With the rapid growth of IoT and AIoT applications, the demand for customized hardware is surging, yet PCB design for IoT devices remains a heavily manual, GUI-centric process that requires significant expertise in circuit and tools. This creates a widening gap between the accelerated software development and the difficulty of realizing corresponding hardware, making PCB-based hardware a critical bottleneck for system innovation. We present IoTGen, an LLM-driven agentic system that generates IoT PCB designs directly from natural-language demands. IoTGen introduces semantic-rich programming abstractions that capture schematic construction, enabling a domain-specialized language model to synthesize schematics with code. It further integrates a semantic component retrieval algorithm that combines LLM understanding with vector-based search, and an LLM-guided hybrid layout procedure that coordinates auto-routing tools to produce PCB layouts suitable for fabrication. We evaluate IoTGen on a dataset of diverse IoT designs, achieving a high component matching accuracy of 90%, a schematic generation success rate of 82%, and significant layout improvement. Case studies further demonstrate its capability to generate IoT PCB designs while substantially reducing the human effort and domain expertise required for hardware development. We release IoTGen to facilitate further research.

Precision agriculture demands continuous and accurate monitoring of soil moisture M and key macronutrients, including nitrogen N, phosphorus P, and potassium K, to optimize yields and conserve resources. Wireless soil sensing has been explored to measure these four components; however, current solutions require recalibration (i.e., re-train the data processing model) to handle variations in soil texture (characterized by aluminosilicates Al and organic carbon C, limiting their practicality. To address this, we introduce SoilX, a calibration-free soil sensing system that jointly measures six key components: {M, N, P, K, C, Al}. By explicitly modeling C and Al, SoilX eliminates texture- and carbon-dependent recalibration. SoilX incorporates Contrastive Cross-Component Learning (3CL), with two customized terms: the Orthogonality Regularizer and Separation Loss, to effectively disentangle cross-component interference. Additionally, we design a novel Tetrahedral Antenna Array with an antenna-switching mechanism, which can robustly measure soil dielectric permittivity independent of device placement. Extensive experiments demonstrate that SoilX reduces estimation errors by 23.8% to 31.5% over baselines and generalizes well to unseen fields.

We present MetaPolar, a fully passive mmWave metasurface tag that enables robust Infrastructure-to-Vehicle (I2V) communication using standard automotive radars. Unlike conventional backscatter or spatial-encoding approaches, MetaPolar exploits the polarization domain: engineered split-ring resonators rotate the incident polarization to generate strong cross-polarized returns, while a crossed-dipole layer independently sculpts co- and cross-polarized amplitudes and phases. Through full-wave simulation, we build a codebook mapping metasurface layouts to distinct 2 × 2 polarimetric RCS signatures. A novel semi-retroreflective beam-shaping design maintains strong polarimetric contrast across a ± 7.5° field of view, ensuring reliable decoding under realistic vehicle motion. We implement MetaPolar on low-cost FR-4 PCBs (< $3 per tag) and validate it with a custom mmWave radar platform. Experiments demonstrate 2-bit encoding per tag (scalable to 2N bits with N tags), simultaneous multi-tag reading, and seamless integration into existing radar pipelines. Our results highlight MetaPolar’s potential for cost-effective, scalable radar-readable signage in next-generation intelligent transportation systems.

Millimeter wave (mmWave) networks offer multi-gigabit data rates but suffer from severe path loss and blockage, resulting in spotty coverage. Emerging reconfigurable intelligent surfaces (RIS) can mitigate these challenges, but their reliance on active control channels, power sources, and complex runtime coordination imposes significant hardware and deployment overhead. This paper introduces FlowForm, a system that expands mmWave coverage using networks of passive metasurfaces that require no power, control, or runtime coordination. FlowForm's key innovation is a hierarchical flow topology that organizes passive metasurfaces into major flows (directional relay chains using near-field focusing) and minor flows (wide-area fan beams), enabling multi-hop passive routing and over-the-air combination of analog signals. We develop a theoretical framework establishing the optimality of this topology and a hierarchical optimization algorithm that jointly determines metasurface placement and beam configurations. FlowForm operates transparently with standard mmWave network protocols, managing channel dynamics and multi-user interference through diversity-aware design rather than runtime reconfiguration. Our experimental evaluation across five indoor environments demonstrates up to 94% average rate improvement and 114% coverage expansion using low-cost 3D-printed metasurfaces ($2 per unit), achieving performance comparable to active RIS at orders of magnitude lower cost.

The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these tools possess unique workflows, integrating multiple information sources as input and prioritizing code suggestions over natural language interaction, which introduces distinct security challenges. Additionally, LCCTs often rely on proprietary code datasets for training, raising concerns about the potential exposure of sensitive data. This paper exploits these distinct characteristics of LCCTs to develop targeted attack methodologies on two critical security risks: jailbreaking and training data extraction attacks. Our experimental results expose significant vulnerabilities within LCCTs, including a 99.4% success rate in jailbreaking attacks on GitHub Copilot and a 46.3% success rate on Amazon Q. Furthermore, We successfully extracted sensitive user data from GitHub Copilot, including 54 real email addresses and 314 physical addresses associated with GitHub usernames. Our study also demonstrates that these code-based attack methods are effective against general-purpose LLMs, highlighting a broader security misalignment in the handling of code by modern LLMs. These findings underscore critical security challenges associated with LCCTs and suggest essential directions for strengthening their security frameworks.

Student simulation supports educators to improve teaching by interacting with virtual students.However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data.To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time.Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors.Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data.Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a "digital twin" for online education.

Radio frequency (RF) propagation modeling poses unique electromagnetic simulation challenges. While recent neural representations have shown success in visible spectrum rendering, the fundamentally different scales and physics of RF signals require novel modeling paradigms. In this paper, we introduce RFScape, a novel framework that bridges the gap between neural scene representation and RF propagation modeling. Our key insight is that complex RF-object interactions can be captured through object-centric neural representations while preserving the composability of traditional ray tracing. Unlike previous approaches that either rely on crude geometric approximations or require dense spatial sampling of entire scenes, RFScape learns perobject electromagnetic properties and enables flexible scene composition. Through extensive evaluation on real-world RF testbeds, we demonstrate that our approach achieves 13 dB improvement over conventional ray tracing and 5 dB over state-of-the-art neural baselines in modeling accuracy, while requiring only sparse training samples.

Using deep neural networks as computational models to simulate cognitive processes can provide key insights into human behavioral dynamics. Challenges arise when environments are highly dynamic, obscuring stimulus-behavior relationships. However, the majority of current research focuses on simulating human cognitive behaviors under ideal conditions, neglecting the influence of environmental disturbances. We propose CogReact, which integrates drift-diffusion with deep reinforcement learning to simulate granular effects of dynamic environmental stimuli on the human cognitive process. Quantitatively, it improves cognition modeling by considering the temporal effect of environmental stimuli on the cognitive process and captures both subject-specific and stimuli-specific behavioral differences. Qualitatively, it captures general trends in the human cognitive process under stimuli. We examine our approach under diverse environmental influences across various cognitive tasks. Overall, it demonstrates a powerful, data-driven methodology to simulate, align with, and understand the vagaries of human cognitive response in dynamic contexts.

Although DRL (deep reinforcement learning) has emerged as a powerful tool for making better decisions than existing hand-crafted communication protocols, it faces significant limitations: 1) Selecting the appropriate neural network architecture and setting hyperparameters are crucial for achieving desired performance levels, requiring domain expertise. 2) The decision-making process in DRL models is often opaque, commonly described as a ‘black box’. 3) DRL models are data hungry. In response, we propose CP-AgentNet, the first framework to employ generative agents as autonomous decision-makers for communication protocol design. This approach addresses these challenges by creating an autonomous system for protocol design, significantly reducing human effort. As practical use cases, we developed LLMA (LLM-agents-based multiple access) and CPTCP (CP-Agent-based TCP) tailored for heterogeneous environments. Our comprehensive simulations have demonstrated the efficient coexistence of LLMA and CPTCP with nodes using different types of protocols, as well as enhanced explainability.

Mobile Virtual Reality (VR) is essential for achieving convenient and immersive human-computer interaction and realizing emerging applications such as Metaverse and spatial computing. However, existing VR technologies require two separate renderings of binocular images, thereby causing a significant bottleneck for mobile devices with limited computing and battery capacity. This paper proposes a new approach to optimizing mobile VR rendering called YORO. By utilizing the per-pixel attribute, YORO can generate binocular VR images from the monocular image through genuinely one rendering, saving half the computation over conventional approaches. Our experimental evaluation and detailed user study indicate that, YORO can save 27% power consumption on average and increase frame rate by 115.2%, while maintaining similar binocular image quality compared with state-of-the-art mobile VR rendering solutions. YORO is production-ready and has already been tested in real VR applications. The source code, demo video, prototype android app, video game engine plugins, and more are released anonymously at YORO-VR.github.io.

Vibrometry-based side channels pose a significant privacy risk, exploiting sensors like mmWave radars, light sensors, and accelerometers to detect vibrations from sound sources or proximate objects, enabling speech eavesdropping. Despite various proposed defenses, these involve costly hard-ware solutions with inherent physical limitations. This paper presents EveGuard, a software-driven defense framework that creates adversarial audio, protecting voice privacy from side channels without compromising human perception. We leverage the distinct sensing capabilities of side channels and traditional microphones-where side channels capture vibrations and microphones record changes in air pressure, resulting in different frequency responses. EveGuard first proposes a perturbation generator model (PGM) that effectively suppresses sensor-based eavesdropping while maintaining high audio quality. Second, to enable end-to-end training of PGM, we introduce a new domain translation task called Eve-GAN for inferring an eavesdropped signal from a given audio. We further apply few-shot learning to mitigate the data collection overhead for Eve-GAN training. Our extensive experiments show that EveGuard achieves a protection rate of more than 97% from audio classifiers and significantly hinders eaves-dropped audio reconstruction. We further validate the performance of EveGuard across three adaptive attack mechanisms. We have conducted a user study to verify the perceptual quality of our perturbed audio.

Figure 1: UltraPoser enables ubiquitous full-body pose estimation by integrating ultrasound sensing and IMU using commodity wearable devices.In addition to measuring IMU data, a smartphone and smartwatch are used to transmit and receive ultrasound signals.The extracted ultrasound features capture motions from joints without any attached devices and offer drift-free range measurements to complement IMU data for more accurate pose estimation.

After over a decade of intensive research, wireless sensing technology is nearing commercialization. However, the inherent openness of the wireless medium exposes this technology to security flaws and vulnerabilities. In this paper, we introduce RISiren to reveal the risk. RISiren is a pioneering end-to-end black-box attack system leveraging programmable metasurface with a high level of stealthiness. The key insight of RISiren lies in its ability to generate malicious multipath using metasurface, thereby disrupting wireless channel metrics influenced by genuine human activities and facilitating malicious attacks. To ensure the effectiveness of RISiren, we propose a novel metasurface configuration strategy aiming at creating human-like activities that stem from a comprehensive analysis of how human activities impact wireless signal propagation. We have implemented and validated RISiren using commercial Wi-Fi devices. Our evaluation involved testing our attack strategies against five state-of-the-art systems (including five different types of recognition frameworks) representative of the current landscape. The experimental results show that the adversarial wireless signals generated by RISiren achieve over 90% attack success rate on average, and remain robust and effective across different environments and deployment setups, including through wall attack scenarios.

Human external biofluid (e.g., sweat, urine) contains vast health data that is readily harvestable. Currently, wearable sweat sensors require an electrochemical-based approach that is used in single use, creating environmental pollution as people track their exercise in the wild. Moreover, such solution relies on a battery-powered design, which brings battery health and thermal related issues. We present MetaBioLiq, a 3D printed wireless-readable sweat sensing system that offers continuous monitoring, featuring completely passive, environmentally friendly, and easily accessible. MetaBioLiq is developed upon sweat liquid's resonance upon high frequency RF interaction, with different sweat content driving RF resonance characteristics. To activate such resonance, we design 3D PolyLactic Acid (PLA) structures that capture e-field energy from the air, and tunneling it to the sweat. Once the resonance effect occurs, we analyze return signal from a wireless RF receiver to decouple the sweat's resonance. Lastly, we evaluate MetaBioLiq's performance with 24 artificial sweat samples containing different levels of glucose, electrolytes, and fat. MetaBioLiq proves its effectiveness with 95% liquid level detection performance, and 96% sweat liquid identification performance. We further investigate MetaBioLiq's robustness and reliability, as well as limitations. Overall, MetaBioLiq shows promising results to expand the realm of mobile continuous sensing to microscopic realm untangible in the past.

Millimeter wave (mmWave) links offer high-bandwidth connectivity for next-generation wireless networks but face challenges in interference management and performance prediction. This paper introduces DDS, a novel hybrid data-driven simulator that accurately predicts link throughput distribution in mmWave networks. DDS leverages readily available physical (PHY) layer measurements and employs a deep reinforcement learning (DRL) framework to interpret the PHY environment and determine appropriate parameters for subsequent simulations. The system integrates a DRL-based parameter tuner with a PHY and MAC layer simulator, bridging the gap between simulation and real-world performance. We conduct comprehensive evaluations of DDS, demonstrating its superior accuracy compared to its baselines. Our experiments validate DDS's effectiveness in enhancing network controller training and deriving optimal network configuration policies in dense mmWave deployments.

The complexity and volatility of real-world radio environments often hamper wireless networks from achieving optimal performance. Recently, intelligent metasurfaces have been explored to dynamically reshape the radio propagation environment. However, existing systems are limited to standalone metasurfaces, only enabling one-time signal redirection/reshaping effects within their direct line-of-sight. They cannot effectively scale to cover larger areas. In this paper, we propose RFMagus, which employs a network of metasurfaces to overcome the limitation. We carefully optimize the configurations of the networked metasurfaces so that they can cooperatively and coherently propagate the analog signals towards the target regions. We have implemented the networked metasurfaces and deployed them in a variety of real-world environments. Experimental results demonstrate that RFMagus can effectively expand the coverage, improve the throughput, and operate transparently to different wireless standards.

The advent of 5G promises high bandwidth with the introduction of mmWave technology recently, paving the way for throughput-sensitive applications. However, our measurements in commercial 5G networks show that frequent handovers in 5G, due to physical limitations of mmWave cells, introduce significant under-utilization of the available bandwidth. By analyzing 5G link-layer and TCP traces, we uncover that improper interactions between these two layers causes multiple inefficiencies during handovers. To mitigate these, we propose M2HO, a novel device-centric solution that can predict and recognize different stages of a handover and perform state-dependent mitigation to markedly improve throughput. M2HO is transparent to the firmware, base stations, servers, and applications. We implement M2HO and our extensive evaluations validate that it yields significant improvements in TCP throughput with frequent handovers.

Millimeter-wave (mmWave) radar has become instrumental in diverse consumer applications. Yet current radar architectures face major limitations. While full-MIMO structures are feature-rich, their cost and complexity rise rapidly with more antennas. Phased-MIMO radars promise enhanced scalability by combining large phased arrays with a small number of RF chains. Nevertheless, the phased-MIMO research thus far primarily relies on simulation or theoretical analysis. In this paper, we introduce HybRadar, a novel programmable phased-MIMO radar platform to address this experimental gap. HybRadar repurposes the phased arrays on a low-cost 802.11ad radio to create a scalable low-cost array of phased subarrays. It further incorporates transmit/receive front-end, control channel, and hardware synchronization mechanisms to enable a modular phased-MIMO system. By extending recent MIMO array synthesis models, we optimize the placement of phased subarrays to maximize the spatial resolution. Our prototype validation and case studies confirm the capability and versatility of HybRadar.

Accurate and responsive simulation of radio frequency (RF) signal propagation is crucial for designing wireless systems operating in dynamic environments. Conventional ray tracing approaches struggle to accurately model the intricate geometries and material properties of objects that impact propagation. Recently proposed neural scene representations can learn such intricacies from RF data, but they treat the entire scene as implicit neural networks, necessitating retraining with a massive amount of RF data upon any environmental changes. In this paper, we propose RFCanvas, which fuses visual priors and RF measurements to achieve high accuracy for realistic scenes and be responsive to environmental changes. To ensure compatibility between visual priors and RF measurements, we introduce RFCanvas scene representations that model shapes and materials of substantial objects with tensorial fields and signed distance fields. We further extract motion information from visual priors to adapt RFCanvas scene representations to scene dynamics. RFCanvas is built upon an end-to-end optimization framework with differentiable RF simulation. Extensive evaluations across real-world wireless communication and sensing environments demonstrate RFCanvas's superiority over both existing methods.

Soil moisture level sensing is essential for enabling smart irrigation, which is crucial for our food security and sustainable agriculture. Existing soil moisture sensing systems face limitations such as single-layer sensing, limited depth, power supply reliance, and complex calibration. In addition, costly and cumbersome sensor unit design hinders mass and dense deployment of passive intelligence. This paper introduces MetaSoil, a soil moisture sensing system that is calibration-free, continuous, and multi-layered, leveraging a passive 3D printable mmWave metamaterial. When soil moisture level changes, our hydrogel patched polylactic acid (PLA) metamaterial alters resonant frequency in the impinging mmWave signals due to impedance match offset. Our system eliminates in-soil power supply dependencies by utilizing the RF resonance of 3D-printed metamaterial, allowing for deeper placement, and simultaneous multi-layer sensing. We then integrate a commercial-off-the-shelf (COTS) mmWave radar to query the metamaterial sensor. With MetaSoil's fully passive metamaterial pole, RF signal from far is redirected towards the sensor unit, bypassing soil's heavy attenuation effect. Through our extensive evaluation, MetaSoil achieves 98.9 % accuracy with ±10% moisture level precision in single-layered sensing, at depth of 1m meter. It achieves 98.8 % accuracy with ±10% in double layered sensing at same depth with 10cm sensor spacing. We further examine the robustness of our system with real-world requirements. Overall, MetaSoil represents a low-cost, durable, and easily deployable solution that supports remote and continuous soil moisture monitoring, advancing the scalability and effectiveness of smart agricultural practices.

Reliable cross medium (e.g., air-water) communication using radio frequency (RF) has remained an open-problem for decades. Currently, underwater devices cannot communicate directly with land-based or airborne devices. Typical solutions are inadequate when communicating through the boundary due to cross-medium boundary reflection/refraction/attenuation effects. We present MetaLink, an RF wireless communication system that enables underwater radios to communicate with airborne ones using novel underwater antenna design and 3D printed bianisotropic metasurface. MetaLink leverages bianisotropic structures that can correct for the severe boundary reflections/refractions between the air/water mediums, opening up the air/water medium as a viable communication channel without the need for multiple types of signals. We further exploit the electromagnetic properties of water to drastically scale down MetaLink's meta-atom size, and improve communication range. To examine real world communications performance from water to air, we prototype MetaLink and measure in a 14 ft deep swimming pool. Moreover, we push the robustness, reliability, and performance of MetaLink to its limit under various real-world circumstances. Our experiments demonstrate that MetaLink can communicate through the water/air boundary with SNR improvements of more than 35dB using WiFi modulation at distances of 14 ft and reach a simulated maximum of 95 ft within water using commercially available equipment and measured data.

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) to provide adaptive time pressure feedback to improve user performance in a math arithmetic task. Time pressure feedback could either improve or deteriorate user performance by regulating user attention and anxiety. Adaptive time pressure feedback controlled by a DRL policy according to users’ real-time performance could potentially solve this trade-off problem. However, the DRL training and hyperparameter tuning may require large amounts of data and iterative user studies. Therefore, we propose a dual-DRL framework that trains a regulation DRL agent to regulate user performance by interacting with another simulation DRL agent that mimics user cognition behaviors from an existing dataset. Our user study demonstrates the feasibility and effectiveness of the dual-DRL framework in augmenting user performance, in comparison to the baseline group.

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.

Millimeter-wave backscatter can simultaneously support high-precision sensing and massive communication and represent one prominent technical evolution in next-generation wireless systems. The backscatter tags should ideally work across a wide mmWave spectrum range with consistent signal strength and angular coverage to accommodate highly diverse application scenarios. However, existing tags made of resonant antennas and RFICs only achieve a few GHz of bandwidth and hardly meet these requirements. In this paper, we present UniScatter, a new backscatter tag structure based on metamaterials. The key design of UniScatter is a graphene-based modulator and a lens-based retroreflector, which have consistent electromagnetic responses across an extensive frequency range and wide angular field-of-view. We have developed a robust fabrication process for UniScatter, and tested it on various mmWave sensing and communication devices. Our field tests show that UniScatter can backscatter signals across a wide frequency band from 24 GHz to 77 GHz with consistently high signal strength and wide angular coverage in 3D space.

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. This paper proposes NeuroRadar, a neuromorphic radar sensor, to achieve low-power wireless sensing. NeuroRadar jointly optimizes the analog hardware and the computation model, in order to mimic the highly efficient biological sensing and neural processing system. NeuroRadar features a highly simplified radar front end, which eliminates the power-hungry components in conventional radars. It directly "encodes" ambient motion into spiking signals, which can be processed using spiking neural networks running on energy-efficient neuromorphic computing platforms. We have prototyped NeuroRadar and evaluated its performance in two use cases: gesture sensing and localization. Our experiments demonstrate that NeuroRadar can achieve high sensing accuracy, at orders of magnitude lower power consumption compared with traditional radar.

This paper presents RF Genesis (RFGen), a novel and cost-effective method for synthesizing RF sensing data using cross-modal diffusion models, in order to improve the generalization capability of millimeter-wave (mmWave) sensing systems. Traditional machine learning models used in mmWave sensing struggle with limited training datasets. Their performance degrades drastically when confronted with unseen users, environments, sensor configurations, test classes, etc. RFGen mitigates these challenges by using a cross-modal generative framework to synthesize and expand mmWave sensing data. We specifically propose a custom ray tracing simulator to simulate RF propagation and interaction with objects/environments. We then leverage a set of diffusion models to generate massive 3D scenes, and transform the visual scene representation into the corresponding mmWave sensing data, under the direction of application-specific "prompts". Our proposed approach reconciles the physics-based ray tracing with the blackbox diffusion model, leading to accurate, scalable, and explainable vision-to-RF data synthesis. Our extensive real-world experiments highlight RFGen's effectiveness in diverse mmWave sensing applications, enhancing their generalization to unseen test cases without laborious data collection.

A large number of traffic collisions occur as a result of non-line-of-sight (NLoS) obstructions. Recent work has explored NLoS automotive radar sensing systems to detect objects in occluded regions. However, current NLoS radars require substantial ambient reflectors, whose size needs to scale with the desired angular resolution and coverage, impeding their deployment in real-world scenarios. In this paper, we propose Metasight, which leverages carefully designed passive millimeter-wave metasurface reflectors and a novel angular encoding scheme to dramatically reduce the reflector size. The Metasight metasurfaces are fully passive, low cost, and can be fabricated by simply using a 3D printer and copper tape. By processing the reflected signals with a robust angle decoding algorithm on the radar, Metasight achieves high NLoS sensing resolution and wide coverage, with an asymptotically higher space-efficiency than conventional natural or artificial reflectors.

Phased array beamforming has been extensively explored as a physical layer primitive to improve the secrecy capacity of wireless communication links. However, existing solutions are incompatible with low-profile IoT devices due to cost, power and form factor constraints. More importantly, they are vulnerable to eavesdroppers with a high-sensitivity receiver. This paper presents Protego, which offloads the security protection to a metasurface comprised of a large number of 1-bit programmable unit-cells (i.e., phase shifters). Protego builds on a novel observation that, due to phase quantization effect, not all the unit-cells contribute equally to beamforming. By judiciously flipping the phase shift of certain unit-cells, Protego can generate artificial phase noise to obfuscate the signals towards potential eavesdroppers, while preserving the signal integrity and beamforming gain towards the legitimate receiver. A hardware prototype along with extensive experiments has validated the feasibility and effectiveness of Protego.

Next generation wireless networks embrace mmWave technology for its high capacity. Yet, mmWave radios bear a fundamental coverage limitation due to the high directionality and propagation artifacts. In this paper, we explore an economical paradigm based on 3D printing technology for mmWave coverage expansion. We propose MilliMirror, a fully passive metasurface, which can reshape and resteer mmWave beams to anomalous directions to illuminate the coverage blind spots. We develop a closed-form model to efficiently synthesize the MilliMirror design with thousands of unit elements and across a wide frequency band. We further develop an economical process based on 3D printing and metal deposition to fabricate MilliMirror. Our field test results show that MilliMirror can effectively fill the coverage holes and operate transparently to the standard mmWave beam management protocols.

This demonstration presents a working prototype of MilliMirror. This fully passive metasurface expands coverage blind spots of mmWave radios by reshaping and re-steering mmWave signals to any anomalous directions. The MilliMirror prototype consists of thousands of unit elements. A closed-form model is developed for efficient beam pattern synthesis. MilliMirror further explores 3D printing technology and metal deposition to achieve economical fabrication. MilliMirror prototype successfully establishes an indirect link between the WiGig transceivers, with the maximum gain over 10 dB.

A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations, and enable NLoS radar sensing using an entirely new class of reflectors.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio [1]. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.

In a modern logistics network, high-performance automation of inventory tracking and package management calls for a reliable, high-throughput and long range RFID localization system. We present RF-Chord, the first RFID localization system that simultaneously meets all these requirements. RF-Chord features a one-shot multisine-constructed wideband design that can process the RF signal with a 200 MHz bandwidth in real-time to facilitate one-shot localization at scale. In addition, multiple SINR enhancement techniques are designed for range extension. Finally, we propose a kernel-layer-based near-field localization and a multipath-suppression algorithm that reduces the 99% long-tail errors.

Typical passive internet of things (IoT) localization systems, such as those based on UHF RFID, adopt narrow bandwidth signal and bind the localization function with energy harvesting and communication waveform. Due to the signal bandwidth and waveform constraints, the systems can not meet crucial requirements of practical IoT use cases. In this poster, we identify the fundamental challenges and analyze why the existing systems fall short. Based on the analysis, we propose to adopt dual band backscatter design and identify different design choices on frequency band, waveform and tag modulation. Finally, we build an ultra-wideband FMCW signal based prototype UWB2 to verify the feasibility of our proposal. Our results show that the system can achieve low tail error and realize one shot localization even under harsh multipath scenarios.

The increasing processing load of today’s mobile machine learning (ML) application challenges the stringent computation budget of mobile user equipment (UE). With the wide deployment of 5G edge-cloud, a new ML offloading scheme called split ML is provisioned to enable computation-intensive mobile ML applications by splitting an ML model across mobile UE, edge, and cloud. However, the complex split assignment problems pose new challenges for split ML system design. In this paper, we introduce HiveMind, the first practical multi-split ML system tailored for 5G cellular networks. HiveMind reformulates the complicated multi-split problem to a min-cost graph search and optimizes the distributed algorithm to drastically reduce the signaling overhead. Benefit from its low overhead property, HiveMind makes the optimal split decision on multiple computing nodes in real-time and adapts the split decisions to the instantaneous network dynamics. HiveMind also incorporates a multi-objective mechanism that accommodates heterogeneous objectives for a single ML task. HiveMind adapts to a wide range of ML frameworks, including non-linear models like Recurrent Neural Network (RNN), Federated Learning (FL), and Multi-agent Reinforcement Learning (MARL). We evaluate HiveMind on 5G MEC network simulators with realistic traffic patterns and real-life MEC computation/communication profiles. Our experiments demonstrate that HiveMind achieves the optimal efficiency comparing to state-of-art split ML designs.

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.

This paper presents a new privacy threat, the Invisible Infrared Shadow Attack (IRSA), which leverages the inconspicuous infrared (IR) light emitted by indoor security cameras, to reveal in-home human activities behind opaque curtains. The key observation is that the in-home IR light source can project invisible shadows on the window curtains, which can be captured by an attacker outside using an IR-capable camera. The major challenge for IRSA lies in the shadow deformation caused by a variety of environmental factors involving the IR source position and curtain shape, which distorts the body contour. A two-stage attack scheme is proposed to circumvent the challenge. Specifically, a DeShaNet model performs accurate shadow keypoint detection through multi-dimension feature fusion. Then a scene constructor maps the 2D shadow keypoints to 3D human skeletons by iteratively reproducing the on-site shadow projection process in a virtual Unity 3D environment. Through comprehensive evaluation, we show that the proposed attack scheme can be successfully launched to recover 3D skeleton of the victims, even under severe shadow deformation. Finally, we propose potential defense mechanisms against the IRSA.

Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort lights and significantly reduce visibility. Thus, existing methods relying on lidar or camera experience significant performance degradation in rare but critical adverse weather conditions. To remedy this, we resort to exploiting complementary radar, which is less impacted by adverse weather and becomes prevalent on vehicles. In this paper, we present Multimodal Vehicle Detection Network (MVDNet), a two-stage deep fusion detector, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results. To evaluate MVDNet, we create a procedurally generated training dataset based on the collected raw lidar and radar signals from the open-source Oxford Radar Robotcar. We show that the proposed MVDNet surpasses other state-of-the-art methods, notably in terms of Average Precision (AP), especially in adverse weather conditions. The code and data are available at https://github.com/qiank10/MVDNet.

Immersive face-to-virtual-face telecommunication is one unique use case for virtual reality (VR) technologies. Existing camera-based telephony systems cannot be used for such immersive VR video chat, due to the physical occlusions of head-mounted displays (HMDs) and/or unwieldy positioning of cameras. To address these, we present ExGSense, a new VR input modality that can sense and reconstruct both upper and lower facial gestures, by only using lightweight biopotential sensors embedded within the HMDs. We optimize the sensor arrangement based on facial anatomy and employ a multiview classification pipeline to exploit the multiple dimensions of signal features. We thus enable ExGSense to detect whole facial gestures by using a sparse set of biopotential transducers. We prototyped ExGSense and evaluated its performance with 42 facial gestures and across different users. We showed a 93% accuracy for user-specific evaluation, and 77% accuracy for user-independent evaluation with low calibration overhead. We believe ExGSense constitutes a promising input modality for immersive VR interactions.

Delay-sensitive multimedia streaming applications require their data to be delivered before a deadline to be useful. The data transmitted by these applications can usually be partitioned into blocks with different priorities, assigned based on the impact of a block on the Quality of Experience (QoE) if it misses its delivery deadline. Meet their deadline requirements is challenging due to the dynamics of the network and these applications' high demand on network resources. To encourage the research community to address this challenge, we organize the "Meet Deadline Requirements" Grand Challenge at ACM Multimedia 2021. This grand challenge provides a simulation platform onto which the participants can implement their block scheduler and bandwidth estimator and then benchmark against each other using a common set of application traces and network traces.

Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to "reshape" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.

Robust speech enhancement is considered as the holy grail of audio processing and a key requirement for human-human and human-machine interaction. Solving this task with single-channel, audio-only methods remains an open challenge, especially for practical scenarios involving a mixture of competing speakers and background noise. In this paper, we propose UltraSE, which uses ultrasound sensing as a complementary modality to separate the desired speaker's voice from interferences and noise. UltraSE uses a commodity mobile device (e.g., smartphone) to emit ultrasound and capture the reflections from the speaker's articulatory gestures. It introduces a multi-modal, multi-domain deep learning framework to fuse the ultrasonic Doppler features and the audible speech spectrogram. Furthermore, it employs an adversarially trained discriminator, based on a cross-modal similarity measurement network, to learn the correlation between the two heterogeneous feature modalities. Our experiments verify that UltraSE simultaneously improves speech intelligibility and quality, and outperforms state-of-the-art solutions by a large margin.

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).

mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

In order to meet the high data rate requirements of emerging roadway use cases, mmWave vehicular communications will be needed. This work studies the ability of vehicles to communicate with a Roadside Unit (RSU) placed at an intersection. Practical mmWave radios utilize a codebook, a discrete set of analog beams, that is periodically searched during runtime to find the optimal beam to use for each receiver. This search creates overhead as the wireless channel is not used for communication while this beam search is happening. This work focuses on reducing the overhead of beam training by optimizing the site-specific codebook design of a RSU. Owing to the sparsity of the mmWave channel and the user distribution for vehicles, it is found that 85% of beams can be removed from the codebook with zero-impact. By carefully selecting the usage of wide beams the codebook size can be further reduced to just 64 beams while still providing omni-directional coverage for an intersection. Other research thrusts have focused on attempting to augment or remove beam training entirely; however, this necessitates a change to the PHY layer. Codebook optimization achieves approximately 80% of the communications performance that would be achieved if beam training overhead could be completely removed while only requiring a radio configuration update. Thus, this work finds that today’s commercial mmWave radios are sufficient for deployments in RSUs. To validate the proposed codebook optimization algorithm, a detailed mmWave ray tracing framework that encompasses 3D environmental information and material properties of reflectors is developed.

Modern autonomous vehicles are commonly instrumented with radars for all-weather perception. Yet the radar functionality is limited to identifying the positions of reflectors in the environment. In this paper, we investigate the feasibility of smartening transportation infrastructure for the purpose of conveying richer information to automotive radars. We propose RoS, a passive PCB-fabricated smart surface which can be reconfigured to embed digital bits, and inform the radar much like visual road signs do to cameras. We design the RoS signage to act as a retrodirective reflector which can reflect signals back to the radar from wide viewing angles. We further introduce a spatial encoding scheme, which piggybacks information in the reflected analog signals based on the geometrical layout of the retroreflective elements. Our prototype fabrication and experimentation verifies the effectiveness of RoS as an RF ''barcode'' which is readable by radar in practical transportation environment.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances.

Millimeter-wave (mmWave) networking represents a core technology to meet the demanding bandwidth requirements of emerging connected vehicles. However, the feasibility of mmWave vehicle-to-everything (V2X) connectivity has long been questioned. One major doubt lies in how the highly directional mmWave links can sustain under high mobility. In this paper, we present the first comprehensive reality check of mmWave V2X networks. We deploy an experimental testbed to mimic a typical mmWave V2X scenario, and customize a COTS mmWave radio to enable microscopic investigation of the channel and the link. We further construct a high-fidelity 3D ray-tracer to reproduce the mmWave characteristics at scale. With this toolset, we study the mmWave V2X coverage, mobility and blockage, codebook/beam management, and spatial multiplexing. Our measurement debunks some common misperceptions of mmWave V2X networks. In particular, due to the constrained roadway network structures, we find the beam management can be handled easily by the often-denounced beam scanning schemes, as long as the codebook is properly designed. Blockage can be almost eliminated through proper basestation deployment and cooperation. Highly effective spatial multiplexing can be realized even without sophisticated MIMO radios. Our work points to possible ways to realize efficient and reliable mmWave networks under high mobility, while maintaining the simplicity of standard network protocols.

Millimeter-wave (mmWave) networks are conventionally considered to bear a fundamental coverage limitation, due to the directional beams and limited field-of-view (FoV) of the phased array antennas. In this paper, we explore an array of phased arrays (APA) architecture, which aggregates co-located phased arrays with complementary FoVs to approximate WiFi-like omni-directional coverage. We found that straightforwardly activating all the arrays may even hamper network performance. To fully exploit the APA's potential, we propose X-Array, which jointly selects the arrays and beams, and applies a dynamic co-phasing mechanism to ensure different arrays' signals enhance each other. X-Array also incorporates a link recovery mechanism to identify alternative arrays/beams that can efficiently recover the link from outage. We have implemented X-Array on a commodity 802.11ad APA radio. Our experiments demonstrate that X-Array can approach omni-directional coverage and maintain high performance in spite of link dynamics.

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) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. This paper fills the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 288 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. The key design principle behind M3 is to hijack a low-cost commodity 802.11ad radio, separate the control path and data path inside, regenerate the phased array control signals, and recreate the data signals using a programmable baseband. Extensive experiments have demonstrated the effectiveness of the M3 design, and its usefulness for research in mmWave massive MIMO communication and sensing.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.

Many human activities involve interactions with passive objects. By wirelessly sensing human interactions with such "things", one can infer activities at a fine resolution, enabling a new wave of ubiquitous applications. This forms the basis of the tangible user interface allowing individual to use omnipresent objects as a control interface to the digital world. Existing works have tendencies to create such interface with complicated circuitry, leading to overwhelm complexities. To conquer these, we propose the inkjet printable capacitive tags (CapTags), empowering a new paradigm of printable communications and sensing modality. We use discrete capacitive and inductive components to simulate the tag-interrogator system, and prove the feasibility of proposed hardware featurization and high frequency sweeping strategy where the information can be encoded in the resonating spikes. This enables the touch points to be detected by searching resonating detune effects. Although this work only includes the designs and simulations, we believe this new sensing modality would truly realize the vision of printable ubiquitous computing.

Voice assistants (VAs) are becoming highly popular recently as a general means of interacting with the Internet of Things. However, the use of always-on microphones on VAs imposes a looming threat on users' privacy. In this paper, we propose MicShield, the first system that serves as a companion device to enforce privacy preservation on VAs. MicShield introduces a novel selective jamming mechanism, which obfuscates the user's private speech while passing legitimate voice commands to the VAs. It achieves this by using a phoneme level jamming control pipeline. Our implementation and experiments demonstrate that MicShield can effectively protect a user's private speech, without affecting the VA's responsiveness.

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.

Current UHF RFID systems suffer from two long-standing problems: 1) miss-reading non-line-of-sight or misoriented tags and 2) cross-reading undesired, distant tags due to multi-path reflections. This paper proposes a novel system, NFC+, to overcome the fundamental challenges. NFC+ is a magnetic field reader, which can inventory standard NFC tagged objects with a reasonably long range and arbitrary orientation. NFC+ achieves this by leveraging physical and algorithmic techniques based on magnetic resonance engineering. We build a prototype of NFC+ and conduct extensive evaluations in a logistic network. Comparing to UHF RFID, we find that NFC+ can reduce the miss-reading rate from 23% to 0.03%, and cross-reading rate from 42% to 0, for randomly oriented objects. NFC+ demonstrates high robustness for RFID unfriendly media (e.g., water bottles and metal cans). It can reliably read commercial NFC tags at a distance of up to 3 meters which, for the first time, enables NFC to be directly applied to practical logistics network applications.

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.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand "landmarks'', especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, "overclocks'' its sampling rate to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96%) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.4%), and under various practical usage disturbances.

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.

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.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near-term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification, and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high-frequency (greater than 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-story office building, parking lot, and grocery store further validates LiTell as an accurate, robust, and ready-to-use indoor localization system.

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.

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.

Mobile virtual reality (VR) headsets, such as Google Cardboard and Samsung GearVR, can reuse a smartphone as near-eye display to create immersive experience. But such devices barely support any user interaction, even for simple tasks such as menu selection and single-character input. In this paper, we design Inkput, a simple passive interface attached to the unexploited backside of the headset to enable touch sensing. Inkput is a piece of paper substrate with carbon ink patterns printed atop. It leverages the column of electrodes near the edge of the smartphone touchscreen to sense multi-touch on the 2D space, and is even able to locate finger hovering. Our experiments demonstrate that Inkput can precisely detect touch positions with mm-level precision. Our case studies in actual VR applications also verify that Inkput can support common VR interactions and can even outperform high-end handheld controllers in terms of efficiency.

Millimeter-wave (mmWave) technology is emerging as the most promising solution to meet the multi-fold demand increase for mobile data. Very short wavelength, high directionality, together with sensitivity to rampant blockages and mobility, however, render state-of-the-art mmWave technologies unsuitable for ubiquitous wireless coverage. In this work, we design and implement UbiG - a mmWave wireless access network - that can deliver ubiquitous gigabits per second wireless access consistently to the commercial-off-the-shelf IEEE 802.11ad devices. UbiG has two key design components: (1) a fast probing based beam alignment algorithm that can identify the best beam consistently with guaranteed latency in a mmWave link, and the algorithm scales well even with a very large number of beams; and (2) an infrastructure-side predictive ranking based fast access point switching algorithm to ensure seamless gigabits per second connectivity under mobility and blockage in a dense mmWave deployment. Our IEEE 802.11ad testbed experiments show that UbiG performs close to an "Oracle" solution that instantaneously knows the best beam and access point for gigabits per second data transmission to users.

Panoramic or 360° video streaming has been supported by a wide range of content providers and mobile devices. Yet existing work primarily focused on streaming on-demand 360° videos stored on servers. In this paper, we examine a more challenging problem: Can we stream real-time interactive 360° videos across existing LTE cellular networks, so as to trigger new applications such as ubiquitous 360° video chat and panoramic outdoor experience sharing? To explore the feasibility and challenges underlying this vision, we design POI360, a portable interactive 360° video telephony system that jointly investigates both panoramic video compression and responsive video stream rate control. For the challenge that the legacy spatial compression algorithms for 360° video suffer from severe quality fluctuations as the user changes her region-of-interest (ROI), we design an adaptive compression scheme, which dynamically adjusts the compression strategy to stabilize the video quality within ROI under various user input and network condition. In addition, to meet the responsiveness requirement of panoramic video telephony, we leverage the diagnostic statistics on commodity phones to promptly detect cellular link congestion, hence significantly boosting the rate control responsiveness. Extensive field tests for our real-time POI360 prototype validate its effectiveness in enabling panoramic video telephony over the highly dynamic cellular networks.

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.

Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.

Despite years of innovative research and development, multi-Gbps 60 GHz wireless networks are still not mainstream. The unfavorable propagation characteristics due to short wavelength and high directionality, makes the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this demonstration, we will present MUST, an 802.11-compliant real-time system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead at 60 GHz; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. We have implemented MUST on off-the-shelf devices where our experiments show high throughput gain and almost 2 orders of magnitude cross-band switching latency improvement over state-of-the-art solutions.

60 GHz millimeter-wave networking has emerged as the next frontier technology to provide multi-Gbps wireless connectivity. However, the intrinsic directionality and limited field-of-view of 60 GHz antennas make the links extremely sensitive to user mobility and orientation change. Hence, seamless coverage, even at room level, becomes challenging. In this paper, we propose Pia, a robust 60 GHz network architecture that can provide seamless coverage and mobility support at multi-Gbps bitrate. Pia comprises multiple cooperating access points (APs). It leverages the pose information on mobile clients to proactively select the AP and manage multi-link spatial reuse. These decisions require a model of the pose/location of the APs and ambient reflectors. We address these challenges through a set of AP-pose sensing and compressive angle estimation algorithms that fuse the pose measurement with link quality measurement on the client. We have implemented Pia using commodity 60 GHz platforms. Our experiments show that Pia reduces the occurrence of link outage by 6.3x and improves the spatial sharing capacity by 76%, compared to conventional schemes that only use in-band information for adaptation.

The past decade's research in visible light positioning (VLP) has led to technologies with high location precision. However, existing VLP systems either require specialized LEDs which hinder large-scale deployment, or need cameras which preclude continuous localization because of high power consumption and short coverage. In this paper, we propose Pulsar, which uses a compact photodiode sensor, readily fit into a mobile device, to discriminate existing ceiling lights---either fluorescent lights or conventional LEDs---based on their intrinsic optical emission features. To overcome the photodiode's lack of spatial resolution, we design a novel sparse photogrammetry mechanism, which resolves the light source's angle-of-arrival, and triangulates the device's 3D location and even orientation. To facilitate ubiquitous deployment, we further develop a light registration mechanism that automatically registers the ceiling lights' locations as landmarks on a building's floor map. Our experiments demonstrate that Pulsar can reliably achieve decimeter precision in both controlled environment and large-scale buildings.

The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the cooperation of the photographer or camera hardware, which can hardly be guaranteed in practice. In this paper, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. Our prototype implementation and experiments show that LiShield can effectively destroy unauthorized capturing while maintaining robustness against potential attacks.

The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the compliance of the photographer or camera hardware, which can hardly be guaranteed in practice. In this demo, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in a specialized waveform. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. This demo will show our prototype implementation, with simple on-site experiments that demonstrate how LiShield effectively destroys unauthorized photo capturing.

Despite the 4G LTE's 10X capacity improvement over 3G, mobile Web loading latency remains a major issue that hampers user experience. The root cause lies in the inefficient transport-layer that underutilizes LTE capacity, due to high channel dynamics, wireless link losses, and insufficient application traffic to propel the bandwidth probing. In this paper, we propose Cellular Link-Aware Web loading (CLAW), which boosts mobile Web loading using a physical-layer informed transport protocol. CLAW harnesses the limited PHY-layer statistics available on LTE phones to quantitatively model the LTE channel resource utilization, which is then translated into a transport window that best fits the bandwidth. Consequently, CLAW can estimate and fully utilize the available bandwidth almost within one RTT. In addition, CLAW can precisely differentiate LTE wireless loss from congestion loss, and identify the rare cases when the wireline backhaul becomes the bottleneck. We have prototyped CLAW on commodity LTE phones. Across a wide range of experimental settings, CLAW consistently reduces Web loading latency by more than 30%, compared to classical TCP variants and state-of-the-art congestion controls for cellular networks.

For over one decade, research in visible light positioning has focused on using modulated LEDs as location landmarks. But the need for specialized LED fixtures, and the associated retrofitting cost, has been hindering the adoption of VLP. In this paper, we forgo this approach and design iLAMP to enable reliable, high-precision VLP using conventional LEDs and fluorescent lamps inside today's buildings. Our key observation is that these lamps intrinsically possess hidden visual features, which are imperceptible to human eyes, but can be extracted by capturing and processing the lamps' images using a computational imaging framework. Simply using commodity smartphones' front cameras, our approach can identify lamps within a building with close to 100% accuracy. Furthermore, we develop a geometrical model which combines the camera image with gyroscope/accelerometer output, to estimate a smartphone's 3D location and heading direction relative to each lamp landmark. Our field tests demonstrate a mean localization (heading) precision of 3 cm (2.6 degree) and 90-percentile 3.5 cm (2.8 degree), even if a single lamp falls in the camera's field of view.

The proliferation of smartphones and tablet devices is changing the landscape of user connectivity and data access from predominantly static users to a mix of static and mobile users. While significant advances have been made in wireless transmission strategies (e.g., beamforming and network MIMO) to meet the increased demand for capacity, such strategies primarily cater to static users. To cope with growing heterogeneity in data access, it is critical to identify and optimize strategies that can cater to users of various profiles to maximize system performance and more importantly, improve users' quality of experience. Toward this goal, we first show that users can be profiled into three distinct categories based on their data access (mobility) and channel coherence characteristics. Then, with real-world experiments, we show that the strategy that best serves users in these categories varies distinctly from one profile to another and belongs to the class of strategies that emphasize either multiplexing (e.g., network MIMO), diversity (e.g., distributed antenna systems) or reuse (e.g., conventional CSMA). Two key challenges remain in translating these inferences to a practical system, namely: 1) how to profile users and 2) how to combine strategies to communicate with users of different profiles simultaneously. In addressing these challenges, we present the design of TRINITY-a practical system that effectively caters to a heterogeneous set of users. We implement and evaluate a prototype of TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared with individual transmission strategies.

Full-duplex radio technology is becoming mature and holds potential to boost the spectrum efficiency of a point-to-point wireless link. However, a fundamental understanding is still lacking, with respect to its advantages over half-duplex in multi-cell wireless networks with contending links. In this paper, we establish a spatial stochastic framework to analyze the mean network throughput gain from full duplex, and pinpoint the key factors that determine the gain. Our framework extends classical stochastic geometry analysis with a new tool set, which allows us to model a tradeoff between the benefit from concurrent full-duplex transmissions and the loss of spatial reuse, particularly for CSMA-based transmitters with random backoff. We analytically derive closed-form expressions for the full-duplex gain as a function of link distance, interference range, network density, and carrier sensing schemes. It can be easily applied to guide the deployment choices in the early stage of network planning.

In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.

Increasing popularity of mobile devices and upload-intensive applications is rapidly driving the uplink traffic demand in wireless LANs. Network MIMO (netMIMO) can potentially meet the demand by enabling concurrent uplink transmissions to an AP cluster (APC) comprised of multiple access points. NetMIMO's PHY-layer communication algorithms have been well explored, but the MAC-level signaling procedure remains an open issue: prior to uplink transmission, a group of clients must gain channel access, and ensure synchronization and channel orthogonality with each other. But such signaling is fundamentally challenging, because netMIMO clients tend to be widely distributed and may not even sense each other. In this paper, we introduce the first signaling protocol, called NURA, to meet the challenge. NURA clients employ a novel medium-access-signaling mechanism to realize group-based random access and synchronization, without disturbing ongoing uplink transmissions. The APC leverages a lightweight user-admission mechanism to group users with orthogonal channels (and hence high uplink capacity), without requiring costly channel-state feedback from all users. We have implemented NURA on a software-radio based netMIMO platform. Our experiments show that NURA is feasible, efficient, and can readily serve as the a priori signaling mechanism for distributed asynchronous netMIMO clients.

Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: a) Commodity 802.11ac users do not provide full CSI feedback, which has been widely used for MU-MIMO grouping. b) Heterogeneous channel bandwidth users limit grouping opportunities. c) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement MUSE, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show MUSE can achieve high throughput gains over existing designs.

3D orientation tracking is an essential ingredient for many Internet-of-Things applications. Yet existing orientation tracking systems commonly require motion sensors that are only available on battery-powered devices. In this paper, we propose Tagyro, which attaches an array of passive RFID tags as orientation sensors on everyday objects. Tagyro uses a closed-form model to transform the run-time phase offsets between tags into orientation angle. To enable orientation tracking in 3D space, we found the key challenge lies in the imperfect radiation pattern of practical tags, caused by the antenna polarity, non-isotropic emission and electromagnetic coupling, which substantially distort phase measurement. We address these challenges by designing a set of phase sampling and recovery algorithms, which together enable reliable orientation sensing with 3 degrees of freedom. We have implemented a real-time version of Tagyro on a commodity RFID system. Our experiments show that Tagyro can track the 3D orientation of passive objects with a small error of 4°, at a processing rate of 37.7 samples per second.

Orientation tracking is an essential ingredient for many Internet-of-Things applications. In this work, we introduce Tagyro, which attaches an array of passive RFID tags as orientation sensors on everyday objects. Tagyro uses a closed-form model to transform the run-time phase offsets between tags into orientation angle, and addresses the unexpected deviation of phase measurement distorted by imperfect radiation pattern of practical tags. We have implemented a real-time version of Tagyro on a commodity RFID system. In this demo, we show that Tagyro can handle the phase distortion by sensing the effective layout tag array, an use the sensed layout to track the orientation of objects in high accuracy.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. We present LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency, which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency (> 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. In our experiments, LiTell demonstrates high reliability in discriminating different FLs, and great potential to achieve sub-meter granularity.

The 60 GHz wireless technology holds great potential for multi-Gbps communications and high-precision radio sensing. But the lack of an accessible experimental platform has been impeding its progress. In this paper, we overcome the barrier with OpenMili, a reconfigurable 60 GHz radio architecture. OpenMili builds from off-the-shelf FPGA processor, data converters and 60 GHz RF front-end. It employs customized clocking, channelization and interfacing modules, to achieve Gsps sampling bandwidth, Gbps wireless bit-rate, and Gsps sample streaming from/to a PC host. It also incorporates the first programmable, electronically steerable 60 GHz phased-array antenna. OpenMili adopts programming models that ease development, through automatic parallelization inside signal processing blocks, and modular, rate-insensitive interfaces across blocks. It provides common reference designs to bootstrap the development of new network protocols and sensing applications. We verify the effectiveness of OpenMili through benchmark communication/sensing experiments, and showcase its usage by prototyping a pairwise phased-array localization scheme, and a learning-assisted real-time beam adaptation protocol.

The 60 GHz wireless technology holds great potential for multi-Gbps communications and high-precision radio sensing. But the lack of an accessible experimental platform has been impeding its progress. We propose to overcome the barrier with OpenMili, a reconfigurable 60 GHz radio architecture. OpenMili builds from off-the-shelf FPGA processor, data converters and 60 GHz RF front-end. It employs customized clocking, channelization and interfacing modules, to achieve Gsps sampling bandwidth, Gbps wireless bit-rate, and Gsps sample streaming from/to a PC host. It also incorporates the first programmable, electronically steerable 60 GHz phased-array antenna. OpenMili adopts programming models that ease development, through automatic parallelization inside signal processing blocks, and modular, rate-insensitive interfaces across blocks. In this demo, we will showcase OpenMili's hardware modules, and demonstrate example communication and sensing applications based on it.

Visible light indoor localization offers promising accuracy and reliability due to dense light deployment and multipath-free propagation. However, current visible light localization schemes rely on either energy-hungry cameras or assumptions about channel responses of light sources, which limits their application. They also require LED lights with specialized circuitry, which increases cost and hinders large scale deployment. We present LiTell2, a scheme that extracts unique features from incumbent fluorescent or LED lights, and uses a simple photodiode sensor to extract AoA information for localization. LiTell2's sensor achieves AoA measurements by leveraging the diversity of photodiodes' angular responses. By comparing signal from photodiodes of different angular response, LiTell2 can derive AoA of each light source, thus achieving high precision localization with unmodified light fixtures and extremely low fingerprinting cost. Our experiments show that LiTell2 provides accurate AoA sensing and enables energy efficient yet robust localization.

To meet the diverse traffic demands of different applications, emerging WLAN standards have been incorporating a variety of channel widths ranging from 5 to 160 MHz. The coexistence of variable-width channels imposes a new challenge to the 802.11 protocols, since the 802.11 MAC is agnostic of, and thus incapable of, adapting to the PHY-layer spectrum heterogeneity. To address this challenge, we uncover the cause and effect of variable-width channel coexistence, and develop a MAC-layer scheme, called Fine-grained Spectrum Sharing (FSS), that solves the general problem of fair and efficient spectrum sharing among users with heterogeneous channel-widths. Instead of deeming its spectrum band as an atomic block, an FSS user divides the spectrum into chunks, and adapts its chunk usage on a per-packet basis. FSS's spectrum adaptation is driven by a decentralized optimization framework. It preserves the 802.11 CSMA/CA primitives while allowing users to contend for each spectrum chunk, and can opportunistically split a wide-band channel or bond multiple (discontiguous) chunks to ensure fair and efficient access to available spectrum. In making such adaptation decisions, it balances the benefit from discontiguous chunks and the cost of guardband - a unique tradeoff in WLANs with heterogeneous channel-widths. Our in-depth evaluation demonstrates that FSS can improve throughput by multiple folds, while maintaining fairness of spectrum sharing for heterogeneous WLANs.

We explore the use of TV White Space (TVWS) wireless networks for providing robust and long range connectivity to vehicles. A key distinctive requirement of TVWS networks is the power asymmetry - the static APs are allowed to transmit at up to 4 W, while the mobile clients in vehicles are limited to only 100 mW. Our measurements reveal that the power asymmetry not only causes severe uplink blackouts but also poses significant coexistence problems, as high-power fixed nodes can easily starve the low-power mobile ones due to carrier sensing loss. To tackle these unique challenges, we propose a cross-layer design of a Direct-Sequence Spread Spectrum (DSSS) based system. We employ an adaptive DSSS mechanism that strategically configures the spreading code, so as to boost uplink coverage while maximizing throughput. We further design a traffic-aware code assignment algorithm for uplink packets to balance the requirement of throughput-intensive and latency-sensitive flows. We have implemented the design on a TVWS software-radio platform on a moving vehicle in an urban environment, and demonstrated that link asymmetry can be completely removed to support realistic application traffic, while the carrier sense loss rate at fixed nodes can be reduced by around 85%.

Full-duplex radio technology is becoming mature and holds potential to boost the spectrum efficiency of a point-to-point wireless link. However, a fundamental understanding is still lacking, with respect to its advantage over half-duplex in multi-cell wireless networks with contending links. In this paper, we establish a spatial stochastic framework to analyze the mean network throughput gain from full-duplex, and pinpoint the key factors that determine the gain. Our framework extends classical stochastic geometry analysis with a new tool-set, which allows us to model a trade-off between the benefit from concurrent full-duplex transmissions and the loss of spatial reuse, particularly for CSMA-based transmitters with random backoff. The analysis derives closed-form expressions for the full-duplex gain as a function of link distance, interference range, network density, and carrier sensing schemes. It can be easily applied to guide the deployment choices during the early stage of network planning.

Visible Light Communications (VLC) is emerging as an appealing technology to complement WiFi in indoor environments. Yet maintaining VLC performance under link dynamics remains a challenging problem. In this paper, we build a VLC software-radio testbed and examine VLC channel dynamics through comprehensive measurement. We find minor device movement or orientation change can cause the VLC link SNR to vary by tens of dB even within one packet duration, which renders existing WiFi rate adaptation protocols ineffective. We thus propose a new mechanism, DLit, that leverages two unique properties of VLC links (predictability and full-duplex) to realize fine-grained, in-frame rate adaptation. Our prototype implementation and experiments demonstrate that DLit achieves near-optimal performance for mobile VLC usage cases, and outperforms conventional packet-level adaptation schemes by multiple folds.

Leveraging the redundancy and parallelism from multiple RF chains, MIMO technology can easily scale wireless link capacity. However, the high power consumption and circuit-area cost prevents MIMO from being adopted by energy-constrained wireless devices. In this paper, we propose Halma, that can boost link capacity using multiple antennas but a single RF chain, thereby, consuming the same power as SISO. While modulating its normal data symbols, a Halma transmitter hops between multiple passive antennas on a per-symbol basis. The antenna hopping pattern implicitly carriers extra data, which the receiver can decode by extracting the index of the active antenna using its channel pattern as a signature.

An unobtrusive and continuous estimation of breathing volume could play a vital role in health care, such as for critically ill patients, neonatal ventilation, post-operative monitoring, just to name a few. While radar-based estimation of breathing rate has been discussed in the literature, estimating breathing volume using wireless signal remains relatively intact. With the presence of patient body movement and posture changes, long-term monitoring of breathing volume at fine granularity is even more challenging. In this work, we propose for the first time an autonomous system that monitors a patient's breathing volume with high resolution. We discuss the key research components and challenges in realizing the system. We also present an initial system design encompassing a continuous wave radar, motion tracking and control system, and a set of methods to accurately derive breathing volume from the reflected signal and to address challenges caused by body movement and posture changes. Our implementation shows promising results in estimating breathing volume with fine granularity.

Line-of-Sight blockage by human body is a severe challenge to enable robust 60 GHz directional links. Beamsteering is one feasible solution to overcome this problem by electronically steering phased-array beam towards Non-Line-of-Sight. However, effectiveness of beamsteering depends on the link deployment and a lack of assessment of steering effectiveness may render the link completely blacked-out during human blockage. In this poster, we propose a new technique called BeamScope, that predicts best possible location for a randomly deployed link in an indoor environment without the need of any explicit war-driving. BeamScope first characterizes the environment exploiting measurement from the randomly deployed reference location and then predicts the performance in unobserved locations to suggest a possible re-deployment. The environment characterization is captured through a novel metric and prediction is achieved via how this metric is shared between the reference location and unobserved locations. Our preliminary results show promising accuracy of identifying the best possible alternate location for 60 GHz link to achieve a robust connection during human blockage.

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.

Radio-based passive-object sensing can enable a new form of pervasive user-computer interface. Prior work has employed various wireless signal features to sense objects under a set of predefined, coarse motion patterns. But an operational UI, like a trackpad, often needs to identify fine-grained, arbitrary motion. This paper explores the feasibility of tracking a passive writing object (e.g., pen) at sub-centimeter precision. We approach this goal through a practical design, mTrack, which uses highly-directional 60 GHz millimeter-wave radios as key enabling technology. mTrack runs a discrete beam scanning mechanism to pinpoint the object's initial location, and tracks its trajectory using a signal-phase based model. In addition, mTrack incorporates novel mechanisms to suppress interference from background reflections, taking advantage of the short wavelength of 60 GHz signals. We prototype mTrack and evaluate its performance on a 60 GHz reconfigurable radio platform. Experimental results demonstrate that mTrack can locate/track a pen with 90-percentile error below 8 mm, enabling new applications such as wireless transcription and virtual trackpad.

Large-scale multiuser MIMO (MU-MIMO) systems have the potential for multi-fold scaling of network capacity. The research community has recognized this theoretical potential and developed architectures [1,2] with large numbers of RF chains. Unfortunately, building the hardware with a large number of RF chains is challenging in practice. CSI data transport and computational overhead of MU-MIMO beamforming can also become prohibitive under large network scale. Furthermore, it is difficult to physically append extra RF chains on existing communication equipments to support such large-scale MU-MIMO architectures.

Adaptive HTTP video streaming over LTE has been gaining popularity due to LTE's high capacity. Quality of adaptive streaming depends highly on the accuracy of client's estimation of end-to-end network bandwidth, which is challenging due to LTE link dynamics. In this paper, we present piStream, that allows a client to efficiently monitor the LTE basestation's PHY-layer resource allocation, and then map such information to an estimation of available bandwidth. Given the PHY-informed bandwidth estimation, piStream uses a probabilistic algorithm to balance video quality and the risk of stalling, taking into account the burstiness of LTE downlink traffic loads. We conduct a real-time implementation of piStream on a software-radio tethered to an LTE smartphone. Comparison with state-of-the-art adaptive streaming protocols demonstrates that piStream can effectively utilize the LTE bandwidth, achieving high video quality with minimal stalling rate.

Mobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interaction workspace to any nearby surface area. Okuli piggybacks on visible light communication modules, and uses a low-power LED and two light sensors to locate user's finger within the workspace. It is built on a light propagation/reflection model that achieves around one-centimeter location precision, with zero run-time training overhead. We have prototyped Okuli as an Android peripheral, with a 3D-printed shroud to host the LED and light sensors. Our experiments demonstrate Okuli's accuracy, stability, energy efficiency, as well as its potential in serving virtual keyboard and trackpad applications.

To handle increased capacity demands, sophisticated MIMO-based transmission strategies, based on transmitter cooperation, have emerged. However, different types of users' channels (e.g., static vs mobile, stable vs dynamic channels) that make up today's enterprises, require different MIMO transmission strategies. With the wrong strategy, a user could even see a degradation in performance. Our overarching goal is to design and implement a framework, TRINITY, that can simultaneously cater to a heterogeneous mix of users, by intelligently combining a plurality of MIMO transmission strategies wherein the transmitters at different nodes can cooperate to deliver significant performance gains. Three key challenges that we address in building TRINITY are: (i) how to categorize users into channel profiles such that a single transmission strategy caters to the users of a profile, (ii) how to combine strategies to communicate with users of different profiles simultaneously, and (iii) what is the granularity of transmitter cooperation needed to balance efficiency with complexity. We implement and evaluate TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared to individual transmission strategies.

Equipped with the multi-user MIMO (MU-MIMO) technology, a WiFi access point (AP) with M antennas can achieve M degrees-of-freedom (DoF) in theory. Existing MU-MIMO protocols strive to maximize DoF usage by serving M users simultaneously. In this paper, through a MU-MIMO testbed measurement, we found that the correlation between users' channels can severely compromise network throughput under a DoF-maximizing strategy. To combat this problem, we propose Kardia that judiciously distributes the DoF to least-correlated users within an AP's cell, and coordinates neighboring APs such that each can serve the best set of users while nulling mutual interference. The key challenge in Kardia lies in efficiently determining which set of users to serve without knowing the channel state from all of them. We propose a lightweight mechanism that enables APs to collaboratively infer the correlation between users through precoded probing packets. Our analysis shows that Kardia's DoF distribution framework has a provable performance guarantee. Our testbed implementation further reveals that Kardia can significantly boost the performance of multi-cell MU-MIMO networks, in contrast to legacy protocols like 802.11ac.

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.

WiFi Display, also called Miracast, is an emerging technology that allows a mobile device (source) to duplicate its screen content to an external display (sink) via a peer-to-peer WiFi link. Despite its diverse application scenarios and growing popularity, Miracast consumes substantial power due to a combination of video encoding/decoding and transmission. In this paper, we first conduct a measurement study to quantify and model key parameters that scale Miracast's power consumption. We then propose a set of optimization mechanisms to bypass redundant codec operations, reduce video tail traffic, and relocate the Miracast channel dynamically to maximize transmission efficiency. We have implemented this energy-efficient Miracast framework on an Android smartphone. Experimental results show that the legacy Miracast system costs 1.3 to 2.4 Watts. Our framework reduces the power consumption by 29% to 61%, depending on the Miracast application's video traffic patterns. Our optimization mechanisms do not affect the video quality, and can even reduce the latency of certain Miracast applications.

60 GHz technology holds tremendous potential to upgrade wireless link throughput to Gbps level. To overcome inherent vulnerability to attenuation, 60 GHz radios communicate by forming highly-directional electronically-steerable beams. Standards like IEEE 802.11ad have tailored MAC/PHY protocols to such flexible-beam 60 GHz networks. However, lack of a reconfigurable platform has thwarted a realistic proof-of-concept evaluation. In this paper, we conduct an in-depth measurement of indoor 60 GHz networks using a first-of-its-kind software-radio platform. Our measurement focuses on the link-level behavior with three major perspectives: (i) coverage and bit-rate of a single link, and implications for 60 GHz MIMO; (ii) impact of beam-steering on network performance, particularly under human blockage and device mobility; (iii) spatial reuse between flexible beams. Our study dispels some common myths, and reveals key challenges in maintaining robust flexible-beam connection. We propose new principles that can tackle such challenges based on unique properties of 60 GHz channel and cognitive capability of 60 GHz links.

Cooperative relay aims to realize the capacity of multi-antenna arrays in a distributed manner. However, the symbol-level synchronization requirement among distributed relays limits its use in practice. We propose to circumvent this barrier with a cross-layer protocol called Distributed Asynchronous Cooperation (DAC). With DAC, multiple relays can schedule concurrent transmissions with packet-level (hence coarse) synchronization. The receiver then extracts multiple versions of each relayed packet via a collision-resolution algorithm, thus realizing the diversity gain of cooperative communication. We demonstrate the feasibility of DAC by prototyping and testing it on the GNURadio/USRP software radio platform. To explore its relevance at the network level, we introduce a DAC-based medium access control (MAC) protocol, and a generic approach to integration of the DAC MAC/PHY layer into a typical routing algorithm. Considering the use of DAC for multiple network flows, we analyze the fundamental tradeoff between the improvement in diversity gain and the reduction in multiplexing opportunities. DAC is shown to improve the throughput and delay performance of lossy networks with intermediate link quality. Our analytical results have also been confirmed via network-level simulation with ns-2.

This paper presents a recognition scheme for fine-grain gestures. The scheme leverages directional antenna and short-range wireless propagation properties to recognize a vocabulary of action-oriented gestures from the American Sign Language. Since the scheme only relies on commonly available wireless features such as Received Signal Strength (RSS), signal phase differences, and frequency subband selection, it is readily deployable on commercial-off-the-shelf IEEE 802.11 devices. We have implemented the proposed scheme and evaluated it in two potential application scenarios: gesture-based electronic activation from wheelchair and gesture-based control of car infotainment system. The results show that the proposed scheme can correctly identify and classify up to 25 fine-grain gestures with an average accuracy of 92% for the first application scenario and 84% for the second scenario.

Full-duplex radios are often envisioned to double wireless link capacity. Substantial work has focused on redesigning the radio hardware to achieve this theoretical gain. From a network-protocol perspective, however, it remains an open problem how to exploit full-duplex radio, and how much gain it can achieve in practical multi-cell wireless LANs. In this paper, we propose FuMAC, a channel access protocol tailored for full-duplex radios to optimally exploit their unique capabilities. FuMAC addresses a unique trade off between PHY-layer full duplex transmission and MAC-level spatial reuse, through a semi synchronous channel access principle. Its design is enabled by a novel self-interference cancellation mechanism called Active Antenna Cancellation. We verify FuMAC using software-radio implementation combined with large scale simulation. The results demonstrate that conventional MAC protocols severely underutilize full-duplex's potential. In contrast, FuMAC can achieve more-than-doubled throughput gain over half-duplex wireless LANs and significantly outperform alternative full-duplex MAC designs, while maintaining a much higher level of fairness.

Full-duplex has emerged as a new communication paradigm and is anticipated to double wireless capacity. Existing studies of full-duplex mainly focused on its PHY layer design, which enables bidirectional transmission between a single pair of nodes. In this paper, we establish an analytical framework to quantify the network-level capacity gain of full-duplex over half-duplex. Our analysis reveals that inter-link interference and spatial reuse substantially reduces full-duplex gain, rendering it well below 2 in common cases. More remarkably, the asymptotic gain approaches 1 when interference range approaches transmission range. Through a comparison between optimal half- and full-duplex MAC algorithms, we find that full-duplex's gain is further reduced when it is applied to CSMA based wireless networks. Our analysis provides important guidelines for designing full-duplex networks. In particular, network-level mechanisms such as spatial reuse and asynchronous contention must be carefully addressed in full-duplex based protocols, in order to translate full-duplex's PHY layer capacity gain into network throughput improvement.

In a multi-user MIMO (MU-MIMO) network, an AP with M antennas can only serve up to M users out of a large user population. The M users' rates are inter-coupled and depend on their channel orthogonality. Substantial theoretical studies focused on selecting users to maximize capacity, but they require feedback of channel state information (CSI) from all users. The resulting overhead can easily overwhelm useful data in large scale networks. In this paper, we propose a scalable user selection mechanism called orthogonality probing based user selection (OPUS). OPUS only requires up to M rounds of CSI feedback. In each round, it employs a novel probing mechanism that enables a user to evaluate its orthogonality with existing users, and a distributed contention mechanism that singles out the best user to feedback its CSI. Software-radio based implementation and experimentation shows that OPUS significantly outperforms traditional user selection schemes in both throughput and fairness.

High-quality, speaker-location-aware audio capturing has traditionally been realized using dedicated microphone arrays. But high cost and lack of portability prevents such systems from being widely adopted. Today's smartphones are relatively more convenient for audio recording, but the audio quality is much lower in noisy environment and speaker location cannot be readily obtained. In this paper, we design and implement Dia, which leverages smartphone cooperation to overcome the above limitations. Dia supports spontaneous setup, by allowing a group of users to rapidly assemble an array of smartphones to emulate a dedicated microphone array. It employs a novel framework to accurately synchronize the audio I/O clocks of the smartphones. The synchronized smartphone array further enables autodirective audio capturing, i.e., tracking the speaker's location, and beamforming the audio capturing towards the speaker to improve audio quality. We implement Dia on a testbed consisting of 8 Android phones. Our experiments demonstrate that Dia can synchronize the microphones of different smartphones with sample-level accuracy. It achieves high localization accuracy, and similar beamforming performance compared with a microphone array with perfect synchronization.

A well-known bottleneck of contemporary mobile devices is the inefficient and error-prone touchscreen keyboard. In this paper, we propose UbiK, an alternative portable text-entry method that allows user to make keystrokes on conventional surfaces, e.g., wood desktop. UbiK enables text-input experience similar to that on a physical keyboard, but it only requires a keyboard outline printed on the surface or a piece of paper atop. The core idea is to leverage the microphone on a mobile device to accurately localize the keystrokes. To achieve fine-grained, centimeter scale granularity, UbiK extracts and optimizes the location-dependent multipath fading features from the audio signals, and takes advantage of the dual-microphone interface to improve signal diversity. We implement UbiK as an Android application. Our experiments demonstrate that UbiK is able to achieve above 95% of localization accuracy. Field trial involving first-time users shows that UbiK can significantly improve text-entry speed over current on-screen keyboards.

A well-known bottleneck of contemporary mobile devices is the inefficient and error-prone touchscreen keyboard. We have developed UbiK, an alternative portable text-entry method that allows user to type on a piece of paper, placed on solid surfaces like wood desktop. UbiK leverages the microphones on a mobile device to accurately localize the keystrokes through fine-grained acoustic fingerprinting. We have implemented UbiK as an Android application. Our experiments demonstrate that UbiK is able to achieve above 95% of localization accuracy. In this demonstration, we will show how Ubik works with an external paper keyboard (Figure 1) for a smartphone and a 7-inch tablet. User participation will be welcome in this live demo.

Coordination of co-located wireless devices is a fundamental function/requirement for reducing interference. However, different devices cannot directly coordinate with one another as they often use incompatible modulation schemes. Even for the same type (e.g., WiFi) of devices, their coordination is infeasible when neighboring transmitters adopt different spectrum widths. Such an incompatibility between heterogeneous devices may severely degrade the network performance. In this paper, we introduce Gap Sense (GSense), a novel mechanism that can coordinate heterogeneous devices without modifying their PHYlayer modulation schemes or spectrum widths. GSense prepends legacy packets with a customized preamble, which piggy-backs information to enhance inter-device coordination. The preamble leverages the quiet period between signal pulses to convey such information, and can be detected by neighboring nodes even when they have incompatible PHY layers. We have implemented and evaluated GSense on a software radio platform, demonstrating its significance and utility in three popular protocols. GSense is shown to deliver coordination information with close to 100% accuracy within practical SNR regions. It can also reduce the energy consumption by around 44%, and the collision rate by more than 88% in networks of heterogeneous transmitters and receivers.

MIMO beamforming technology can scale wireless data rate proportionally with the number of antennas. However, the overhead induced by receivers' CSI (channel state information) feedback scales at a higher rate. In this paper, we address this fundamental tradeoff with Adaptive Feedback Compression (AFC). AFC quantizes or compresses CSI from 3 dimensions --- time, frequency and numerical values, and adapts the intensity of compression according to channel profile. This simple principle faces many practical challenges, e.g., a huge search space for adaption, estimation or prediction of the impact of compression on network throughput, and the coupling of different users in multi-user MIMO networks. AFC meets these challenges using a novel cross-layer adaptation metric, a metric extracted from 802.11 packet preambles, and uses it to guide the selection of compression intensity, so as to balance the tradeoff between overhead reduction and capacity loss (due to compression). We have implemented AFC on a software radio testbed. Our experiments show that AFC can outperform alternative approaches in a variety of radio environments.

Network MIMO (netMIMO) has potential for significantly enhancing the capacity of wireless networks with tight coordination of access points (APs) to serve multiple users concurrently. Existing schemes realize netMIMO by integrating distributed APs into one ``giant'' MIMO but do not scale well owing to their global synchronization requirement and overhead in sharing data between APs. To remedy this limitation, we propose a novel system, NEMOx, that realizes netMIMO downlink transmission for large-scale wireless networks. NEMOx organizes a network into practical-size clusters, each containing multiple distributed APs (dAPs) that opportunistically synchronize with each other for netMIMO downlink transmission. Inter-cluster interference is managed with a decentralized channel-access algorithm, which is designed to balance between the dAPs' cooperation gain and spatial reuse---a unique tradeoff in netMIMO. Within each cluster, NEMOx optimizes the power budgeting among dAPs and the set of users to serve, ensuring fairness and effective cancellation of cross-talk interference. We have implemented and evaluated a prototype of NEMOx in a software radio testbed, demonstrating its throughput scalability and multiple folds of performance gain over current wireless LAN architecture and alternative netMIMO schemes.

Conventional wireless broadcast protocols rely heavily on the 802.11-based CSMA/CA model, which avoids interference and collision by conservative scheduling of transmissions. While CSMA/CA is amenable to multiple concurrent unicasts, it tends to degrade broadcast performance significantly, especially in lossy and large-scale networks. In this paper, we propose a new protocol called Chorus that improves the efficiency and scalability of broadcast service with a MAC/PHY layer that allows packet collisions. Chorus is built upon the observation that packets carrying the same data can be effectively detected and decoded, even when they overlap with each other and have comparable signal strengths. It resolves collision using symbol-level interference cancellation, and then combines the resolved symbols to restore the packet. Such a collision-tolerant mechanism significantly improves the transmission diversity and spatial reuse in wireless broadcast. Chorus' MAC-layer cognitive sensing and scheduling scheme further facilitates the realization of such an advantage, resulting in an asymptotic broadcast delay that is proportional to the network radius. We evaluate Chorus' PHY-layer collision resolution mechanism with symbol-level simulation, and validate its network-level performance via ns-2, in comparison with a typical CSMA/CA-based broadcast protocol. Our evaluation validates Chorus's superior performance with respect to scalability, reliability, delay, etc., under a broad range of network scenarios (e.g., single/multiple broadcast sessions, static/mobile topologies).

The unlicensed ISM spectrum is getting crowded by wireless local area network (WLAN) and wireless personal area network (WPAN) users and devices. Spectrum sharing within the same network of devices can be arbitrated by existing MAC protocols, but the coexistence between WPAN and WLAN (e.g., ZigBee and WiFi) remains a challenging problem. The traditional MAC protocols are ineffective in dealing with the disparate transmit-power levels, asynchronous time-slots, and incompatible PHY layers of such heterogeneous networks. Recent measurement studies have shown moderate-to-high WiFi traffic to severely impair the performance of coexisting ZigBee. We propose a novel mechanism, called cooperative carrier signaling (CCS), that exploits the inherent cooperation among ZigBee nodes to harmonize their coexistence with WiFi WLANs. CCS employs a separate ZigBee node to emit a carrier signal (busy tone) concurrently with the desired ZigBee's data transmission, thereby enhancing the ZigBee's visibility to WiFi. It employs an innovative way to concurrently schedule a busy tone and a data transmission without causing interference between them. We have implemented and evaluated CCS on the TinyOS/MICAz and GNURadio/USRP platforms. Our extensive experimental evaluation has shown that CCS reduces collision between ZigBee and WiFi by 50% for most cases, and by up to 90% in the presence of a high-level interference, all at negligible WiFi performance loss.

Coordinated Multi-Point (CoMP) transmission is emerging as a concept that can substantially suppress interference, thus improving the capacity of multi-cell wireless networks. However, existing CoMP techniques either require sharing of data and channel state information (CSI) for all links in the network, or have limited capability of interference suppression. In this paper, we propose distributed interference alignment and cancellation (DIAC) to overcome these limitations. DIAC builds on a key intuition of interference locality — since each link is interfered with a limited number of neighboring links, it is sufficient to coordinate with those strong interferers and ignore others, in order to bound the overhead in CoMP. DIAC realizes the localized coordination by integrating interference cancellation and distributed interference alignment, and can be applied to both the uplink and downlink of multi-cell wireless networks. We validate DIAC using both model-driven and trace-based simulation where the traces are collected by implementing a MIMO-OFDM channel estimator on a software radio platform. Our experiments show that DIAC can substantially improve the degrees of freedom in multi-cell wireless networks.

The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order to increase their revenue and improve spectrum utilization. In DSM, determining the optimal spectrum leasing price is an important yet challenging problem that requires a comprehensive understanding of market participants' interests and interactions. In this paper, we study spectrum pricing competition in a duopoly DSM, where two wireless service providers (WSPs) lease spectrum access rights, and secondary users (SUs) purchase the spectrum use to maximize their utility. We identify two essential, but previously overlooked, properties of DSM: 1) heterogeneous spectrum resources at WSPs and 2) spectrum sharing among SUs. We demonstrate the impact of spectrum heterogeneity via an in-depth measurement study using a software-defined radio (SDR) testbed. We then study the impacts of spectrum heterogeneity on WSPs' optimal pricing and SUs' WSP selection strategies using a systematic three-step approach. First, we study how spectrum sharing among SUs subscribed to the same WSP affects the SUs' achievable utility. Then, we derive the SUs' optimal WSP selection strategy that maximizes their payoff, given the heterogeneous spectrum propagation characteristics and prices. We analyze how individual SU preferences affect market evolution and prove the market convergence to a mean-field limit, even though SUs make local decisions. Finally, given the market evolution, we formulate the WSPs' pricing strategies in a duopoly DSM as a noncooperative game and identify its Nash equilibrium points. We find that the equilibrium price and its uniqueness depend on the SUs' geographical density and spectrum propagation characteristics. Our analytical framework reveals the impact of spectrum heterogeneity in a real-world DSM, and can be used as a guideline for the WSPs' pricing strategies.

WiFi interface is known to be a primary energy consumer in mobile devices, and idle listening (IL) is the dominant source of energy consumption in WiFi. Most existing protocols, such as the 802.11 power-saving mode (PSM), attempt to reduce the time spent in IL by sleep scheduling. However, through an extensive analysis of real-world traffic, we found more than 60 percent of energy is consumed in IL, even with PSM enabled. To remedy this problem, we propose Energy-Minimizing idle Listening (E-MiLi) that reduces the power consumption in IL, given that the time spent in IL has already been optimized by sleep scheduling. Observing that radio power consumption decreases proportionally to its clock rate, E-MiLi adaptively downclocks the radio during IL, and reverts to full clock rate when an incoming packet is detected or a packet has to be transmitted. E-MiLi incorporates sampling rate invariant detection, ensuring accurate packet detection and address filtering even when the receiver's sampling clock rate is much lower than the signal bandwidth. Further, it employs an opportunistic downclocking mechanism to optimize the efficiency of switching clock rate, based on a simple interface to existing MAC-layer scheduling protocols. We have implemented E-MiLi on the USRP software radio platform. Our experimental evaluation shows that E-MiLi can detect packets with close to 100 percent accuracy even with downclocking by a factor of 16. When integrated with 802.11, E-MiLi can reduce energy consumption by around 44 percent for 92 percent of users in real-world wireless networks.

Recent works have considered the feasibility of full duplex (FD) wireless communications in practice. While the first FD system by Choi et.al. relied on a specific antenna cancellation technique to achieve a significant portion of self-interference cancellation, the various limitations of this technique prompted latter works to move away from antenna cancellation and rely on analog cancellation achieved through channel estimation. However, the latter systems in turn require the use of variable attenuator and delay elements that need to be automatically tuned to compensate for the self-interference channel. This not only adds complexity to the overall system but also makes the performance sensitive to wide-band channels. More importantly, none of the existing FD schemes can be readily scaled to MIMO systems.

Emerging WLAN standards have been incorporating a variety of channel widths ranging from 5MHz to 160MHz, in order to match the diverse traffic demands on different networks. Unfortunately, the current 802.11 MAC/PHY is not designed for the coexistence of variable-width channels. Overlapping narrowband channels may block an entire wide-band channel, resulting in severe spectrum underutilization and even starvation of WLANs on the wide-band. A similar peril exists when a WLAN partially overlaps its channel with multiple orthogonal WLANs. In this paper, we propose to solve the problem of partial spectrum sharing using Adaptive Subcarrier Nulling (ASN). ASN builds on the 802.11 OFDM PHY, but allows the radios to sense, transmit, detect, and decode packets through spectrum fragments, or subbands. An ASN transmitter can adapt its spectrum usage on a per-packet basis, by nulling the subbands used by neighboring WLANs, and sending packets through the remaining idle subbands. ASN preserves the 802.11 CSMA/CA primitives while allowing users to contend for access to each subband, and can opportunistically exploit the merits of wide-band channels via spectrum aggregation. We have implemented and evaluated ASN on the GNURadio/USRP platform. Our experimental results have shown ASN to achieve detection and decoding performance comparable to the legacy 802.11. Our detailed simulation in ns-2 further shows that ASN substantially improves the efficiency and fairness of spectrum sharing for multi-cell WLANs.

WiFi interface is known to be a primary energy consumer in mobile devices, and idle listening (IL) is the dominant source of energy consumption in WiFi. Most existing protocols, such as the 802.11 power-saving mode (PSM), attempt to reduce the time spent in IL by sleep scheduling. However, through an extensive analysis of real-world traffic, we found more than 60% of energy is consumed in IL, even with PSM enabled. To remedy this problem, we propose E-MiLi (Energy-Minimizing idle Listening) that reduces the power consumption in IL, given that the time spent in IL has already been optimized by sleep scheduling. Observing that radio power consumption decreases proportionally to its clock-rate, E-MiLi adaptively downclocks the radio during IL, and reverts to full clock-rate when an incoming packet is detected or a packet has to be transmitted. E-MiLi incorporates sampling rate invariant detection, ensuring accurate packet detection and address filtering even when the receiver's sampling clock-rate is much lower than the signal bandwidth. Further, it employs an opportunistic downclocking mechanism to optimize the efficiency of switching clock-rate, based on a simple interface to existing MAC-layer scheduling protocols. We have implemented E-MiLi on the USRP software radio platform. Our experimental evaluation shows that E-MiLi can detect packets with close to 100% accuracy even with downclocking by a factor of 16. When integrated with 802.11, E-MiLi can reduce energy consumption by around 44% for 92% of users in real-world wireless networks.

The ISM spectrum is becoming increasingly populated by emerging wireless networks. Spectrum sharing among the same network of devices can be arbitrated by MAC protocols (e.g., CSMA), but the coexistence between heterogeneous networks remains a challenge. The disparate power levels, asynchronous time slots, and incompatible PHY layers of heterogeneous networks severely degrade the effectiveness of traditional MAC. In this paper, we propose a new mechanism, called the Cooperative Busy Tone (CBT), that enables the reliable coexistence between two such networks, ZigBee and WiFi. CBT allows a separate ZigBee node to schedule a busy tone concurrently with the desired transmission, thereby improving the visibility of ZigBee devices to WiFi. Its core components include a frequency flip scheme that prevents the mutual interference between cooperative ZigBee nodes, and a busy tone scheduler that minimizes the interference to WiFi, for both CSMA and TDMA packets. To optimize CBT, we establish an analytical framework that relates its key design parameters to performance and cost. Both the analytical and detailed simulation results demonstrate CBT's significant throughput improvement over the legacy ZigBee protocol, with negligible performance loss to WiFi. The results are validated further by implementing CBT on sensor motes and software radios.

In cognitive radio networks (CRNs), detecting small-scale primary devices, such as wireless microphones, is a challenging, but very important, problem that has not yet been addressed well. Recently, cooperative sensing and sensing scheduling have been advocated as an effective MAC (medium access control) layer approach to detecting large-scale primary signals. However, it is unclear whether and how they can improve the detection of a small-scale primary signal because of (i) its small signal footprint due to the use of weak transmit-power, and (ii) the unpredictability of its spatial and temporal spectrum-usage patterns. Based on extensive analysis and simulation, we identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary signals. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, we propose an efficient sensing framework that jointly performs \underline{De}tection, \underline{LOC}ation estimation, and transmit-power estimation ({t DeLOC}) for small-scale primary users. Our extensive evaluation results in a realistic CRN environment show that {t DeLOC} achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft. These findings provide useful insights and guidelines in designing a sensing scheme for detection of small-scale primaries in CRNs.

Network coding is emerging as a promising alternative to traditional content distribution approaches in P2P networks. By allowing information mixture and randomized block selection, it simplifies the block scheduling problem, resulting in more efficient data delivery. Existing protocols have validated such advantages assuming altruistic and obedient peers. In this paper, we develop an analytical framework that characterizes a coding-based P2P content distribution market where rational agents seek for individual payoff maximization. Unlike existing game theoretical models, we focus on a decentralized resale market-through virtual monetary exchanges, agents buy the coded blocks from others and resell their possessions to those in need. We model such transactions as decentralized strategic bargaining games, and derive the equilibrium prices between arbitrary pairs of agents when the market enters the steady state. We further characterize the relations between coding complexity and market properties including agents' entry price and expected payoff, thus providing guidelines for strategic operations in a real P2P market. Our analysis reveals that the major power of network coding lies in maintaining stability of the market with impatient agents, and incentivizing agents with lower price and higher payoff, at the cost of reasonable coding complexity. Since the traditional P2P content distribution approach is a special case of network coding, our model can be generalized to analyze the equilibrium strategies of rational agents in decentralized resale markets.

In cognitive radio networks (CRNs), detecting small-scale primary devices---such as wireless microphones (WMs)---is a challenging, but very important, problem that has not yet been addressed well. We identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary devices. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, we propose an efficient sensing framework, called DeLOC, that iteratively performs location/transmit-power estimation and dynamic sensor selection for cooperative sensing. Our extensive simulation results in a realistic CRN environment show that DeLOC achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft.

Cooperative relay is a communication paradigm that aims to realize the capacity of multi-antenna arrays in a distributed manner. However, the symbol-level synchronization requirement among distributed relays limits its use in practice. We propose to circumvent this barrier with a cross-layer protocol called Distributed Asynchronous Cooperation (DAC). With DAC, multiple relays can schedule concurrent transmissions with packet-level (hence coarse) synchronization. The receiver then extracts multiple versions of each relayed packet via a collision-resolution algorithm, thus realizing the diversity gain of cooperative communication. We demonstrate the feasibility of DAC by prototyping and testing it on the GNURadio/USRP software radio platform. To explore its relevance at the network level, we introduce a DAC-based MAC, and a generic approach to integrate the DAC MAC/PHY layer into a typical routing algorithm. Considering the use of DAC for multiple network flows, we analyze the fundamental tradeoff between the improvement in diversity gain and the reduction in multiplexing opportunities. DAC is shown to improve the throughput and delay performance of lossy networks with medium-level link quality. Our analytical results are also confirmed by network-level simulation in ns-2.

Traditional wireless broadcast protocols rely heavily on the 802.11-based CSMA/CA model, which avoids interference and collision by conservatively scheduling transmissions. While CSMA/CA is amenable to multiple concurrent unicasts, it tends to degrade broadcast performance, especially when there are a large number of nodes and links are lossy. In this paper, we propose a new, drastically different protocol called Chorus that improves the efficiency and scalability of broadcast service with a MAC layer that allows packet collisions. Chorus is built upon the observation that packets carrying the same data can be effectively detected and decoded, even when they overlap in time and have comparable signal strength. It performs collision resolution using symbol-level iterative decoding, and then combines the resolved symbols to reconstruct the packet. This collision-tolerant mechanism significantly improves the transmission diversity and spatial reuse in wireless broadcast, providing an asymptotic broadcast delay that is proportional to the network radius. This advantage is exploited further by Chorus's MAC-layer cognitive sensing and scheduling scheme. We evaluate Chorus with symbol-level simulation, and validate its network-level performance via ns-2, in comparison with a typical CSMA/CA broadcast protocol.

Network coding is emerging as a promising alternative to traditional content distribution approaches in P2P networks. By allowing information mixture in peers, it simplifies the block scheduling problem, resulting in more efficient data delivery. Existing protocols have validated such advantages assuming altruistic and obedient peers. In this paper, we develop an analytical framework that characterizes a coding based P2P content distribution market where peers selfishly seek for individual payoff maximization. Through virtual monetary exchanges, agents in the market buy the coded blocks from others and resell their possessions to those in need. We model such transactions as decentralized strategic bargaining games, and derive the equilibrium prices between arbitrary pairs of agents when the market enters the steady state. We identify the traditional P2P content distribution approach as a special case of network coding, and characterize the relations between coding complexity and market performance metrics, including agents' entry price and expected payoff, thus providing operation guidelines for a real P2P market. Our analysis reveals that the major power of network coding lies in its ability to maintain stability of the market with impatient and selfish agents, and to incentivize agents with lower price and higher payoff, at the cost of reasonable coding complexity.

Network coding has been a prominent approach to a series of problems that used to be considered intractable with traditional transmission paradigms. Recent work on network coding includes a substantial number of optimization based protocols, but mostly for wireline multicast networks. In this paper, we consider maximizing the benefits of network coding for unicast sessions in lossy wireless environments. We propose optimized multipath network coding (OMNC), a rate control protocol that dramatically improves the throughput of lossy wireless networks. OMNC employs multiple paths to push coded packets to the destination, and uses the broadcast MAC to deliver packets between neighboring nodes. The coding and broadcast rate is allocated to transmitters by a distributed optimization algorithm that maximizes the advantage of network coding while avoiding congestion. With extensive experiments on an emulation testbed, we find that OMNC achieves more than two-fold throughput increase on average compared to traditional best path routing, and significant improvement over existing multipath routing protocols with network coding. The performance improvement is notable not only for one unicast session, but also when multiple concurrent unicast sessions coexist in the network.

Taking advantage of the frequency diversity and multiuser diversity in OFDMA based wireless networks, dynamic subcarrier assignment mechanisms have shown to be able to achieve much higher downlink capacity than static assignment. A rich literature exists that proposes MAC and physical layer schemes aiming at exploiting the diversity gain with low implementation complexity. In this paper, we propose a cross layer approach that explores the joint advantage of network coding and dynamic subcarrier assignment. Our algorithm improves the bandwidth efficiency of OFDMA downlink by encoding frames of the mobile stations that exchange information. We highlight a tradeoff between diversity gain and the network coding advantage, which is critical to the network performance. To explore the tradeoff, we formulate the coding aware dynamic assignment scheme as a mixed integer program, and design a polynomial time heuristic that can be used in practical systems. Based on a network flow formulation and a penalty scheme, our heuristic well approximates the performance of an optimal algorithm, in terms of both throughput and fairness.

Network coding has been a prominent approach to a series of problems that used to be considered intractable with traditional transmission paradigms. Recent work on network coding includes a substantial number of optimization based protocols, but mostly for wireline multicast networks. In this paper, we consider maximizing the benefits of network coding for unicast sessions in lossy wireless environments. We propose Optimized Multipath Network Coding (OMNC), a rate control and routing protocol that dramatically improves the throughput of lossy wireless networks. OMNC employs multiple paths to push coded packets to the destination, and uses the broadcast MAC to deliver packets between neighboring nodes. The coding and broadcast rate is allocated to transmitters by a distributed optimization algorithm that maximizes the advantage of path diversity while avoiding congestion. With extensive experiments on an emulation testbed, we find that OMNC achieves significant throughput improvement over traditional best path routing protocols, and existing multipath routing protocols with network coding.

Prototyping application-layer algorithms in wireless networks is a lengthy and challenging process, involving either programming within a specific simulation platform, or deploying a real testbed that is neither flexible nor scalable. In this paper, we present Drift, a high-performance wireless emulation testbed that takes advantage of the benefits of both simulation and real implementation, while trying to avoid their drawbacks. As a highly condensed emulation infrastructure, Drift makes it possible to rapidly develop and validate large-scale wireless network application-layer protocols within a cluster computing environment. Unlike existing emulation testbeds, Drift features a fully decentralized architecture, an efficient message processing unit, and more accurate network models for mobile wireless nodes. It balances the fundamental trade-off between scalability and emulation accuracy, focusing on maximizing scalability with minimal loss of accuracy. Through baseline comparison and extensive experiments, we find that Drift is able to accommodate thousands of emulated nodes per server host (in contrast to only tens of nodes typically seen in existing emulation tools), while maintaining comparable accuracy to packet level simulators. Drift will be released as an open source platform.

Network coding has emerged as a promising approach that enables reliable and efficient end-to-end transmissions in lossy wireless mesh networks. Existing protocols have demonstrated its resilience to packet losses, as well as the ability to integrate naturally with multipath opportunistic routing. However, these heuristics do not take into account the inherent resource competition in wireless networks, thereby compromising the coding advantages. In this paper, we take a game-theoretic perspective towards optimized resource allocation for network coding based unicast protocols. We design decentralized mechanisms that achieve better efficiency-fairness tradeoff, for both cooperative and selfish users. Our framework features a modularized optimization of two subproblems: the multipath routing of coded information flows for each player, and the broadcast and coding rate allocation among competing players. We have implemented the framework on a wireless emulation testbed and demonstrated its high performance in terms of throughput and fairness.

Wireless mesh networks have emerged as a favorable infrastructure that promises to unify the existing 802.11 wireless LANs. With multiple orthogonal channels and possibly multiple interfaces on the mesh nodes, such networks can provide broadband access for a large number of wireless clients. However, efficient assignment of channels to the available network interfaces has long been a daunting task for network designers. Existing heuristic and theoretical work unanimously focuses on joint design of channel assignment with the conventional transport/IP/MAC architecture. In this paper, we show that a new paradigm, network coding, is able to further increase the capacity of multi-channel mesh networks. We propose a joint optimization problem that accounts for routing, channel assignment, and network coding, and analyze its potential performance gains over the non-coding schemes. This problem inspires a practical algorithm that naturally combines network coding and routing. We also explore the benefits of network coding for emerging multi-channel wireless networks, including 802.16 and 802.11n, and derive the upper bound for its performance gains over existing channel assignment protocols.