and easing the training process. An empirical evaluation of the FinP-AS model across nearly 80 subjects demonstrates robust AS detection, with the best accuracy of $94 \%$, and sensitivity of $\mathbf{9 8 \%}$.

Despite the prevalence of Crowdsourced Live Video Streaming (CLVS), video viewers still suffer from low QoE particularly under rush hours, as the existing Content Delivery Network (CDN) is not scalable enough to handle the massive concurrent streaming. The rapid emergence of Web 3.0 provides new incentives for revisiting and applying the classical P2P networking in CLVS. However, the highly dynamic joining or leaving behavior of CLVS viewers frequently interrupts the real-time streaming and leads to low QoE, which demands to retrofit P2P. In this work, we bridge the gap by proposing a reliable P2P-assisted CLVS system named Venus, where viewers can share their streaming content smoothly, without video freeze regardless of viewers leaving. To realize Venus, different from the single-flow sharing in previous P2P video streaming, we design a novel multiflow framework with lightweight redundancy encoding, so as to handle the inherently high viewer dynamics. Correspondingly, we introduce a multiflow scheduler to enable QoE adaption concertedly over heterogeneous multiple flows. Real-world evaluation confirms the benefits of decentralized CLVS streaming, with Venus outperforming the state-of-the-art CDN solution by almost totally eliminating the video stall while enhancing the video quality by 10.2%.

Cuff-less methods, especially pulse wave analysis (PWA) techniques with PPG/mmWave sensing, have shown great potential for non-intrusive blood pressure (BP) monitoring. However, the state-of-the-art solutions are only validated on small-scale healthy subjects, neglecting patients with abnormal BP and thus a more urgent need for BP monitoring. To bridge the gap, we first build the largest mmWave-BP dataset to our knowledge, including 930 real patients with cardiovascular diseases, and perform extensive experiments, which reveals that all existing PWA methods exhibit far less satisfactory performance with standard deviation errors (STD) exceeding 16 mmHg for systolic BP (SBP) and 11mmHg for diastolic BP (DBP). An in-depth investigation shows that physiological factors have complex effect on vascular elasticity and structure, thus people with very different BP values may exhibit extremely similar pulse waveform, which leads to confusion in model learning. In this work, we propose BP3, which fuses physiological factors into sensing-data-driven deep-learning framework, so as to capture the intricate effect of physiological factors during the whole process of learning pulse waveforms. Evaluation results show that BP3 achieves the mean errors of-1.57 mmHg and -0.34 mmHg, STD of 9.77 mmHg and 7.93 mmHg for SBP and DBP, respectively. Moreover importantly, BP3 shows remarkable gain particularly for subjects with abnormal BP, achieving mean errors that are only 0.48% ~ 20.86% of the state-of-the-art solutions.

Wireless sensing offers a promising approach to identify the content of liquids without opening the container or directly touching the liquid. Although existing methods aim to achieve fine-grained identification, i.e., distinguishing a 1% v/v difference in alcohol content, they still have limitations in detecting highly deceptive counterfeit liquors that have much smaller content differences, sometimes as low as 0.2% v/v alcohol content. In this paper, we propose mm Taster, a mobile system that combines the mmWave radar with a smartphone to perform fine-grained and robust alcohol sensing. To achieve the desired fine granularity, we introduce a novel feature extraction model that exploits theunique reflection responses across multiple mmWave frequencies, which provide discriminative information about liquid content. Furthermore, we observe the serious interference of target displacement on identification performance, which hinders the various applications in mobile scenarios. To enhance the robustness, mm Taster incorporates a customizedtranslation-invariantneural network,ConvNet, to remove the location interference and extract stable liquid-dependent features regardless of target displacement. Extensive experimental results demonstrate that mm Taster can accurately distinguish the alcohol differences as low as0.2% v/vwith an accuracy of over90.8%even in scenarios involving diverse displacements and rotations.

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, i.e., adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95$^{\rm \textit {th}}$percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose TrafAda, a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed TrafAda on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that TrafAda improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.

The rising crowdsourced live video streaming (CLVS) poses great challenges to Internet transport scalability, where a broadcaster’s live video is expected to reach thousands and even millions of viewers in real time. To accommodate such huge concurrent video traffic, the de-facto solution is to employ content delivery network (CDN), which distributes the traffic spatially relative to end viewers, using geographically distributed servers. However, our measurement study over a top operational CLVS platform reveals that CDN is not scalable enough, i.e., it loses efficacy, particularly duringbusy timeand leads to tremendous QoE degradation, e.g., 33.3% video bitrate reduction, in comparison to networkidle time. In this work, we propose Spider, which revives the peer-to-peer (P2P) networking principle to extend the scalability of CLVS system. Beyond traditional P2P for elastic data transmission, Spider retrofits P2P to meet the stringent low-latency requirements of CLVS: proposing a “pair-push” streaming mode to tame the excessive signaling latency; designing a QoE-driven peer pairing algorithm to tackle the Internet path variation and CLVS viewer dynamics. We implement, deploy and evaluate Spider in real-world over 20.9 thousand video sessions. Compared to the de-facto CDN solution, Spider achieves remarkable gains, e.g., video stall rate reductions of 52.57%, video quality gains of 8.22%, and even 66% CDN bandwidth saving. The results validate the feasibility and practicability of embracing P2P for low-latency live video communication for the first time.

Maximizing the quality of experience (QoE) for live video streaming is a long-standing challenge. Traditional video transport protocols, represented by a few deterministic rules, can hardly adapt to the highly heterogeneous and dynamic modern Internet environments. Emerging learning-based algorithms have demonstrated the potential to meet above challenge. However, our measurement study reveals an alarming long-tail performance issue: these learning-based algorithms tend to be bottlenecked by occasional catastrophic events due to the built-in exploration mechanisms. In this work, we propose Loki-plus, which improves the robustness of learning-based model by coherently integrating it with a rule-based algorithm. To enable integration at deep feature level, we first reverse-engineer the rule-based algorithm into an equivalent “black-box” neural network, and then devise a transformer-based continual learning model with effective historical feature reservation. Then, we design a network feature-aware fusion mechanism to fuse the two models in a deep manner. We train Loki-plus in a full-fledged live video system through online learning, and evaluate it over massive video sessions, in comparison to state-of-the-art rule-based and learning-based solutions. The results show that Loki-plus improves not only the average but also the tail performance substantially.

Gait recognition based on millimeter waves (mmWave) can recognize people's identity information by sensing their walking posture, which has found versatile usages in many fields, such as smart home, intelligent security, and health monitoring. While this technology has gained extensive attention in recent years, its possibility of being misused is also increasing. The snooper who misuses the technology could monitor the victim's identity information, which is imperceptible due to the characteristics of mmWave-based gait recognition. In this paper, we propose an identity protector called WW-IDguard, which disrupts the snooper at the physical level. The key idea is that the protector sends a unique signal to interfere with not only the signal but also the gait feature of the person “seen” by the snooper. Experiments demonstrate that WW-IDguard can significantly reduce the accuracy of the mmWave-based gait recognition used by snoopers. We also perform a measurement analysis on the basic method.

Emotion recognition, particularly contactless recognition via wireless sensing, has shown its promise in diverse applications. However, the previous works only focus on emotions rather than the person, i.e., the premise is already knowing who the subject is, without considering the issue of identifying subjects. We envision that user identification and emotion recognition together will bring more adaptive and personalized Internet of Things applications, e.g., a smart home system can react to specific emotions of a specific user, independently. In this work, we move forward to investigate the problem of simultaneous user identification, using only physiological indicators embedded in wireless signals reflected off from targets. Toward the objective, in this article, we first carry out a comprehensive measurement study, which validates the feasibility of simultaneous user identification and emotion recognition. Moreover, the measurement also discovers that the key challenge lies in the limitation of artificial features and the substantial emotion feature deviation across different days, which hinders accurate and robust sensing. To resolve the challenge, we design two multiscale neural networks, incorporated with a custom-built feature attention mechanism, so as to obtain rich feature expression and, thus, enhance the important features for accurate recognition. We prototype mmEMO using a commercial off-the-shelf millimeter-wave radar and experimental evaluation shows that mmEMO can achieve 87.68% user identification accuracy and 80.59% emotion recognition accuracy, respectively.

Respiratory rate (RR) is of great value in health care, especially when it can be continuously monitored using wearable devices in daily life. Recent works employ photoplethysmography (PPG) on smartwatch for continuous respiration monitoring, based on a certain medical discovery called respiratory sinus arrhythmia (RSA), which describes the relationship between respiratory and heart rate. However, we find that these works fall short of robustness. In particular, the respiratory estimation accuracy drops significantly when people breathe faster (e.g., after sports). We further identify the root reason that the RSA gradually weakens as the RR increases. In this article, we propose BreathAnalyzer, which can estimate RR accurately even at high RRs. To achieve this, BreathAnalyzer boosts the weakened RSA and also handles the motion artifacts, by integrating features from multiple domains, i.e., frequency, time, and nonlinear Poincare domain, instead of using the single spectrum or raw signal in previous studies. Moreover, BreathAnalyzer custom-designs a tree-based learning model, which fits multidomain features, while considering limitations of smartwatch. We implement BreathAnalyzer prototype on COTS smartwatch, and extensive evaluation demonstrates that BreathAnalyzer outperforms the state-of-the-art approaches, with accuracy improvement by 35.37%–80.42% across a variety of practical scenarios including high RRs.

Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff based or newly emerging wearable based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this article, we explore contactless BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates the arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size commercial off-the-shelf millimeter-wave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with a mean error of –0.30 mmHg and –0.23 mmHg, as well as a standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an“offline learning”paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this article, we close the gap by proposing Legato, an online RL framework for real-time mobile interactive video systems. Legato puts many individual RL agents directly into the video system, which make video bitrate decisions in real-time and evolve their models over time. Legato then employs a two-level cooperative learning mechanism to enhance video QoE. First, Legato proposes a score-based robust learning algorithm to eliminate risks of quality degradation caused by the RL model's exploration attempts. Then, Legato adaptively aggregates agents following a network condition-aware manner to form its corresponding high-level RL model that can help each individual to react to unseen network conditions. We implement Legato on an interactive real-time video system. Based on the exhaustive evaluations, we find that Legato outperforms the state-of-the-art algorithms significantly across a wide range of QoE metrics.

While 5G has rolled out since 2019 and exhibited versatile advantages, its performance under high/extreme mobility scenes (e.g., driving, high-speed railway or HSR) remains mysterious. In this work, we carry out a large-scale field-trial campaign, taking >13,000 Km round-trips on HSR moving at 250–350 Km/h, with operational 5G cellular coverage along the railway. Our empirical study reveals that coupling interaction among high mobility, 5G handover characteristics, and applications’ sluggish reaction to handover, results in catastrophic damage to user experience: low TCP bandwidth utilization of 26.6% and glitchy 4K VoD streaming. To solve the problem, we propose an edge-assisted mobility management framework called Octopus. Different from previous works, Octopus aims at a standard-compatible and easy-to-deploy solution, thus we take a new design paradigm of exploiting the edge intelligence on multi-access edge computing (MEC). We realize Octopus as a universal MEC service ready for benefiting any third-party mobile applications. We prototype, deploy, and evaluate Octopus in operational 5G, which demonstrates the significant performance gain across the full-range mobile scenarios, e.g., HSR, driving, and walking.

Ambient environment information, including reflectors’ geometrical layout, dimension, and reflectivity, is a key input to versatile millimeter-wave networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support and rely on costly software-defined radios, which prevent their usage in practice. In this work, we design and implement e-mmRanger, which can efficiently sense the environment without infrastructure support. e-mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves. It then re-engineers the time-domain signal series to derive the spatial-domain environment structure through novel reflection path extraction and clustering algorithms. Moreover, e-mmRanger accelerates the mapping process by incorporating a novel Space-knit algorithm, which strategically plans an optimal movement route consisting of minimum sampling locations for the robot. Our experiments verify that e-mmRanger can accurately and efficiently sense the surrounding environment, and the learned information can bring multi-fold performance gain over empirical approaches in mmWave networks.

Human activity recognition (HAR) shows significant applicable values in health care. However, it has not been widely used due to some constraints, like privacy concerns of vision-based methods and the inconvenience of wearable devices-based methods. Recently, millimeter-wave (mmWave) based HAR has caught attention increasingly as it is able to provide a non-imaging, contactless and continuous approach for HAR. However, the existing mmWave-based HAR methods have limited ability and scalability to discriminate similar activities. To address existing problems, we present MM-HAT, an end-to-end Transformer network for mmWave point cloud based HAR. We design point-cloud-specific adaptations to Transformers that have gained success in natural language processing and vision. In addition, MM-HAT takes both mmWave point cloud data and target-data extracted from point clouds as input to mitigate the adverse effects of mmWave signals' vulnerability. We have collected a 7-activity mmWave dataset and carried out experiments on it. Evaluation results show that MM-HAT outperforms the existing methods by up to 34.76% (RadHAR) and 20.05% (MMPoint-GNN).

Adaptive bitrate (ABR) algorithm is deployed extensively in commercial video delivery platforms, aiming to ensure users' quality of experience(QoE). Among the majority of existing ABR algorithms, throughput prediction plays a critical role. However, these predictors suffer from neglecting the throughput inconsistency across diverse chunk payloads under the network dynamics, e.g., the actual throughput of downloading a 4K or a 720P chunk is usually different, even when starting from the same moment. In this paper, we propose a payload-aware adaptive algorithm called PAR, which predicts multiple throughput estimations for different target payloads, and utilizes them to make better bitrate adaptation decisions. Trace-driven experiments show that PAR outperforms the existing ABR schemes across diverse network conditions, with the average QoE improvement of 2.66% to 79.43%.

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to support demanding applications with stringent latency requirements. The basic idea is to deploy servers close to end-users, e.g., on the network edge-side instead of the remote cloud. While conceptually reasonable, we find that the operational 5G is not coordinated with MEC and thus suffers from intolerable long response latency. In this work, we propose Tutti, which couples 5G RAN and MEC at the user space to assure the performance of latency-critical video analytics. To enable such capacity, Tutti precisely customizes the application service demand by fusing instantaneous wireless dynamics from the 5G RAN and application-layer content changes from edge servers. Tutti then enforces a deadline-sensitive resource provision for meeting the application service demand by real-time interaction between 5G RAN and edge servers in a lightweight and standard-compatible way. We prototype and evaluate Tutti on a software-defined platform, which shows that Tutti reduces the response latency by an average of 61.69% compared with the existing 5G MEC system, as well as negligible interaction costs.

Millimeter wave (mmWave) sensing promises to enable contactless and high-precision “in-air” gesture-based human–computer interaction (HCI). While previous works have demonstrated its feasibility, they require tedious gesture collecting for person-independent recognition and they operate in an off-line mode without considering practical issues, such as segmenting gesture and recognition latency. In this work, we proposeM-Gesture, a person-independent real-time mmWave gesture recognition solution. We first build a compact gesture model with a custom-designed neural network to distill the unique features underlying each gesture, while suppressing personalized discrepancy across different users without extra collection and retraining. Furthermore, we design a system status transition (SST) to decide when a gesture begins and ends, which enables automatic gesture segmentation and hence real-time recognition. We prototypeM-Gestureon a commodity mmWave sensor and demonstrate its advantages using two practical applications: 1) a contactless music player and 2) camera. Extensive experiments and user studies show thatM-Gesturehas an accuracy of 99% and a short response latency within 25 ms. Moreover, we also collect and release a comprehensive mmWave gesture data set consisting of 54 620 instances from 144 persons, which may have an independent value of facilitating future research.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we proposeMDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction,MDSRtakes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph,MDSRimproves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluateMDSRon the testbed using commodity mmWave radios. The evaluation results demonstrate thatMDSRimproves network throughput by multi-folds compared with the state-of-the-art one.

Multi-Gbps 60 GHz millimeter wave (mmWave) networks, are considered as the enabling technology for emerging applications such as untethered VR and 4K/8K Miracast. However, user motion, and even orientation change, can cause mis-alignment between mmWave transceivers’ directional beams and thus severe link outage. Within the practical 3D spaces, the combination of location and orientation dynamics leads to the exponential growth of beam searching complexity, which substantially exacerbates the outage. In this paper, we first measure the impact of 3D motion on 60 GHz link performance in the context of VR and Miracast applications. We find that 3D motion exhibits inherent non-predictability, so conventional beam steering solutions are no longer effective. Therefore, we propose a model-driven 3D beam-steering mechanism called Parallel Scanner (PSCAN), which can maintain high performance for mobile 60 GHz links. To enable PSCAN, we first discover and prove a hidden interaction between 3D beams and the spatial channel profile of 60 GHz radios. Leveraging on which, PSCAN strategically scans the 3D space to reduce the search latency by more than one order of magnitude. Experiment results based on a custom-built 60 GHz platform demonstrate PSCAN’s remarkable throughput gain, up to$5\times $, compared with the state-of-the-art.

Natural human activity recognition (HAR) via millimeter wave (mmWave) sensing is a key to the human-computer interaction (HCI), e.g., activity assistance and living state monitoring. Prior work has shown the feasibility of HAR by utilizing mmWave radar, but it falls short of two real-world issues: poor recognition accuracy in the noisy environment and unable to give real-time response due to long latency. In this paper, we propose m-Activity, which can realize HAR while reducing noise caused by environmental multi-path effects, and operate fluently at runtime. m-Activity first distills the human-orientated movements from the noisy background environment and then classify the movements using a custom-designed lightweight neural network called HARnet. To drive the above methods, we propose a simple but efficient response mechanism to enable real-time recognition. We prototype m-Activity on a commodity mmWave radar chip and evaluate its recognition performance over 5 pre-defined human activities within the detection range of 3m, which results in off-line accuracy of 93.25%, and real-time accuracy of 91.52%. Furthermore, we validate m-Activity’s ability under a complex real-world scenario, i.e., fitness center, which is full of severe multi-path effects caused by various strong metal reflectors.

Maximizing the quality of experience (QoE) for real-time video is a long-standing challenge. Traditional video transport protocols, represented by a few deterministic rules, can hardly adapt to the heterogeneous and highly dynamic modern Internet. Emerging learning-based algorithms have demonstrated potential to meet the challenge. However, our measurement study reveals an alarming long tail performance issue: these algorithms tend to be bottle-necked by occasional catastrophic events due to the built-in exploration mechanisms. In this work, we propose Loki, which improves the robustness of learning-based model by coherently integrating it with a rule-based algorithm. To enable integration at feature level, we first reverse-engineer the rule-based algorithm into an equivalent "black-box" neural network. Then, we design a dual-attention feature fusion mechanism to fuse it with a reinforcement learning model. We train Loki in a commercial real-time video system through online learning, and evaluate it over 101 million video sessions, in comparison to state-of-the-art rule-based and learning-based solutions. The results show that Loki improves not only the average but also the tail performance substantially (26.30% to 44.24% reduction of stall rate and 1.76% to 2.17% increase in video throughput at 95-percentile).

Gait recognition, i.e., recognizing persons from their walking postures, has found versatile applications in security check, health monitoring, and novel human-computer interaction. The millimeter-wave (mmWave) based gait recognition represents the most recent advance. Compared with traditional camera-based solutions, mmWave based gait recognition bears unique advantages of being still effective under non-line-of-sight scenarios, such as in black, weak light, or blockage conditions. Moreover, they are able to accomplish person identification while preserving privacy. Currently, there are only few works in mmWave gait recognition, since no public data set is available. In this paper, we build a first-of-its-kind mmWave gait data set, in which we collect gait of 95 volunteers 'seen' from two mmWave radars in two different scenarios, which together lasts about 30 hours. Using the data set, we propose a novel deep-learning driven mmWave gait recognition method called mmGaitNet, and compare it with five state-of-the-art algorithms. We find that mmGaitNet is able to achieve 90% accuracy for single-person scenarios, 88% accuracy for five co-existing persons, while the existing methods achieve less than 66% accuracy for both scenarios.

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate concurrently without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns generated by commodity mmWave radios and strong reflections. In this paper, we conduct an extensive measurement on the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction-based interference-resolving approaches are insufficient. Motivated by the findings, we propose mmMuxing, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, mmMuxing takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfectness and reflections. Using the conflict graph, mmMuxing designs a joint user-beam selection algorithm, which can determine the optimal user-beam combination and lead to the minimum interference in each schedule. We prototype and evaluate mmMuxing over testbed using commodity mmWave radios. The evaluation results demonstrate that mmMuxing improves network throughput by multi-folds compared with the state-of-the-art.

5G, as a monumental shift in cellular communication technology, holds tremendous potential for spurring innovations across many vertical industries, with its promised multi-Gbps speed, sub-10 ms low latency, and massive connectivity. On the other hand, as 5G has been deployed for only a few months, it is unclear how well and whether 5G can eventually meet its prospects. In this paper, we demystify operational 5G networks through a first-of-its-kind cross-layer measurement study. Our measurement focuses on four major perspectives: (i) Physical layer signal quality, coverage and hand-off performance; (ii) End-to-end throughput and latency; (iii) Quality of experience of 5G's niche applications (e.g., 4K/5.7K panoramic video telephony); (iv) Energy consumption on smartphones. The results reveal that the 5G link itself can approach Gbps throughput, but legacy TCP leads to surprisingly low capacity utilization (< 32%), latency remains too high to support tactile applications and power consumption escalates to 2 - 3x over 4G. Our analysis suggests that the wireline paths, upper-layer protocols, computing and radio hardware architecture need to co-evolve with 5G to form an ecosystem, in order to fully unleash its potential.

In best-effort packet networks, a buffer is commonly used to eliminate network jitter and enable smooth video playback at the expense of additional delay (i.e. jitterdelay). In this work, we examine how jitter buffer performs in the Web Real-Time Communications (WebRTC), which is the de-facto standard used in interactive multimedia applications. We collect a dataset from a live video streaming service provider, which adopts WebRTC. After an in-depth analysis of the dataset, we find that jitter buffer can dynamically adjust the jitterdelay but is too conservative, resulting in a very slow decline of jitterdelay. To address the issue, we analyze the control logic of the jitter buffer and find that the reason lies in the use of a fixed reduction factor, known as psi (ψ). We propose an enhanced jitter buffer adaptation mechanism called JTB-ψ, which dynamically adjusts ψ according to frame size and frame duration, to reasonably speed up the decline of jitterdelay. Practical testbed experiments show that JTB-ψ achieves a 41.5% lower jitterdelay and improves receiving frame rate, quantization parameter (QP) and sending bit-rate under different network conditions, compared to the fixed-ψ approach.

The emerging millimeter-wave (mmWave) networking technology promises to unleash a new wave of multi-Gbps wireless applications. However, due to high directionality of the mmWave radios, maintaining stable link connection remains an open problem. Users' slight orientation change, coupled with motion and blockage, can easily disconnect the link. In this paper, we propose RoMil, a robotic mmWave relay that optimizes network coverage through wireless sensing and autonomous motion/rotation planning. The robot relay automatically constructs the geometry/reflectivity of the environment, by estimating the geometries of all signal paths. It then navigates itself along an optimal moving trajectory, and ensures continuous connectivity for the client despite environment/human dynamics. We have prototyped RoMil on a programmable robot carrying a commodity 60 GHz radio. Our field trials demonstrate that RoMil can achieve nearly full coverage in dynamic environment, even with constrained speed and mobility region.

Google congestion control (GCC) is the de-facto standard for web real-time communications (WebRTC) applications and has been implemented in mainstream browsers including Chrome and Firefox. While GCC is designed to achieve high video bit-rate and low latency simultaneously, we find that GCC's performance is far from satisfactory particularly under good network conditions. In particular, we collect a GCC trace dataset with over 1.18 million sessions from a major crowd-sourced live video streaming service provider. We perform in-depth analytics using the dataset, which shows that the sending video bit-rate unnecessarily experiences frequent rollbacks caused by minor fluctuation of transmission delay. To address the issue, we propose a mechanism called GCC-β, which can distinguish normal network fluctuation from real network congestion, and then adaptively sends appropriate bitrates. We implement GCC-β in the WebRTC framework and evaluate its performance using test-bed experiments. The results show that GCC-β is able to avoid up to 90% unnecessary bitrate rollbacks.

Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. The global knowledge and mid-level details are learned from the whole scene and the regions of persons and objects, respectively. Most importantly, we explore the fine-granularity pose keypoints of persons to discover the interactions among persons and objects. Specifically, the pose-guided Person-Object Graph and Person-Pose Graph are proposed to model the actions from persons to object and the interactions between paired persons, respectively. Based on the graphs, social relation reasoning is performed by graph convolutional networks. Finally, the global features and reasoned knowledge are integrated as a comprehensive representation for social relation recognition. Extensive experiments on two public datasets show the effectiveness of the proposed framework.

Ambient environment information, including reflectors' location, dimension and reflectivity, is a key input to many millimeter-wave (mmWave) networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support or non-trivial human labor, and rely on costly software defined radios, which prevent their usage in practice. In this work, we design and implement mmRanger, a system can automatically sense environment without any infrastructure support. mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves and rotates. mmRanger then re-engineers the time-domain signal series to derive the spatial-domain environment structure, through novel reflection path extraction and reflector mapping algorithms. Our experiments verify that mmRanger can accurately sense a given environment with minimal overhead, and the learned information can bring 1.6× and 2.1× performance gain, in terms of network coverage and mobile link throughput, respectively, over empirical approaches in mmWave networks.

Despite the pervasive use of real-time video telephony services, the users' quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. Previous work studied the problem via controlled experiments, while a systematic and in-depth investigation in the wild is still missing. To bridge the gap, we conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our measurement logs fine-grained performance metrics over 1 million video call sessions. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. Instead of blindly following the transport layer's estimation of network capacity, \name reviews historical logs of both layers, and extracts high-level features of codec/network dynamics, based on which it determines the highest bitrates for forthcoming video frames without incurring congestion. To attain the ability, we train \name with the aforementioned massive data traces using a custom-designed imitation learning algorithm, which enables \name to learn from past experience. We have implemented and incorporated \name into \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.

Despite the pervasive use of real-time video telephony services, their quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. We conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. We train \name with the massive data traces from the measurement campaign using a custom-designed imitation learning algorithm, which enables \name to learn from past experience following an expert's iterative demonstration/supervision. We have implemented and incorporated \name into the \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.

The emerging millimeter-wave (mmWave) networking technology promises to unleash a new wave of multi-Gbps wireless applications. However, due to high directionality of the mmWave radios, maintaining stable link connection remains an open problem. Users' slight orientation change, coupled with motion and blockage, can easily disconnect the link. In this paper, we propose miDroid, a robotic mmWave relay that optimizes network coverage through wireless sensing and autonomous motion/rotation planning. The robot relay automatically constructs the geometry/reflectivity of the environment, by estimating the geometries of all signal paths. It then navigates itself along an optimal moving trajectory, and ensures continuous connectivity for the client despite environment/human dynamics. We have prototyped miDroid on a programmable robot carrying a commodity 60 GHz radio. Our field trials demonstrate that miDroid can achieve nearly full coverage in dynamic environment, even with constrained speed and mobility region.

Efficient typing or text-input on mobile devices, such as smartphones and wearables is a long-standing problem, due to the miniature touchscreen on the devices. Recently, touchscreen-free solutions leveraging on acoustic sensing have been proposed, with the advantage of low cost and ubiquitous availability. However, existing solutions usually require people to write in print-style, and more importantly, they are highly vulnerable to environmental change, i.e., they need repetitive training upon slight deviation of writing places or device locations. Therefore, they are far from practical usage. In this paper, we propose a novel acoustic-based text-input system called UbiWriter, which can recognize freestyle handwriting with high ubiquity, i.e., one-time training and writing elsewhere. UbiWriter is built on a new letter recognition principle, which treats the acoustic signal from writing a letter as a complete trajectory, and then distills the recognition feature that is resilient to environmental change. For the actual realization of the principle, we adopt and incorporate a series of techniques, including a feature-preserved fast letter alignment, ${K}$ -nearest neighbor letter classification, and language structure-driven word recognition. We also design and implement an APP with cloud-computing support, in order to facilitate real-time text input. Extensive experimental results demonstrate that UbiWriter outperforms the state-of-the-art under various practical settings.

Due to the increasing density of wireless devices, the ever-growing demands for extremely high data rates, and the spectrum scarcity at the sub-6 GHz bands, making use of the spectrum-rich millimeter-wave (mmWave) frequencies is among the most important technology trends for future wireless networks. The major commercial potential of mmWave networks has led to mmWave being considered a key element for 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. Despite this intense interest in mmWave communications from both the research community and industry, much fundamental research is still needed, especially at the higher layers of the networking stack.

Multi-user MIMO (MU-MIMO) can serve multiple users concurrently, and is the key technology to enable ultra-high-speed wireless access. However, in practice MU-MIMO networks are far from their full potential due to the poor scalability problem, including high computational complexity at PHY layer and large-overhead channel contention at MAC layer. Moreover, cross-cell interference among multiple MU-MIMO cells also counteracts network performance. In this paper, we perform a systematic study on MU-MIMO and propose a fully scalable MU-MIMO user selection protocol called Guidepost. In contrast with previous works, Guidepost builds on a novel principle of indirection channel orthogonality evaluation, so as to decouple and simplify the complicated computational/contention interaction among users. Based on the principle, Guidepost first achieves scalable MU-MIMO user selection with only linear computational complexity. Second, Guidepost realizes distributed user selection through a two-dimensional prioritized contention mechanism, which can single out the best concurrent users efficiently by utilizing both the time and frequency domain resources. Third, Guidepost incorporates a lightweight AP-assisted contention mechanism to handle cross-cell interference in distributed MU-MIMO (netMIMO) where users are widely distributed and cannot sense each other. Software-radio based implementation and experimentation show that Guidepost significantly outperforms state-of-the-art methods under various traffic patterns and node mobility.

In a Multi-User Multiple Input Multiple Output (MU-MIMO) system, an access point (AP) equipped with multiple antennas can serve multiple users simultaneously (i.e., support concurrent multi-streams) and thus achieves multi-fold through- put gain. In practice, however, the gain is significantly comprised by frame-size diversity,i.e., shorter frames need to wait for the finish of the longest frame, which leads to low channel utilization and thus throughput degradation. Frame padding (i.e., more than one short frames are grouped together to fill in the idle channel) has been proposed to solve the problem, but existing approaches are based on heuristic and cannot fully exploit the potential of padding. In this paper, we propose Knapsack Padding (KPad), a novel model-driven frame padding design to maximize MU-MIMO channel utilization. We first formally formulate the frame padding problem as amulti-stream knapsackmodel, and then design a stream decoupling mechanism to handle the unique and complicated inter-stream interference underlying the model, so as to derive the optimal padding schedule efficiently. We evaluate KPad using trace-driven emulation. Extensive evaluation results demonstrate remarkable throughput gain (up to 42%) compared with the state-of-the-art.

60 GHz networks, with multi-Gbps bitrate, are considered as the enabling technology for emerging applications such as wireless Virtual Reality (VR) and 4K/8K real-time Miracast. However, user motion, and even orientation change, can cause mis-alignment between 60 GHz transceivers' directional beams, thus causing severe link outage. Within the practical 3D spaces, the combination of location and orientation dynamics leads to exponential growth of beam searching complexity, which substantially exacerbates the outage and hinders fast recovery. In this paper, we first conduct an extensive measurement to analyze the impact of 3D motion on 60 GHz link performance, in the context of VR and Miracast applications. We find that 3D motion exhibits inherent non-predictability, so conventional beam steering solutions, which targets 2D scenarios with lower search space and short-term motion coherence, fail in practical 3D setup. Motivated by these observations, we propose a model-driven 3D beam-steering mechanism called Orthogonal Scanner (OScan), which can maintain high performance for mobile 60 GHz links in 3D space. OScan discovers and leverages a hidden interaction between 3D beams and the spatial channel profile of 60 GHz radios, and strategically scans the 3D space so as to reduce the search latency by more than one order of magnitude. Experiment results based on a custom-built 60 GHz platform along with a trace-driven emulator demonstrate OScan's remarkable throughput gain, up to 5×, compared with the state-of-the-art.

Low robustness under mobility is the Achilles' heel of the emerging 60 GHz networking technology. Instead of using omni-directional antennas as in existing Wi-Fi/cellular networks, 60 GHz radios communicate via highly-directional links formed by phased-array beam-forming, so as to upgrade wireless link throughput to multi-Gbps. However, user motion causes misalignment between the Tx's and Rx's beam directions, and often leads to link outage. Legacy 60 GHz protocols realign the beams by scanning alternative Tx/Rx beams. But unfortunately this tedious process can easily overwhelm the useful channel time, leaving the Tx/Rx in misalignment most of the time during mobility. In this paper, we propose Beam-forecast, a novel model-driven beam steering approach that can sustain high performance for mobile 60 GHz links. Beam-forecast is built on the observation that 60 GHz channel profiles at nearby locations are highly-correlated. By exploiting this correlation, Beam-forecast can reconstruct the channel profile as the Tx/Rx moves, without explicit channel scanning. In this way, it can predict new optimal beams and realign links for mobile users with minimal overhead. We evaluate Beam-forecast using a reconfigurable 60 GHz testbed along with a trace-driven simulator. Our experiments demonstrate multi-fold throughput gain compared with state-of-the-art under various practical scenarios.

As Wi-Fi networks are becoming insecurely denser, frame loss and the consequent throughput degradation are much more profound, due to severe interference in dense networks. Pre- vious works propose to exploit multi-radio diversity to improve loss resilience. However, they are far from practical because of their incompatibility with the Wi-Fi standard, high deployment cost and large processing delay. In this work, we propose Musubi, which is a practical and low cost solution for exploiting multi- radio diversity. Furthermore, Musubi does not require client-side modification, thus it is totally compatible with the legacy Wi-Fi standard. Musubi leverages flexibility and programmability of the growingly-popular Software- Defined-Network (SDN) based WLAN, and incorporates capture effect and redundancy packet elimination, so as to handle the specific challenges raised in loss resilience. Compared with a state- of-the-art solution, in theory analysis, Musubi achieve 15% jitter decrease with only 0.8% throughput decrease, when frame loss rate is 20%. We implemented deployed and evaluated Musubi. Experiment results show that Musubi reduces frame loss by 70% and achieves throughput gain up to 1.4amp;#x000D7; as well as packet delay decreasing by 34% compared with the legacy Wi-Fi.

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.

Loudspeakers are widely used in conferencing and infotainment systems. Private information leakage from loudspeaker sound is often assumed to be preventable using sound-proof isolators like walls. In this paper, we explore a new acoustic eavesdropping attack that can subvert such protectors using radio devices. Our basic idea lies in an acoustic-radio transformation (ART) algorithm, which recovers loudspeaker sound by inspecting the subtle disturbance it causes to the radio signals generated by an adversary or by its co-located WiFi transmitter. ART builds on a modeling framework that distills key factors to determine the recovered audio quality. It incorporates diversity mechanisms and noise suppression algorithms that can boost the eavesdropping quality. We implement the ART eavesdropper on a software-radio platform and conduct experiments to verify its feasibility and threat level. When targeted at vanilla PC or smartphone loudspeakers, the attacker can successfully recover high-quality audio even when blocked by sound-proof walls. On the other hand, we propose several pragmatic countermeasures that can effectively reduce the attacker's audio recovery quality by orders of magnitude.

Poor scalability is a long standing problem in multi-user MIMO (MU-MIMO) networks: in order to select concurrent uplink users with strong channel orthogonality and thus high total capacity, channel state information (CSI) feedback from users is required. However, when the user population is large, the overhead from CSI feedback can easily overwhelm the actual channel time spent on data transmission. Moreover, due to spontaneous uplink traffic, uplink user selection cannot rely on the access point's central assignment and needs a distributed realization instead, which makes the problem even more challenging.

As video applications are increasingly popular over smartphones, many cooperative video streaming mechanisms have been proposed. These mechanisms use cellular link as well device-to-device links simultaneously to provide higher quality video streaming to mobile users. However current works solely focus on throughput enhancement in static scenarios. Consequently these mechanisms result in unfairness since smartphones with higher download rate expend more cellular traffic and monetary costs. Additionally, previous works assume a static scenario that all smartpone users start to watch the same video at the same time. Obviously, the static scenario is unrealistic in actual mobile environments. Based on these insights, in this paper, we focus on a more practical dynamic cooperation scenario and propose a scheduling scheme to achieve efficient cooperative video streaming and guarantee fluent user experience. More importantly, the proposed scheduling scheme achieves a significant improvement in fairness among cooperators. Through extensive simulations across a wide range of scenarios, we show that the proposed scheme significantly outperforms other works by 52%, 24% and 27% respectively in terms of fairness, without sacrificing efficiency.

Recent studies reveal that the main cause of the well-known unfairness problem in wireless networks is the ineffective coordination of CSMA-based random access due to topology asymmetry. In this paper, we take a modeling-based approach to understand and solve the unfairness problem. Compared to existing works, we advance the state of the art in two important ways. First, we propose an analytical model called the G-Model, which accurately characterizes the ineffective coordination of medium access in asymmetrical topologies. The G-Model can estimate network performance under arbitrary parameter configurations. Second, while previous works decompose a wireless network into embedded basic asymmetric topologies and study each basic topology separately, we go beyond the basic asymmetrical topology and design a model-driven optimization method called Flow Level Adjusting (FLA) to solve the unfairness problem for larger wireless networks. Through extensive simulations, we validate the proposed G-Model and show that FLA can greatly improve the overall fairness of wireless networks in which basic asymmetric topologies are embedded.

One major problem of cross-layer control algorithms in multi-hop wireless networks is that they lead to large end-to-end delays. Recently there have been many studies devoted to solving the problem to guarantee order-optimal per-flow delay. However, these approaches also bring the adverse effect of sacrificing a lot of network utility. In this paper, we solve the large-delay problem without sacrificing network utility. We take a fundamentally different approach of delay differentiation, which is based on the observation that flows in a network usually have different requirements for end-to-end delay. We propose a novel joint rate control, routing and scheduling algorithm called CLC_DD, which ensures that the flow delays are proportional to certain pre-specified delay priority parameters. By adjusting delay priority parameters, the end-to-end delays of preferential flows achieved by CLC_DD can be as small as those achieved by delay-order-optimal algorithms. In contrast to high network utility loss in previous approaches, we prove that our approach achieves maximum network utility. Furthermore, we incorporate opportunistic routing into the cross-layer design framework to improve network performance under the environment of dynamic wireless channels.

Centralized WLANs widely deployed in enterprise environment or university campus often have high density of Access Point (AP). The high density leads to multi-AP diversity, which brings possibility to improve network performance. Previ ous studies have proposed different schemes to exploit multi-AP diversity, however, these schemes are all based on heuristic and cannot guarantee an optimal exploitation of multi-AP diversity. In this paper, we propose a Theory Based Centralized Scheduling (TBCS) to exploit the full potential of multi-AP diversity. TBCS is based on the well-known back-pressure scheduling. Although back-pressure scheduling is proved to be throughput-optimal, most of previous studies are purely theoretical. To make a practical use of the theoretical back-pressure scheduling, we design new mechanisms in TBCS to handle the problem caused by the wired/wireless mixed scenario of centralized WLANs and to synchronize the scheduling. We evaluate TBCS through NS 2 simulations and show that compared with previous methods, TBCS can support the largest capacity region and greatly improves the throughput of a network.

In wireless mesh networks with frequency-agile radios, an algorithm of dynamically combining consecutive channels has recently been proposed. However, the available channel widths are limited in the algorithm. In order to further improve the fairness or the throughput under given fairness, we propose a joint variable width spectrum allocation and link scheduling optimization algorithm. Our algorithm is composed of time division multiple access for no interface conflict and frequency division multiple access for no signal interference. In the first phase, we use as few time slots as possible to assign at least one time slots to each radio link with Max-Min fairness. In the second phase, our design jointly allocates the lengths of time slots as well as the spectral widths and center frequencies of radio links in each time slot. Numerical results indicate that compared to the existing algorithm, our algorithm significantly increases the fairness or the throughput under given fairness.

As is known to us, the handover latency of FMIPv6 in its predictive mode is given little concerns. However our previous work [4] shows that FMIPv6 may suffer long handover latency in its predictive mode, and [4] identifies three key issues raising such problems. In this paper, we propose a practical cross-layer fast handover management mechanism (PCLF) to address these issues and improve success rate of mobility prediction. To solve the problem, PCLF includes a smart link layer trigger, a TBScan algorithm, a TBAPS algorithm, a buffering support Bi-Binding scheme and the smart link event notification policy. Experiment results show that our mechanism can achieve reasonable mobility prediction and seamless handover with no interruptions on upper layer applications (VoIP) in IEEE 802.11 WLANs. The average handover latency is less than 50ms, the success rate of mobility prediction is 97.7% and no packet loss is observed.

Most existing soft handoff approaches lead to plenty of out-of-order packets during downward vertical handoffs (VHOs). We have presented a soft VHO scheme, called SHORDER, to avoid packet reordering caused by downward VHOs. In this paper, we analyze the effects of our SHORDER scheme and another typical existing soft VHO method on the handoff latency and the received data size during a downward VHO for TCP applications. Then, we approximately derive the applicable conditions of the two approaches, and further propose a diagnosis-based soft vertical handoff (DSVH) mechanism which can self-adaptively deal with reordering packets. The mechanism has practical advantages of no changes to correspondent nodes and compatibility with various enhanced TCP variants. With numerical analysis and test-bed experiments, we show that the DSVH mechanism has better performance than the SHORDER scheme and the typical existing method. Furthermore, experimental and analytical results are consistent with each other.

The information of available bandwidth on an end-to-end path is important for various network applications, and several probing methods have been proposed to estimate it in recent years. However, previous methods are either based on fluid model or are only partially suitable for bursty real internet cross traffic; and the accuracy of their estimation degrades at different extents in multi-hop situations. Moreover, all previous PGM (Probing Gap Model) based methods require the knowledge of bottleneck link capacity, which may not be available in practice. In this paper, we extend the analysis of queuing behavior of probing packets from single-hop scenarios to multi-hop scenarios and propose a novel hybrid probing technique, called PATHCOS++, which integrates the advantages of both PRM (Probing Rate Model) and PGM based methods, to estimate the end-to-end available bandwidth. Unlike previous works, PATHCOS++ does not make fluid cross traffic assumption and does not require the information about bottleneck link capacity. Simulation results show that PATHCOS++ is quite efficient and provides end-to-end available bandwidth estimation that is significantly more accurate than current state-of-the-art techniques do. The accuracy of PATHCOS++ is nearly unaffected when there are multiple congestible links.

Previous Probe Gap Model (PGM) based available bandwidth (AB) estimation methods all request the "busy assumption" that probing packet pairs should be in the same busy period when transmitted on bottleneck link, which is hard to satisfy especially for the low utilization path. In this paper, we first present a new probabilistic methodology to estimate AB under "non busy assumption". The methodology is quite accurate on the low utilization network path. Secondly, we propose a metric to weigh the busyness of a network path based on the distribution of output probe gap. Using the metric, we combine our new methodology and previous methodology, and present a new AB estimation method called Adaptive Available Bandwidth Estimation (A_ABE) which is fit for both low utilization and high utilization paths. We use NS-2 simulation and reproduce traffic from real Internet links to evaluate A_ABE. Compared with previous methods, A_ABE shows its advantages in terms of accuracy, overhead, and also the robustness when confronted with non-persistent cross traffic in multiple hop situations.