The prevailing single-source paradigm in wireless sensing produces specialized models that are unscalable and generalize poorly to new tasks. Multi-source pre-training offers a path toward a generalist backbone but poses challenges including task heterogeneity, data redundancy, structural incompatibility, and the lack of a general-purpose pre-training objective. To address these issues, we propose WiSwiss, a comprehensive self-supervised multi-source pre-training framework that learns a general-purpose backbone for each modality. WiSwiss integrates semantic deduplication for dataset curation and a transformation-invariant pre-training objective. Experiments show that WiSwiss outperforms models trained from scratch, improving WiFi and mmWave performance by 4.5% and 10.3%, respectively, while reducing fine-tuning data requirements by 22.2% and 28.6%. We also present a qualitative study of scaling laws, showing that gains are task-dependent and that larger models require sufficiently large and diverse pre-training corpora to achieve substantial improvements.

Tracking-based social behavior analysis of lab animals, especially mice, is crucial for research in biology, medicine, and psychology. However, existing visual tracking systems struggle to maintain long-term, accurate tracking due to frequent identity association errors, which lead to extensive manual correction and limit research scalability. This paper introduces mTrack, an RFID–vision hybrid mouse tracking system that leverages the precise identification capability of UHF RFID to assist the visual tracker, enabling long-term, self-correcting, and high-accuracy mouse tracking. Our experiments demonstrate that mTrack can track up to ten mice simultaneously with over 99.23% identification accuracy and a 0.4 cm 99th-percentile localization error, reducing error rate by more than 40x compared with visual tracking systems. Our field studies indicate that mTrack can sustain this performance for over two hours and be seamlessly integrated into existing animal behavior research workflows. The code and dataset are open-sourced at https://github.com/SOAR-PKU/mTrack.

With the rapid advancement of mobile technologies, including self-driving cars and drones, the deployment of mobile software has become increasingly complex. In this context, virtualization plays a pivotal role by simplifying service deployment through containers and enabling container orchestration plat forms to efficiently manage an expanding number of container clusters. This is achieved by leveraging standardized interfaces and minimizing resource optimization overhead. However, the use of distributed servers in mobile edge clusters introduces several challenges, such as bandwidth limitations, network performance fluctuations, and resource constraints, which complicate deployment in these dynamic and resource-constrained environments. In this paper, we rethink the layer-based structure, a fundamental container design, and analyze the challenges and potential of real edge platform traces. Consequently, we propose BREAK, an acceleration middleware for efficient container deployment. With the primary insight of enhancing layer-reuse and deriving benefits from it, we develop a co-design approach centered on layer structure for efficient deployment, ensuring backward compatibility: (i) a container image refactoring solution that optimizes efficiency while preserving the stack-of-layers structure, (ii) distributed shared layer-stack caches, dynamically optimized for collaborative container deployment among mobile edge clusters, (iii) a customized Kubernetes (K8s) scheduler extending awareness of network performance, disk space, and container layer cache for container placement, and (iv) a tailored storage-driver of the standard container runtime for efficient layer extraction. Results indicate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to the state-of-the-art approach.

The emerging low-altitude economy leverages airspace below 1000 meters for intensive commercial and social aerial activities, where integrated sensing and communication (ISAC) service in 6G network for aircraft is critical to ensuring safe and efficient operations. However, aircraft often operate under constrained wireless resources, particularly in areas with limited or no network coverage. In such settings, exhaustive sensing and data communication among neighboring nodes lead to uncoordinated competition and prohibitive overhead. This paper investigates the ISAC service modeling and scheduling in aerial-vehicle-to-everything (Aerial-V2X) networks, which provides continuous high-accuracy sensing without compromising communication throughput under constrained resource budgets. First, we design a reconfigurable ISAC waveform tailored for aircraft, enabling flexible resource partitioning for both cellular communication (aerial vehicle-to-infrastructure, A-V2I) and cooperative sensing (aerial vehicle-to-vehicle, A-V2V). Second, we formulate a joint sensing-communication service model under this waveform and cast the optimization problem in a partially observable Markov decision process (POMDP). Third, a multi-agent deep reinforcement learning (MADRL) approach is developed to perform decentralized scheduling of sensing actions for each aircraft, minimizing time-frequency resource consumption. Experiments and trace-driven evaluations demonstrate that the proposed method can reduce sensing overhead by up to 70% compared to benchmark policies, while maintaining satisfactory communication and sensing performance.

Figure 1: EchoSight leverages in-situ optical backscatter tethering to achieve a look-and-control bidirectional interaction on commercial AR glasses.This process mimics human's natural interaction with low mental burden.EchoSight requires no pre-registration or network connection, facilitating scenarios where interactions are opportunistic and impromptu.

The rapid advancement in satellite technology with the adoption of commercial off-the-shelf (COTS) devices and satellite constellation networking has given rise to Space Computing Networks (SCNs). While SCN research is typically conducted on experimental platforms due to high operational costs, the unique challenges of SCNs require special consideration. In this demo, we introduce Rhone, an emulator that bridges these gaps by achieving both satellite- and constellation-level fidelity (the accurate replication of satellite and constellation states, including power, thermal, and network conditions, as well as application performance) while ensuring usability. Rhone adopts a two-phase approach: i) an offline phase builds power, thermal, orbit, network, and computation models using real satellite telemetries and hardware-in-the-loop chip mirroring, and ii) an online phase executes container-based emulation integrated with these models. Evaluation shows Rhone's power and computation model errors under 5% and thermal model errors within 1.3 – 2.5°C.

Following the broad prospect of Radio Frequency Identification (RFID) technology is the security concern of unauthorized tag identification, which poses threats to the privacy of both objects and users. In this paper, we propose RF-Rock, the first RFID unauthorized identification attack that operates without tag activation, thereby evading almost all existing defenses. This attack exposes the vulnerabilities of current RFID networks in identification legitimacy and privacy. It is based on the intermodulation effect originating from intrinsic nonlinearity within tag circuits. To this end, we explore the distinctness and consistency of the intermodulation-based physical layer fingerprint of RFID tags with theoretical analysis and empirical validation, and optimize the attack accuracy and efficiency with delicate excitation plan. Real-world experiments show that RF-Rock achieves an attack success rate of 93.2% on average under various conditions. The entropy of our proposed fingerprint is 15.5 bits and implies sufficient capacity in practical attacks.

Generative models have gained significant attention for their ability to produce realistic synthetic data that supplements the quantity of real-world datasets. While recent studies show performance improvements in wireless sensing tasks by incorporating all synthetic data into training sets, the quality of synthetic data remains unpredictable and the resulting performance gains are not guaranteed. To address this gap, we propose tractable and generalizable metrics to quantify quality attributes of synthetic data—affinity and diversity. Our assessment reveals prevalent affinity limitation in current wireless synthetic data, leading to mislabeled data and degraded task performance. We attribute the quality limitation to generative models' lack of awareness of untrained conditions and domain-specific processing. To mitigate these issues, we introduce SynCheck, a quality-guided synthetic data utilization scheme that refines synthetic data quality during task model training. Our evaluation demonstrates that SynCheck consistently outperforms quality-oblivious utilization of synthetic data, and achieves 4.3% performance improvement even when the previous utilization degrades performance by 13.4%.

We introduce ErLang Sight, a novel multimodal system designed for liquid identification, integrating vision-based object detection, millimeter-wave (mmWave) Synthetic Aperture Radar (SAR) imaging, and large language model (LLM)-based contextual reasoning. Initially, the system leverages a visual detection pipeline to identify potential liquid containers within the environment, subsequently directing a mmWave sensor to perform targeted SAR imaging of these identified regions, and the permittivity values of the liquids are estimated using reflection coefficient analysis techniques. These physical measurements, combined with visual context and environmental indicators (such as whether the scenario is a kitchen, laboratory, or bar), are then input into a pretrained LLM. The LLM employs advanced semantic and situational reasoning to accurately determine the most likely type of liquid by integrating physics-based data with contextual knowledge. Experimental evaluations demonstrate that ErLang Sight significantly enhances the accuracy of distinguishing visually ambiguous liquids and exhibits robust generalization to previously unseen environments.

As embodied AI gradually transitions into practical applications, enhancing the fidelity of how embodied agents perceive the physical world has become a critical challenge. Current perception methods typically rely on computer vision-based fiducial marker systems, which suffer from limitations such as insufficient reading distance, poor localization accuracy, and high susceptibility to environmental lighting conditions. Currently, SPAD sensor-based LiDAR technology is emerging in commercial mobile devices due to its compact size, high precision, and low power consumption. This paper presents the design of the RetroLiDAR system, which chimes with the concept of backscatter in wireless technology, to create a liquid-crystal fiducial marker system that can be directly read by LiDAR. On the marker side, we use retroreflective materials to reflect the LiDAR's emitted light back and employ a liquid crystal modulator to adjust the intensity of the light signal. On the LiDAR end, we design a signal processing pipeline to demodulate the marker's modulation message using the temporal received signal strength. Experimental results from our prototype demonstrate that compared to visual fiducial markers, RetroLiDAR extends the reading distance by 2.6x compared to QR codes and by 44% compared to AprilTags, while reducing the median ranging error by 85%. We also present a low-power marker circuit design, a link budget analysis, and two proof-of-concept applications to validate the system's efficacy and practicality.

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports$3.12\times $more demand when compared to related existing approaches.

The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.

The recent emergence of Low Earth Orbit (LEO) mega-constellations, designed for high-speed broadband connections with low latency, has introduced new deployment challenges. Efficient launch sequence planning is crucial for rapid service rollout, performance enhancement, and service promotion. However, existing research predominantly focuses on the design and performance analysis of fully-deployed constellations and overlooks the evolving process from a partially-deployed constellation to a fully-deployed one. This paper explores the launch sequence optimization problem for mega-constellations, tailored to expedite service delivery and adapt to changing performance demands. To this end, (1) we identify critical network performance metrics for the constellation evolving process and construct a simulation toolchain capable of simulating and evaluating these metrics for any potential partially-deployed constellation. (2) Drawing upon three key observations on network availability, the number of visible satellites, and latency, we propose an algorithm that can construct a launch sequence for an arbitrary mega-constellation topology. Evaluation results show that this algorithm enables the early provision of services and maximizes network performance gains at each launch batch while catering to different user demands. For instance, our algorithm can achieve network performance nearly equivalent to that of Starlink when it initiated its service, without losing redundancy, while using 55% fewer satellites.

Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.

Acoustic sensing for heartbeat monitoring has emerged as a prevailing research topic in wireless sensing. However, existing acoustic sensing systems face two limitations: a restricted sensing range and operation limited to a single user, impeding large-scale deployment of its applications. In this paper, we present DF-Sense, aDualForming based multi-user acousticSensingsystem for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namelyDualforming, which leverages constructive superposition across multiple subcarriers and microphones. To facilitateDualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method and a 2-D peak identification scheme to locate and identify multiple subjects with subtle motions. Additionally, we propose a phase change-based method to promptly identify body leaning and adaptively re-localize subjects, thereby avoiding the high computational cost. Finally, we propose an enhanced recursive least squares (RLS) filter to effectively reconstruct high-quality heartbeat waveforms from Channel Frequency Response (CFR) signals affected by limb movements. Experimental results show that DF-Sense achieves high precision measurement of instantaneous heart rates within a range of 10 m, sufficient for most daily space requirements, and can monitor heartbeat for up to 6 subjects in a 2-D space.

Congestion control (CC) plays a pivotal role in low-latency interactive video streaming such as cloud gaming. However, existing end-to-end CC methods often cause self-induced network queuing. As a result, they may largely delay video frame transmission and undermine the user’s quality of experience. In this paper, we present a new, practical CC algorithm namedPudicathat strives to achieve near-zero queuing delay and high link utilization while respecting cross-flow fairness. Pudica introduces several judicious approaches to utilize the paced frame to probe the bandwidth utilization ratio (BUR) instead of bandwidth itself. By leveraging BUR estimations, Pudica designs a holistic bitrate adjustment policy to balance low queuing, efficiency, and fairness. We conducted thorough and comprehensive evaluations in real production networks. In comparison to the state-of-the-art methods, Pudica reduces the average and tailed frame delay by 3.1$\times$and 5.1$\times$, respectively. Meanwhile, it increases the frame bitrate by 12.1%. Pudica has been deployed in a large-scale cloud gaming platform, currently serving millions of players.

Container technology has revolutionized service deployment, offering streamlined processes and enabling container orchestration platforms to manage a growing number of container clusters. However, the deployment of containers in distributed edge clusters presents challenges due to their unique characteristics, such as bandwidth limitations and resource constraints. Existing approaches designed for cloud environments often fall short in addressing the specific requirements of edge computing. Additionally, very few edge-oriented solutions explore fundamental changes to the container design, resulting in difficulties achieving backward compatibility.In this paper, we reevaluate the fundamental layer-based structure of containers. We identify that the proliferation of redundant files and operations within image layers hinders efficient container deployment. Drawing upon the crucial insight of enhancing layer reuse and extracting benefits from it, we introduce BREAK, a holistic approach centered on layer structure throughout the entire container deployment pipeline, ensuring backward compatibility. BREAK refactors image layers and proposes an edge-oriented cache solution to enable ubiquitous and shared layers. Moreover, it addresses the complete deployment pipeline by introducing a customized scheduler and a tailored storage driver. Our results demonstrate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to state-of-the-art approaches.

Small satellites (SmallSats) are now widely used in various fields, such as real-time communication and earth observation. These increasingly complex space applications face limited support from conventional radiation-hardened processors onboard. Hence, many SmallSats are designed to utilize high performance commercial off-the-shelf (COTS) computing devices to address this problem but it remains unclear how the unique energy and thermal characteristics of SmallSats impact computing efficiency onboard. This work conducts a systematic and quantitative measurement study of COTS devices’ computing efficiency on two real orbiting SmallSats. The key findings are: 1) inadequate energy management may lead to electricity wastage in sunlit zones and shortages in eclipse zones, impacting onboard computing availability and 2) the weak heat dissipation onboard may compromise COTS computing efficiency by incurring thermal throttling. To address such challenges, we design ProScale, a lightweight application-aware power management and thermal control system to improve computing efficiency under both electrical and thermal energy constraints. Evaluation shows that ProScale can improve the average task completion latency by $2.1 \times$ for computation-intensive applications compared with baselines.

Passive visible light communication (VLC) modulates light propagation or reflection to transmit data without directly modulating the light source. Thus, passive VLC provides an alternative to conventional VLC, enabling communication where the light source cannot be directly controlled. There have been ongoing efforts to explore new methods and devices for modulating light propagation or reflection. The state-of-the-art has broken the 100 kbps data rate barrier for passive VLC by using a digital micro-mirror device (DMD) as the light modulating platform, or transmitter, and a photo-diode as the receiver. We significantly extend this work by proposing a massive spatial data channel framework for DMDs, where individual channels can be decoded in parallel using an event camera at the receiver. For the event camera, we introduce event processing algorithms to detect numerous channels and decode bits from individual channels with high reliability. Our prototype, built with off-the-shelf event cameras and DMDs, can decode up to ~2,000 parallel channels, achieving a data transmission rate of 1.6 Mbps, markedly surpassing current benchmarks by 16x.

This paper presents YU, the first verification system for checking traffic load properties under arbitrary failure scenarios that can scale to production Wide Area Networks (WANs). Building a practical YU requires us to address two challenges in terms of generality and efficiency. The state-of-the-art efforts either assume shortest-path-based forwarding (e.g., QARC) or only target single-failure reasoning (e.g., Jingubang). As a result, the former inherently cannot generalize to widely used protocols (e.g., SR and iBGP) that are beyond shortest-path forwarding, while the latter cannot efficiently handle arbitrary failure scenarios. For the generality challenge, we propose an approach inspired by symbolic execution, called symbolic traffic execution, to model the forwarding behavior of a range of practically deployed protocols (e.g., eBGP, iBGP, iGP, and SR) under failure scenarios. For the efficiency challenge, we propose diverse equivalence classification techniques (i.e., k-failure-equivalence and link-local-equivalence reduction) to reduce the symbolic traffic execution overhead caused by both the large size of the production WAN and the huge number of traffic flows traversing it. YU has been used in the daily verification of our WAN for several months and has successfully identified potential failure scenarios that would lead to traffic load violations.

Abstract As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non‐intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice‐based applications. We model microservice applications using a queue analysis‐based approach and filter the dominant frequency components of average response time signals by employing k‐means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two‐tier microservices‐based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions.

Mobile 360-degree video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient and variable wireless network bandwidth. Recently, saliency-driven 360-degree streaming overcomes the buffer size limitation of head movement trajectory (HMT)-driven solutions and thus strikes a better balance between video quality and rebuffering. However, inaccurate network estimations and intrinsic saliency bias still challenge saliency-based streaming approaches, limiting further QoE improvement. To address these challenges, we design a robust saliency-driven quality adaptation algorithm for 360-degree video streaming, RoSal360. Specifically, we present a practical, tile-size-aware deep neural network (DNN) model with a decoupled self-attention architecture to accurately and efficiently predict the transmission time of video tiles. Moreover, we design a reinforcement learning (RL)-driven online correction algorithm to robustly compensate the improper quality allocations due to saliency bias. Through extensive prototype evaluations over real wireless network environments including commodity WiFi, 4G/LTE, and 5G links in the wild, RoSal360 significantly enhances the video quality and reduces the rebuffering ratio, thereby improving the viewer QoE, compared to the state-of-the-art algorithms.

Internet of meta-material things (meta-IoT) is a network of sensors composed of meta-materials with the advantages of low cost, ultra-low power consumption, and robust, showing great potential for the coming 6G communications. However, existing meta-IoT systems assume specular reflection on meta-IoT sensors, which limits their applications. For example, in chemical factories with harsh environments, receivers are often deployed on mobile robots. Due to their mobility, when measuring the signals, it is infeasible to ensure that the receivers are located at a certain angle relative to the meta-IoT sensors. Therefore, it is necessary to broaden the angle range of reflected signal coverage. In this paper, we propose a meta-IoT system capable of supporting receivers deployed at arbitrary angles in a broadened angle range. To be specific, we first propose an inhomogeneous structural design for meta-IoT sensors to achieve reflection coverage broadening. Then, we establish the signal transmission model from the transmitter to the receiver, going through the proposed meta-IoT sensor. To maximize the reflection coverage while ensuring accurate sensing results, we formulate a joint meta-IoT structure and sensing function optimization problem and propose efficient algorithms to solve it. Simulation results verify the effectiveness of the design method for the proposed meta-IoT system to achieve reflection coverage broadening.

User authentication is essential for threat defense and data protection. Existed authentication systems have some known limitations, such as spoofing attacks, privacy leakage, and user-unfriendliness. In this paper, we propose BackLip, a novel anti-spoofing authentication system based on lip reading. We employ Wi-Fi backscatter-based technology to recognize users lip reading due to its various advantages, e.g. privacy protection, low power consumption, and low cost. Our system is touch-free and passphrase-independent, making it user-friendly, especially for the elderly and disabled. We first filter out irrelevant interference and enhance the signal-to-noise ratio of lip-reading signals by modulating the backscatter tags and constructing a series of suitable filters. Then, we study the energy distribution and steady-state characteristics of the backscattered signal caused by lip-reading movement at different frequencies to extract passphrase-independen lip-reading fingerprints. We build a theoretical model to analyze and prove the feasibility of our method and design an adaptive correction method to resist the interference caused by distance and angle changes. Additionally, we propose an adaptive segmentation algorithm to label lip-reading motions automatically. Extensive experiments demonstrate that BackLip has an average accuracy of 92.1% and is improved to 96.5% when users use the same passphrases.

Cloud providers, such as AWS and Azure, have started providing 5G services on their cloud infrastructure. In this paper, we present the design and implementation of X-Plane, a system that uses commercial programmable ASICs and DRAM servers on today's cloud infrastructure to implement high-performance 5G User Plane Function (UPF). Building X-Plane is hard because we need to address the following challenges: consistency issues when concurrently accessing UPF state data, slow UE table lookup due to repetitive and numerous Packet Detection Rule (PDR) matching, and the need to handle out-of-order packets from disconnected UEs. X-Plane addresses these challenges by designing three novel technologies: concurrent state data access protocol, fast flow table and paging buffer for handling out-of-order packets. We demonstrate its feasibility and practicality with our implementation on a Tofino-based programmable ASIC. Our evaluation shows that X-Plane can support over ~490Gbps throughput per ASIC pipeline, over 10 million UEs, and finish packet processing within predictable ~4 us on average.

With the widespread adoption of mobile networks, the demand for onboard aircraft network connectivity is growing at a significant pace. However, existing solutions for this air-to-ground (ATG) communication scenario, such as satellite communication or 4G/5G ATG communication, fall short in meeting the increasing throughput demands. To address this challenge, leveraging free-space optical (FSO) technology presents a promising opportunity. FSO links have the capability to provide ultra-high throughput up to Tbps[1], offering a significant advantage over RF solutions. However, to ensure the robustness of an ATG FSO link, two key challenges need to be addressed: (i) guaranteeing high-probability link establishment, and (ii) minimizing link loss to ensure reliable communication. In this demonstration, we propose a novel FSO architecture specifically optimized for the ATG scenario. Our design incorporates a retroreflector-based pure-optical feedback mechanism and a self-adaptive tracking and pointing mechanism to tackle these two challenges respectively. To validate the feasibility of our hardware design, we have developed a prototype system and conducted a series of preliminary experiments.

IoT uplink performance is crucial for a wide variety of IoT applications such as health sensing and industrial control, which demand reliable delivery of sensor data to the cloud. However, due to the limited transmission power budget imposed on many power-constrained IoT devices, IoT uplinks are highly susceptible to cross-technology interference (CTI) caused by coexisting networks. Previous approaches to mitigating CTI have relied on MAC/PHY designs. They suffer from poor performance and limited generality in the presence of wideband CTI sources such as Wi-Fi and RF jammer, which transmit aggressively on large spectrum chunks using diverse radio technologies.

Multipath transport allows the simultaneous use of diverse paths on mobile devices to maximize mobile resource usage. Over the years, we have witnessed several mobile multipath deployment examples by network operators and mobile app providers. However, existing deployment methods require modifications to either the network infrastructure or both endpoints. To lower the bar of the deployment, we present Fleety, a mobile system service that provides the multi-path transport capability with client-only modification. To the best of our knowledge, we are the first to carry out a large-scale mobile multipath deployment that can support hundreds of mobile applications in the cross-ISP setting. This paper is a retrospective of our experience in building and deploying multipath transport for mobile applications. We reveal several practical deployment challenges and share our experience in dealing with them.

Received signal strength (RSS) fingerprint map is one of the most widely-used indoor localization approaches, but it often relies on multiple access points (AP) for data collection and suffers from frequent data updates due to dynamic wireless environments. In this work, we implement a reconfigurable-intelligent-surface (RIS) assisted indoor localization system named Meta2Locate to tackle the above issues using only one AP. In the proposed system, we deploy our self-designed RIS at 5.5GHz in an indoor environment, which can customize the propagation channels between the AP and the target. For the changing propagation environment, we design a mean maximum discrepancy weighted meta-learning approach to train a model that maps the RSS fingerprint to the location of the user, and it only needs a few data for the model update.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

The burgeoning field of smart agriculture is increasingly leveraging unmanned aerial vehicles (UAVs) for data collection. However, inadequate visual features and plant occlusion can hamper visual-based simultaneous localization and mapping (SLAM) of UAVs. As a potential solution, RFID can work as an efficient reference infrastructure, enabling a connection between aerial imagery and real-world contexts. Despite this promise, hurdles remain in attaining high-accuracy, high-throughput, and long-range RFID localization, as well as practical deployment of RFID tags and reader implementation on UAVs. Overcoming these challenges holds significant potential, particularly considering their impact on numerous applications, such as large-scale agricultural management and plant stand reduction detection.

This paper presents CellFusion, a system designed for high-quality, real-time video streaming from vehicles to the cloud. It leverages an innovative blend of multipath QUIC transport and network coding. Surpassing the limitations of individual cellular carriers, CellFusion uses a unique last-mile overlay that integrates multiple cellular networks into a single, unified cloud connection. This integration is made possible through the use of in-vehicle Customer Premises Equipment (CPEs) and edge-cloud proxy servers.

Visible light communications (VLC) have gained significant attention as a potential solution for the radio spectrum crunch. To achieve high data rates, emerging transmitter devices like 2D digital micro-mirror devices (DMD) have been proposed, offering significantly faster state flipping rates compared to conventional liquid crystalline shutters. However, previous approaches utilizing DMD suffered from a lack of spatial diversity, as they used all micro-mirrors in the same state. This paper introduces EV-DMD, a novel approach that utilizes DMD as a 2D transmitter, working in tandem with an event-based vision (EV) camera. In this method, multiple bit streams are transmitted in parallel through different mirror blocks of the DMD, while an EV camera simultaneously decodes multiple light blocks, enabling a truly 2D high-speed VLC system. To the best of our knowledge, this is the first implementation of a 2D VLC system that achieves an order-of-magnitude improvement in bit rate compared to state-of-the-art solutions.

Retroreflective visible light communication and positioning (R-VLCP) is a novel ultralow-power Internet of Things (IoT) technology leveraging indoor light infrastructures. Compared to traditional VLCP, R-VLCP offers several additional favorable features, including self-alignment, low-size, weight, and power (SWaP), glaring-free, and sniff-proof. In analogy to RFID, R-VLCP employs a microwatt optical modulator (e.g., LCD shutter) to manipulate the intensity of the reflected light from a corner-cube retroreflector (CCR) to the photodiodes (PDs) mounted on a light source. In our previous works, we derived a closed-form expression for the retroreflection channel model, assuming that the PD is much smaller than the CCR in geometric analysis. In this article, we generalize the channel model to arbitrary size of PD and CCR. The received optical power is fully characterized relative to the sizes of PD and CCR, and the 3-D location of CCR. We also develop a custom and open-source ray tracing simulator—RetroRay, and use it to validate the channel model. Performance evaluation of area spectral efficiency and horizontal location error is carried out based on the channel model validated by RetroRay. The results reveal that increasing the size of PD and the density of CCRs improves communication and positioning performance with diminishing returns.

With the fast evolution in the cellular communication, the unlicensed spectrum is exploited to resolve the shortage of band resources. The sharing of the unlicensed spectrum extends the applications of LTE and 5G NR techniques, especially in the industrial Internet of Things (IIoT). However, the coexistence problem among various communication technologies in the unlicensed spectrum arises great concerns due to the different communication mechanisms. The existing solutions are either not compatible with the LTE/NR standards or not flexible enough for complex and dynamic IIoT environments. In this paper, we propose a policy learning based unlicensed communication (PLUC) framework to directly learn coexistence policies from the spectrogram of frequency channels. A recurrent neural network (RNN) is built to deal with the observations from time-variant spectrogram and extract deep learning features. We further verify this framework under the duty cycle mechanism and the listen before talk mechanism in 3GPP standards, respectively. The experiments reveal the effectiveness of the proposed framework in the dynamic environment.

Mobile 360° video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient wireless network bandwidth. The state-of-the-art solutions are limited by the temporal correlation assumption. Recent studies are aware of the potential of saliency to further QoE improvement, but several fundamental challenges about saliency judgment, saliency acquirement, and quality adaptation are still not fully addressed. To solve these challenges, we present SalientVR, a saliency-driven mobile 360° video streaming system integrated with gaze information. We design (i) a precise gaze-driven saliency judging criterion for mobile VR viewers, (ii) two pragmatic gaze-driven, tile-level saliency acquiring methods based on cross-user similarity and a specific content-aware deep neural network respectively, and (iii) a lightweight saliency-aware quality adaptation algorithm with a motion-assisted online correction, which is robust to wireless bandwidth vagaries and saliency bias. Moreover, we contribute a gaze-annotated dataset and a gaze-driven quality assessment metric for 360° videos. By extensive prototype evaluations (based on dataset tests and user studies), compared to alternatives, SalientVR significantly enhances the video quality and reduces the rebuffering ratio over 4G/LTE network emulations and in the wild, which achieves a 43.68% QoE improvement.

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.

Visible Light Backscatter Communication (VLBC) presents an emerging ultra-low-power IoT connectivity solution with high spatial-spectral efficiency and intrinsic human-perceivable privacy advantages. However, research progress on enhanced data rate and sophisticated device coordination of state-of-the-art VLBC systems still cannot meet the low-latency requirement (sub-second level for an IoT network) for massive connections.

User mobility prediction is widely considered by the research community. Many studies have explored various algorithms to predict where a user is likely to visit based on their contexts and trajectories. Most of existing studies focus on specific targets of predictions. While successful cases are often reported, few discussions have been done on what happens if the prediction targets vary: whether coarser locations are easier to be predicted, and whether predicting the immediate next location on the trajectory is easier than predicting the destination. On the other hand, while spatiotemporal tags and content information are commonly used in current prediction tasks, few have utilized the finer grained, on-device user behavioral data, which are supposed to be more informative and indicative of user intentions. In this paper, we conduct a systematic study on the mobility prediction using a large-scale real-world dataset that contains plentiful contextual information. Based on a series of learning models, including a Markov model, two recurrent neural network models, and a multi-modal learning method, we perform extensive experiments to comprehensively investigate the predictability of different types of granularities of targets and the effectiveness of different types of signals. The results provide insightful knowledge on what can be predicted along with how, which sheds light on the real-world mobility prediction from a relatively general perspective.

Edge Computing has emerged as a new computing paradigm dedicated for mobile performance enhancement and energy efficiency purposes. Specifically, it benefits today’s interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes in close proximity. Meanwhile, FPGA is well known for its excellence in accelerating (domain-specific) compute-intensive tasks such as deep learning algorithms in a high performance and energy-efficient manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their back-end computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

Massive MIMO forms a crucial component for 5G because of its ability to improve quality of service and support multiple streams simultaneously. However, for real-world MIMO deployments, estimating the downlink wireless channel from each antenna on the base station to every client device is a critical bottleneck, especially for the widely used frequency duplexed designs that cannot utilize reciprocity. Typically, this channel estimation requires explicit feedback from client devices and is prohibitive for large antenna deployments. In this paper, we present FIRE, a system that uses an end-to-end machine learning approach to enable accurate channel estimation without requiring any feedback from client devices. FIRE is interpretable, accurate, and has low compute overhead. We show that FIRE can successfully support MIMO transmissions in a real-world testbed and achieves SNR improvement over 10 dB in MIMO transmissions compared to the current state-of-the-art.

Continuous mobile vision is becoming increasingly important as it finds compelling applications which substantially improve our everyday life. However, meeting the requirements of quality of experience (QoE) diversity, energy efficiency and multi-tenancy simultaneously represents a significant challenge. In this paper, we present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. SCYLLA pre-generates a pool of FPGA design and DNN models, and dynamically applies the optimal software-hardware configuration to achieve the maximum overall performance on QoE for concurrent tasks. We implement SCYLLA on state-of-the-art FPGA platform and evaluate SCYLLA using drone-based traffic surveillance application on three datasets. Our evaluation shows that SCYLLA provides much better design flexibility and achieves superior QoE trade-offs than status-quo CPU-based solution that existing continuous mobile vision applications are built upon.

As the most widely used outdoor navigation system, GPS can provide accurate localization on the horizontal plane. However, the vertical localization accuracy exhibits a poor performance. Vehicles on the elevated road usually receive wrong navigation instructions since GPS fails to detect vehicles' vertical location. In this paper, we present a vertical location system named VLD for vehicles in metropolises mainly leveraging smartphones' barometers, a low-power sensor found in an increasing number of smart devices. When initially on the ground, VLD combines the height and angle detection algorithms to confirm the vertical location with low complexity. Once vehicles have exited the ground and start to travel on the elevated road, a pressure-height model trained by a novel proposed sensor fusion algorithm is activated to measure vehicles' relative height. Then, it tracks the relative height in real time according to a pressure-temperature model which is calibrated by current weather. Comparing the relative height with the single-level elevated road's height, VLD can determine which level vehicles travel on in many highway interchanges and when they exit the elevated road. Experiments covering one month demonstrate that the detection accuracy of VLD exceeds 99% under different weather conditions, and it shows a more accurate relative height measurement compared to available literature, including Baidu Maps and one barometer based application-Altitude. Finally, VLD demonstrates a high efficiency with respect to both the detection delay and power consumption.

Conventional road signs convey very concise and static visual information to human drivers, and bear retroreflective coating for better visibility at night. This paper introduces RetroI2V - a novel infrastructure-to-vehicle (I2V) communication and networking system that renovates conventional road signs to convey additional and dynamic information to vehicles while keeping intact their original functionality. In particular, RetroI2V exploits the retroreflective coating of road signs and establishes visible light backscattering communication (VLBC), and further coordinates multiple concurrent VLBC sessions among road signs and approaching vehicles. RetroI2V features a suite of novel VLBC designs including late-polarization, complementary optical signaling and polarization-based differential reception which are crucial to avoid flickering and achieve long VLBC range, as well as a decentralized MAC protocol that make practical multiple access in highly mobile and transient I2V settings. Experimental results from our prototyped system show that RetroI2V supports up to 101 m communication range and efficient multiple access at scale.

LoRa, which is considered as an appealing wireless technique for Low-Power Wide-Area Networks (LPWANs), has found wide applications in fields such as smart cities, intelligent agriculture. Despite its popularity, there exists a growing concern about secure communications mainly due to the free frequency band and minimalist design specified in LoRa communications. For example, an attacker can forge messages to launch spoofing attack. To mitigate the threat, an authentication mechanism is needed. In this paper, we propose a lightweight node authentication scheme named SLoRa for LoRa networks by leveraging two physical layer features-Carrier Frequency Offset (CFO) and spatial-temporal link signature. In particular, we propose a novel CFO compensation algorithm, and identify slight CFO variations by adopting linear fitting for received upchirps to mitigate the noise's randomness on fine-grained CFO estimation. Besides, we can obtain fine-grained link signatures without the conventional de-convolution operation based on the theoretical analysis. Then, we show how these two physical-layer features complement each other to conquer the drift challenge brought by weather and environment variations. Combining these two features, SLoRa can distinguish whether the received signal is conveyed from a legitimate LoRa node or not. Experiments covering indoor and outdoor scenarios are conducted to demonstrate a high accuracy for node authentication in SLoRa, which is around 97% indoors and 90% outdoors.

Visible light backscatter communication (VLBC) presents an emerging low power IoT connectivity solution with spatial reuse and interference immunity advantages over RF-based (backscatter) technologies. State-of-the-art VLBC systems employ COTS LCD shutter as optical modulator, whose slow response fundamentally throttles its data rate to sub-Kbps, and limits its deployment at scale for use cases where higher rate and/or low latency is a necessity.

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.

High-speed rail (HSR) systems potentially provide a more efficient way of door-to-door transportation than airplane. However, they also pose unprecedented challenges in delivering seamless Internet service for on-board passengers. In this paper, we conduct the first large-scale disconnection-centric measurement study of TCP performance over LTE on HSR. Our measurement targets the main HSR route in China operating at 300/350 km/h. We performed extensive data collection obtaining 378.3 GB data collected over 56639 km of trips. Leveraging such a unique dataset, we measure important performance metrics such as TCP goodput, latency and loss rate across different congestion control algorithm, mobile carrier, and different train speed. We further develop the LTE disconnection taxonomy, and conduct a in-depth correlation study between TCP stall and LTE disconnection. Our findings reveal the networking performance on today's HSR environment “in the wild”, as well as identify several root causes of performance inefficiencies, which together highlight the need to develop dedicated protocol mechanisms that are friendly to extreme mobility.

In recent years, QUIC protocol has shown great advantages for HTTPS over TCP in terms of improving handshake delay and head-of-line blocking. Multipath QUIC (MPQUIC) further opens up the opportunity to leverage path diversity for realizing various optimization goals, especially for mobile access. In this paper, we present a context-aware MPQUIC packet scheduler dedicated to mobile HTTP/2. Specifically, the scheduler takes into account the stream priority (from HTTP/2 dependency tree) for stream-aware downlink packet scheduling by exclusively transferring each stream at a time while maintaining the relative stream completing order. Additionally, ACK packets are scheduled by choosing the path with the lowest one-way delay to reduce overall RTT and expedite loss recovery. Real-world experiments show that our scheduler reduces page load time by up to 8.5% and stream average completion time by up to 12.9% over the status-quo.

Client mobility is a fundamental challenge when accessing the current Internet, especially in the context of vehicular networking because of its intermittent connectivity nature. Meanwhile, today's network applications are evolving from host-to-host communication to content retrieval, and fostering new designs of Information-centric networking (ICN) protocol and system optimized towards this end. In this paper, we present SoftStage, a client instructed ICN-based network layer function that effectively manages the edge caching to perform reactive content staging to improve vehicular content delivery without any assumption about the client mobility pattern. Experimental results based on an implementation in eXpressive Internet Architecture (XIA) shows that SoftStage achieves up to 10x throughput gain in vehicular networking environments.

Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.

Visible Light Backscatter Communication presents an emerging IoT connectivity technology that offers low power and flexible angular alignment benefits. State-of-art approaches with unmodified LCD(s) as optical modulator cause flickering with a sub-kbps low data rate, which would be overcome by modulating polarization, but naive implementation requires careful Tx-Rx placement to avoid signal cancellation. To overcome such challenges, we propose a new modulation scheme called polarization-based quadrature amplitude modulation (PQAM). PQAM constructs the orthogonal basis in the polarization domain and provides 2x throughput gain with arbitrary relative orientation.

High-speed rail (HSR) systems potentially provide a more efficient way of door-to-door transportation than airplane. However, they also pose unprecedented challenges in delivering seamless Internet service for on-board passengers. In this paper, we conduct a large-scale active-passive measurement study of TCP performance over LTE on HSR. Our measurement targets the HSR routes in China operating at above 300 km/h. We performed extensive data collection through both controlled setting and passive monitoring, obtaining 1732.9 GB data collected over 135719 km of trips. Leveraging such a unique dataset, we measure important performance metrics such as TCP goodput, latency, loss rate, as well as key characteristics of TCP flows, application breakdown, and users' behaviors. We further quantitatively study the impact of frequent cellular handover on HSR networking performance, and conduct in-depth examination of the performance of two widely deployed transport-layer protocols: TCP CUBIC and TCP BBR. Our findings reveal the performance of today's commercial HSR networks "in the wild'', as well as identify several performance inefficiencies, which motivate us to design a simple yet effective congestion control algorithm based on BBR to further boost the throughput by up to 36.5%. They together highlight the need to develop dedicated protocol mechanisms that are friendly to extreme mobility.

Visible light backscatter communication (VLBC) has shown great potential in IoT field. LCD, often used as optical modulator in a VLBC system, mainly limits the data rate to sub-Kbps due to its slow state transition. There is a great demand for speed improvement, to support both low latency and high throughput communication in IoT deployment. With the observation of nonlinear and time-varying characteristics of LCD, we present sys, a novel modulation scheme named Delayed Superimposition Modulation (DSM) to achieve 4kbps, which is 4x rate over the status-quo. This design is robust in the presence of LCD heterogeneity in practical setting.

Recently, Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based interactive mobile applications, and implement their backend computation parts on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

No abstract available.

No abstract available.

In this poster, we envision the promising cooperation between autonomous drones and driverless cars. We discuss potential applications and opportunities enabled by this cooperation.

We introduce PassiveVLN, a flexible, modular and software-defined platform for visible light backscatter networks. PassiveVLN incorporates a modular hardware design and a full-stack software implementation, enabling convenient and scalable deployment as well as rapid prototyping for testing new protocols and applications.

Vehicle-to-anything (V2X) communications technology is an essential substrate to realize future road intelligence and autonomous driving, especially in the areas where there are no existing (radio) network infrastructure. The emerging visible light backscatter communication technique shows great potentials in enabling the massive on-road retroreflective objects to delivery dynamic information to host vehicles. In this work, we design a polarization-based differential reception scheme to suppress ambient noise and realize long range retroreflective V2X communications.

With the rapid growth of vehicle ownership, parking has become an issue, especially in metropolitan areas -- the extra time for check-ins, check-outs and finding available parking spaces not only causes frustration and potential road rage on the driver side, but also increases the traffic congestion, gasoline waste and air pollution in consequence. In order to address these problems, the concept of "smart parking" is put forward. To make a parking lot "smart", we argue that three basic features, namely Vehicle Identification, Parking Space Detection and Indoor Localization are are critical and should be supported by the infrastructure. Herein, we present LightPark, a Visible Light Communication (VLC) solution to realize the vision of "smart parking". Building on top of the visible light backscatter communication primitive, LightPark is able to leverage the lighting infrastructure to perform scalable visible light communication and networking with the batter-free tag devices instrumented on the vehicles and parking spaces to manage the critical information such as identification and real-time location of vehicles, and status of parking spaces in a centralized and low-cost manner.

This paper investigates the feasibility of practical backscatter communication using visible light for battery-free IoT applications. Based on the idea of modulating the light retroreflection with a commercial LCD shutter, we effectively synthesize these off-the-shelf optical components into a sub- mW low power visible light passive transmitter along with a retroreflecting uplink design dedicated for power constrained mobile/IoT devices. On top of that, we design, implement and evaluate PassiveVLC, a novel visible light backscatter communication system. PassiveVLC system enables a battery-free tag device to perform passive communication with the illuminating LEDs over the same light carrier and thus offers several favorable features including battery-free, sniff-proof, and biologically friendly for human-centric use cases. Experimental results from our prototyped system show that PassiveVLC is flexible with tag orientation, robust to ambient lighting conditions, and can achieve up to 1 kbps uplink speed. Link budget analysis and two proof-of-concept applications are developed to demonstrate PassiveVLC's efficacy and practicality.

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal.

The number of small wireless devices is rapidly increasing, making the radio channel efficiency in limited geographic areas (individual rooms or buildings) an important metric for MAC protocols. Many of these emerging devices have use-cases that are difficult to satisfy with current hardware solutions and channel access methods; for instance device mobility, small energy reserves, and requirements for low cost and small form factors. However, for most of these applications, such as health care monitoring or sensing, feedback to the radio device is unnecessary and unidirectional communication techniques are not only sufficient, but can also be advantageous. We propose an efficient, reliable technique for unidirectional communication, called Transmit Only (TO), that satisfies these requirements while maintaining packet throughput guarantees and reducing energy consumption. In this paper we will demonstrate the feasibility and performance of this kind of highly asymmetric, transmit-only protocol through theoretical, simulated, and experimental results.

Despite its importance, walking direction is still a key context lacking a cost-effective and continuous solution that people can access in indoor environments. Recently, device-free sensing has attracted great attention because these techniques do not require the user to carry any device and hence could enable many applications in smart homes and offices. In this paper, we present WiDir, the first system that leverages WiFi wireless signals to estimate a human's walking direction, in a device-free manner. Human motion changes the multipath distribution and thus WiFi Channel State Information at the receiver end. WiDir analyzes the phase change dynamics from multiple WiFi subcarriers based on Fresnel zone model and infers the walking direction. We implement a proof-of-concept prototype using commercial WiFi devices and evaluate it in both home and office environments. Experimental results show that WiDir can estimate human walking direction with a median error of less than 10 degrees.

In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.

Applications on wearable personal imaging devices, or Smart-glasses as they are called, can largely benefit from accurate and energy-efficient recognition of objects that are within the user's view. Existing solutions such as optical or computer vision approaches are too energy intensive, while low-power active radio tags suffer from imprecise orientation estimates. To address this challenge, this paper presents the design, implementation, and evaluation of a radio-optical hybrid system where a radio-optical transmitter, or tag, whose radio-optical beacons are used for accurate relative orientation tracking of tagged objects by a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio-optical transmitter and receiver so that extremely short optical (infrared) pulses are sufficient for orientation (angle and distance) estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-to-2 degree accuracy and within 40 cm ranging error, with a maximum range of 9 m in typical indoor use cases. With a tag and receiver battery power consumption of 81 μW and 90 mW, respectively, our radio-optical tags and receiver are at least 1.5x energy efficient than prior works in this space.

Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject's location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the occupied cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

The Internet-of-Things (IoT) has quickly moved from the realm of hype to reality with estimates of over 25 billion devices deployed by 2020. While IoT has huge potential for societal impact, it comes with a number of key security challenges---IoT devices can become the entry points into critical infrastructures and can be exploited to leak sensitive information. Traditional host-centric security solutions in today's IT ecosystems (e.g., antivirus, software patches) are fundamentally at odds with the realities of IoT (e.g., poor vendor security practices and constrained hardware). We argue that the network will have to play a critical role in securing IoT deployments. However, the scale, diversity, cyberphysical coupling, and cross-device use cases inherent to IoT require us to rethink network security along three key dimensions: (1) abstractions for security policies; (2) mechanisms to learn attack and normal profiles; and (3) dynamic and context-aware enforcement capabilities. Our goal in this paper is to highlight these challenges and sketch a roadmap to avoid this impending security disaster.

After a very successful edition of the IoT-App workshop, its second edition is conducted in conjunction with SenSys 2015. Again, the workshop succeeded in attracting a high number of high-quality submissions. The topics of the papers submitted feature most prominently urban sensing and smart home or city. Further directions are programming concepts for IoT devices as well as advances in machine learning, particularly deep learning for IoT devices.

Object recognition on wearable devices using computer vision is too energy intensive and challenging when objects are similar looking, while low-power active radio frequency identification (RFID) systems suffer from imprecise orientation (angle and distance) estimates. To address this challenge, this paper presents a novel radio-optical based recognition system where a radio-optical transmitter, or tag, that emits a beacon whose infra-red (IR) signal strength is used for accurate relative orientation tracking of tagged objects at a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio- optical transmitter and receiver so that extremely short optical pulses are sufficient for precise orientation estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-2° accuracy and within 40cm ranging error, with a maximum range of 9m in typical indoor use cases. With a tag battery power consumption of 86μW, the radio-optical tags show potential to achieve about half a decade lifetimes.

The prevalence of mobile devices especially smartphones has attracted research on mobile content delivery techniques. In this paper, we propose to take advantage of the storage available at wireless access points to bring content closer to mobile devices, hence improving the downloading performance. Specifically, we propose to have a separate popularity based cache and a prefetch buffer at the network edge to capture both long-term and short-term content access patterns. Further, we point out that it is insufficient to rely on a device's past history to predict when and where to prefetch, especially in urban settings; instead, we propose to derive a prediction model based on the aggregated network-level statistics. We discuss the proposed mobile content caching/prefetching method in the context of the MobilityFirst future Internet architecture. In MobilityFirst, when mobile clients move between network attachment points (e.g., Wi-Fi access points), their network association records are logged by the network, which then naturally facilitates the network-level mobility prediction. Through detailed simulations with real taxi mobility traces, we show that such a strategy is more effective than earlier schemes in satisfying content requests at the edge (higher cache hit ratios), leading to shorter content download latencies. Specifically, the fraction of requests satisfied at the edge increases by a factor of 2.9 compared to a caching only approach, and by 45% compared to individual user-based prediction and prefetching.

Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.

Content-centric networking (CCN) adopts a receiver-driven, hop-by-hop transport approach that facilitates in-network caching, which in turn leads to multiple sources and multiple paths for transferring content. In such a case, keeping a single round trip time (RTT) estimator for a multi-path flow is insufficient as each path may experience different round trip times. To solve this problem, it has been proposed to use multiple RTT estimators to predict network condition. In this paper, we examine an alternative approach to this problem, CHoPCoP, which utilizes explicit congestion control to cope with the multiple-source, multiple-path situation. Protocol design innovations of CHoPCoP include a random early marking (REM) scheme that explicitly signals network congestion, and a per-hop fair share Interest shaping algorithm (FISP) and a receiver Interest control method (RIC) that regulate the Interest rates at routers and the receiver respectively. We have implemented CHoPCoP on the ORBIT testbed and conducted experiments under various network and traffic settings. The evaluation shows that CHoPCoP is a viable approach that can effectively deal with congestion in the multipath environment.

Energy conservation on mobile devices is now more important than ever due to the increasing benefits that smartphones and tablets provide to our daily life. However, most existing power management approaches either focus narrowly on a particular sub-system of the mobile device such as the sensor system, the LCD display, or the communication system, or use heuristic approaches to maximize energy efficiency at the cost of user experience. In this paper, we present Boe, a context-aware global power management scheme for mobile devices Balancing battery outage and user experience. To meet the mobile device's expected battery life while sacrificing end user experience as little as possible. Boe takes into account the users' phone usage patterns and activities to dynamically adjust the device's global power management policy to minimize outage time and maximize user experience. We demonstrate our proposed technique by controlling display brightness level and GPS sampling rate on smartphones. We evaluate our approach through real world smartphone data from 10 users over two months. Compared to the best fixed user experience policies, we show that: (i) Boe eliminates all frustrating battery outage events for light, moderate, and heavy phone users, and (ii) Boe improves user experience by 20% for light users, maintains the same user experience for moderate users, and degrades user experience by 23% for heavy smartphone users.

Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -- with an average error distance of 1.5 speakers -- through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment.

Radio frequency based device-free passive (DfP) localization techniques have shown great potentials in localizing individual human subjects, without requiring them to carry any radio devices. In this study, we extend the DfP technique to count and localize multiple subjects in indoor environments. To address the impact of multipath on indoor radio signals, we adopt a fingerprinting based approach to infer subject locations from observed signal strengths through profiling the environment. When multiple subjects are present, our objective is to use the profiling data collected by a single subject to count and localize multiple subjects without any extra effort. In order to address the non-linearity of the impact of multiple subjects, we propose a successive cancellation based algorithm to iteratively determine the number of subjects. We model indoor human trajectories as a state transition process, exploit indoor human mobility constraints and integrate all information into a conditional random field (CRF) to simultaneously localize multiple subjects. As a result, we call the proposed algorithm SCPL -- sequential counting, parallel localizing. We test SCPL with two different indoor settings, one with size 150 m2 and the other 400 m2. In each setting, we have four different subjects, walking around in the deployed areas, sometimes with overlapping trajectories. Through extensive experimental results, we show that SCPL can count the present subjects with 86% counting percentage when their trajectories are not completely overlapping. Our localization algorithms are also highly accurate, with an average localization error distance of 1.3 m.

Augmented Reality (AR) applications benefit from accurate detection of the objects that are within a person's view. Typically, it is not only desirable to identify what is currently within view, but also to navigate the users view to the item of interest - for example, finding a misplaced object. In this paper we demonstrate a low-power hybrid radio-optical beaconing system, where objects of interest are tagged with battery-powered RFID-like tags equipped with infrared light emitting diodes (LED) that emit periodic infrared beacons. These beacons are used for accurately estimating the angle and distance from the object to the receiver so as to locate it. The beacons are synchronized using the radio link that is also used to convey the object's unique ID.

Device-free passive (DfP) localization is proposed to localize human subjects indoors by observing how the subject disturbs the pattern of the radio signals without having the subject wear a tag. In our previous work, we have proposed a probabilistic classification based DfP technique, which we call PC-DfP in short, and demonstrated that PC-DfP can classify which cell (32 cells in total) is occupied by the stationary subject with an accuracy as high as 97.2% in a one-bedroom apartment. In this poster, we focus on extending PC-DfP to track a mobile subject in indoor environments by taking into consideration that a human subject's locations should form a continuous trajectory. Through experiments in a 10 × 15 meters open plan office, we show that we can achieve better accuracies by exploiting the property of continuous mobility trajectories.

Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood.

Device-free passive localization (DfP) techniques can localize human subjects without wearing a radio tag. Being convenient and private, DfP can find many applications in ubiquitous/pervasive computing. Unfortunately, DfP techniques need frequent manual recalibration of the radio signal values, which can be cumbersome and costly. We present SenCam, a sensor-camera collaboration solution that conducts automatic recalibration by leveraging existing surveillance camera(s). When the camera detects a subject, it can periodically trigger recalibration and update the radio signal data accordingly. This technique requires camera access occasionally each month, minimizing computational costs and reducing privacy concerns when compared to localization techniques solely based on cameras. Through experiments in an open indoor space, we show that this scheme can retain good localization results while avoiding manual recalibration.

In this paper, we present the design, implementation and evaluation of a RF-based device-free passive localization strategy using active RFID nodes. Patterns of the measured power on multiple radio links are used to determine the location of a person in a room in a home environment. We develop an adaptive algorithm and training technique to minimize multi-path effects. With experimental deployment in a 5 x 8 meters room, we demonstrate that our system can successfully localize an individual to a 30-inch grid square with an 97.2% accuracy and 0.36 meters average error distance.

A typical wireless sensor network consists of many small sensors that collect instrument data around their locations and forward it to a central location for data processing. These networks can be deployed to monitor livestock and agricultural assets, products in a store, patients in a hospital, and so on. In many cases sensors have to be densely deployed, and collisions or overhead due to collision avoidance will considerably degrade the system performance below an application's required levels. With the decreasing cost of radio devices the obvious solution to this problem is the use of multiple receivers on different radio channels. However, we show that if receivers can be placed in different locations then increasing the number of receivers on a single channel will increase the rate of the capture effect and decrease collision losses, while also increasing the fairness of the transmitters' radio links. Not only can this single channel approach be more effective than using multiple channels, it is also required for some techniques, such as localization, where each receiver must be able to detect a transmission from any transmitter. We also show that the optimal choice between these two solutions is influenced by the radio attenuation rate and the number of receivers in the system.