This paper presents E-Cube, a framework designed to enhance the efficiency of mobile video streaming systems. Our core insight is that content correlations among frames, which are critical for video streaming but challenging to extract in dynamic scenes, are often already available as intermediate outputs from other mobile computing subsystems. By adopting a cross-subsystem design, E-Cube repurposes these intermediate results obtained from event cameras to reduce computation, energy consumption, and bandwidth usage of existing video streaming systems, while simultaneously improving video quality. We implement E-Cube on a drone-embedded chip through software-hardware co-design, and plugged E-Cube into three prevalent drone video streaming systems for industrial inspection. Evaluations in one of the world's largest oil fields and on public datasets demonstrate E-Cube can save over 35% network bandwidth overhead and reduce drone video streaming energy consumption by over 12% for H.265 and AV1, and 47% for advanced H.266, all while achieving improved video quality.

Efficient LLM inference on resource-constrained devices presents significant challenges in compute and memory utilization. Due to limited GPU memory, existing systems offload model weights to CPU memory, incurring substantial I/O overhead between the CPU and GPU. This leads to two major inefficiencies: (1) GPU cores are underutilized, often remaining idle while waiting for data to be loaded; and (2) GPU memory has low impact on performance, as reducing its capacity has minimal effect on overall throughput.In this paper, we propose SpecOffload, a high-throughput inference engine that embeds speculative decoding into offloading. Our key idea is to unlock latent GPU resources for storing and executing a draft model used for speculative decoding, thus accelerating inference at near-zero additional cost. To support this, we carefully orchestrate the interleaved execution of target and draft models in speculative decoding within the offloading pipeline, and propose a planner to manage tensor placement and select optimal parameters. Compared to the best baseline, SpecOffload improves GPU core utilization by 4.49x and boosts inference throughput by 2.54x. Our code is available at https://github.com/MobiSense/SpecOffload-public .

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

This article introduces a radar perception learning framework guided by data collected from commonly equipped visual-inertial (VI) sensor suites on smart vehicles. Unlike existing approaches that rely on dense point clouds from 3D LiDARs, which are costly and not widely deployed, this method leverages the broader availability of VI data. However, visual images alone lack the ability to capture the three-dimensional motion of moving targets, which limits their effectiveness in supervising motion-related tasks. To overcome this limitation, the framework integrates multiple perception tasks such as odometry estimation, motion segmentation, and scene flow prediction into a unified learning process. The first component is an odometry estimation module that combines deterministic ego-motion models with data-driven learning results. This fusion helps accurately infer the scene flow of static background points while minimizing drift. The second component is a supervision signal extraction module that aligns optical and millimeter-wave radar measurements to guide the learning of radar scene flow and rigid transformations. This module improves the reliability of dynamic point supervision through joint constraints across sensing modalities. The third component introduces a feature-selection module designed for cross-modal learning. It enhances the accuracy of motion segmentation and enforces consistency between odometry and scene flow, resulting in more coherent radar perception outputs. Experimental evaluations show that this framework achieves superior performance in challenging conditions such as smoke-obscured environments. It surpasses state-of-the-art (SOTA) methods that depend on high-cost LiDAR systems. The implementation of VISC+ will be open-source athttps://github.com/weini-Eve/VISC

This paper studies the problem of integrated optical camera communication and scene sensing. Due to the tight coupling between background images and stripe information in low signal-to-noise ratio (SNR) encoded images, existing methods cannot simultaneously achieve high-quality optical signal decoding for LED-to-camera communication and background image reconstruction for scene sensing. To address this challenge, this paper analyzes the adversarial characteristics between stripe information and background images, and proposes GANOCCAS, a generative adversarial learning framework tailored for integrated optical camera communication and scene sensing that effectively resolves mutual interference between stripes and background content. First, we design a generator using a CondConv-based 4-layer U-NET architecture with SimAM modules on the last three residual layers and CondConv+PixelShuffle combinations as upsampling layers. Second, we develop a discriminator that combines multi-scale convolutional networks, pooling layers, and residual networks to output stripe sequences for optical signal decoding. Third, by leveraging pixel loss, multi-scale structural similarity loss, and adversarial loss, we ensure that the generator outputs clean background images suitable for scene sensing while the discriminator decodes optical signals for communication in complex environments. Experiments on synthetic and real-world datasets demonstrate that GANOCCAS effectively reduces communication interference from background images and accurately reconstructs stripe-free background images across various SNR scenarios, outperforming current state-of-the-art methods in both reflected OCC and scene sensing tasks.

This work proposes a mmWave radar’s scene flow estimation framework supervised by data from a widespread visual-inertial (VI) sensor suite, allowing crowdsourced training data from smart vehicles. Current scene flow estimation methods for mmWave radar are typically supervised by dense point clouds from 3D LiDARs, which are expensive and not widely available in smart vehicles. While VI data are more accessible, visual images alone cannot capture the 3D motions of moving objects, making it difficult to supervise their scene flow. Moreover, the temporal drift of VI rigid transformation also degenerates the scene flow estimation of static points. To address these challenges, we propose a drift-free rigid transformation estimator that fuses kinematic model-based ego-motions with neural network-learned results. It provides strong supervision signals to radar-based rigid transformation and infers the scene flow of static points. Then, we develop an optical-mmWave supervision extraction module that extracts the supervision signals of radar rigid transformation and scene flow. It strengthens the supervision by learning the scene flow of dynamic points with the joint constraints of optical and mmWave radar measurements. Extensive experiments demonstrate that, in smoke-filled environments, our method even outperforms state-of-the-art (SOTA) approaches using costly LiDARs.

Grounding natural language instructions to visual observations is fundamental for embodied agents operating in open-world environments. Recent advances in visual-language mapping have enabled generalizable semantic representations by leveraging visionlanguage models (VLMs). However, these methods often fall short in aligning free-form language commands with specific scene instances, due to limitations in both instance-level semantic consistency and instruction interpretation. We present OpenMap, a zero-shot open-vocabulary visual-language map designed for accurate instruction grounding in navigation tasks. To address semantic inconsistencies across views, we introduce a Structural-Semantic Consensus constraint that jointly considers global geometric structure and vision-language similarity to guide robust 3D instancelevel aggregation. To improve instruction interpretation, we propose an LLM-assisted Instruction-to-Instance Grounding module that enables fine-grained instance selection by incorporating spatial context and expressive target descriptions. We evaluate OpenMap on ScanNet200 and Matterport3D, covering both semantic mapping and instruction-to-target retrieval tasks. Experimental results show that OpenMap outperforms state-of-the-art baselines in zero-shot settings, demonstrating the effectiveness of our method in bridging free-form language and 3D perception for embodied navigation.

Optical motion capture is a foundational technology driving advancements in cutting-edge fields such as virtual reality and film production. However, system performance suffers severely under large-scale marker occlusions common in real-world applications. An in-depth analysis identifies two primary limitations of current models: (i) the lack of training datasets accurately reflecting realistic marker occlusion patterns, and (ii) the absence of training strategies designed to capture long-range dependencies among markers. To tackle these challenges, we introduce the CMU-Occlu dataset, which incorporates ray tracing techniques to realistically simulate practical marker occlusion patterns. Furthermore, we propose OpenMoCap, a novel motion-solving model designed specifically for robust motion capture in environments with significant occlusions. Leveraging a marker-joint chain inference mechanism, OpenMoCap enables simultaneous optimization and construction of deep constraints between markers and joints. Extensive comparative experiments demonstrate that OpenMoCap consistently outperforms competing methods across diverse scenarios, while the CMU-Occlu dataset opens the door for future studies in robust motion solving. The proposed OpenMoCap is integrated into the MoSen MoCap system for practical deployment. The code is released at: https://github.com/qianchen214/OpenMoCap.

This paper considers downlink power allocation and beamforming (PABF) for integrated sensing and communications (ISAC) against multipath interference. Yet, ISAC-oriented PABF is of great difficulty, due to its parameter-coupling structure and non-convex problem nature. A novel PABF method is proposed to address this issue. Firstly, in order to handle its complex problem structure, the PABF problem is divided into three subproblems, where communication-end beamformer, sensing-end beamformer and multipath power vector are decoupled. Secondly, structured models of the complex problem are extracted to address the non-convexity challenge. An efficient alternating optimization-based PABF algorithm with closed-form iterations is obtained. At the sensing receiver end, our PABF method can focus beams at the line-of-sight direction, while form null beams at reflection directions for suppressing multipath interference. Simultaneously, at the communication transceiver ends, it can smartly adjust beam gains and transmitting power over multiple paths to maximize the communication performance while ensuring a promised sensing performance. The proposed PABF algorithm can strike an on-demand communication and sensing performance tradeoff, via adjusting the sensing performance requirement. We have verified the efficiency of our PABF method by numerical simulations.

Recent years have witnessed an increasing number of mobile devices, posing a more diversified demand for device localization solutions. Existing methods can locate connected devices but fail to address the spatial proximity between devices lacking direct communication links. This limitation impedes numerous emerging applications, such as implicit control of IoT device and proximity-based autonomous aerial vehicles scheduling. In response to this technical challenge, we introduce RF-Prox, the pioneering system designed for the proximity estimation of nondirectly connected devices. RF-Prox determines the proximity between devices by extracting and analyzing the spatiotemporal correlation between two signals. RF-Prox introduces a multiresolution spatiotemporal encoder (MRSTE) that extracts multiscale features from complex-valued wireless signals, capturing both spatial and dynamic temporal characteristics. Additionally, the proximity metric adaptation network (PMAN) bridges the gap between high-dimensional signal characteristics and physical proximity. To enhance scalability, we leverage a transfer learning framework, significantly reducing the need for extensive data collection and retraining. Extensive experiments demonstrate RF-Prox’s outstanding performance across Wi-Fi and cellular networks, achieving fine-tuned accuracy rates of 98.6% indoors and 91.3% outdoors. Even without fine-tuning, the pretrained model achieves strong zero-shot performance, showcasing its exceptional performance in both proximity estimation accuracy and domain generalizability.

Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into network switches. By combining control and transmission functions in switches, CaaS virtualizes the whole industrial network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presentsBioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone are three simple yet effective system designs inspired by the mammalian visual system, namely, a chiasm-inspired event filtering, a lateral geniculate nucleus (LGN)-inspired event matching, and a dorsal stream-inspired obstacle tracking. We implement BioDrone on FPGA through software-hardware co-design and deploy it on an industrial drone. In comparative experiments against two state-of-the-art event-based systems, BioDrone consistently achieves an obstacle detection rate of$> $90%, and an obstacle tracking error of$<$5.8 cm across all flight modes with an end-to-end latency of$<$6.4 ms, outperforming both baselines by over 44%.

Many mobile crowdsensing applications require efficient recruitment of workers whose qualities are often unknown a priori. While prior research has explored multi-armed bandit-based mechanisms with short-term incentives to address this unknown worker recruitment challenge, these mechanisms mostly neglect the enduring participation issues stemming from privacy concern and selection starvation in the long-term task. Therefore, in this paper, we focus on incentivizing long-term participation of unknown workers, thereby providing crucial assurance for crowdsensing applications. We first establish an auction framework based on shuffle differential privacy (SDP), where we leverage SDP’s privacy amplification effect to mitigate privacy-related utility loss when dealing with the privacy-sensitive worker and the utility-sensitive platform. Following this, we model the selection requirements of workers as fairness constraints and propose two novel fairness-aware incentive mechanisms, GFA and IFA, to ensure group and individual fairness for unknown workers, respectively. Theoretical analyses highlight the desirable properties of GFA and IFA, complemented by an in-depth exploration of fairness violation and regret. Finally, numerical simulations are conducted on two real-world datasets, validating the superior performance of the proposed mechanisms.

Time-Sensitive Networking (TSN) is foreseen as a foundational technology that enables Industry 4.0. It offers deterministic data transmission over Ethernet for critical applications such as industrial control and automotive systems. However, TSN is susceptible to hardware and software errors, necessitating an effective diagnostic system. Traditional network diagnostic tools are inadequate for TSN fault localization and classification due to the tightly coupled traffic and high precision requirements in TSN. In response, this paper presents TSNCard, a cross-cycle postcard-based diagnostic system tailored for Time-Aware Shaper (IEEE 802.1 Qbv) in TSN. TSNCard introduces a novel telemetry protocol that leverages the cyclical nature of TSN networks for data collection at each node. This protocol, coupled with dedicated analytic algorithms and hardware innovations within switches, forms a comprehensive system for TSN monitoring, fault localization and classification. Extensive experiments on both simulation and physical testbeds show that TSNCard can 100% detect fault location and type of the TSN misbehavior while adhering to industrial bandwidth restrictions. TSNCard not only bridges the gap in the TSN protocol stack, but also serves as a versatile toolkit for time-synchronized network analysis, paving the way for future research. The code is available athttps://github.com/MobiSense/TSNCard

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

Drones have demonstrated their pivotal role in various applications such as search-and-rescue, smart logistics, and industrial inspection, with accurate localization playing an indispensable part. However, in high dynamic range and rapid motion scenarios, traditional visual sensors often face challenges in pose estimation. Event cameras, with their high temporal resolution, present a fresh opportunity for perception in such challenging environments. Current efforts resort to event-visual fusion to enhance the drone’s sensing capability. Yet, the lack of efficient event-visual fusion algorithms and corresponding acceleration hardware causes the potential of event cameras to remain underutilized. In this paper, we introduce EventBoost, an acceleration platform designed for drone-based applications with event-image fusion. We propose a suit of novel algorithms through software-hardware co-design on Zynq SoC, aimed at enhancing real-time localization precision and speed. EventBoost achieves enhanced visual fusion precision and markedly elevated processing efficiency. The performance comparison with two state-of-the-art systems shows EventBoost achieves 24.33% improvement in accuracy with 30 ms latency on resource-constrained platforms.

Edge-assisted visual SLAM stands as a pivotal enabler for emerging mobile applications, such as search-and-rescue, smart logistics, and industrial inspection. Limited by the computing capability of lightweight mobile devices like MAVs, current innovations balance system accuracy and efficiency by allocating lightweight and time-sensitive tracking tasks to mobile devices, while offloading the more resource-intensive yet delay-tolerant map optimization tasks to the edge. However, our pilot study in a large-scale oil field reveals several limitations of such a tracking-optimization decoupled paradigm, arising due to the disruption of inter-dependencies between the two tasks concerning data, resources, and threads.In this paper, we design and implement edgeSLAM2, an innovative system that reshapes the edge-assisted visual SLAM paradigm by tightly integrating tracking and partial-yet-crucial optimization on mobile. edgeSLAM2 harnesses the hierarchical and heterogeneous computing units offered by the latest commercial systems-on-chip (SoCs) to enhance the computational capacity of mobile devices, which in turn, allows edgeSLAM2 to design a suit of novel algorithms for map sync, optimization, and tracking that accommodate such architectural upgrade. By fully embracing the on-chip intelligence, edgeSLAM2 simultaneously enhances system accuracy and efficiency through software-hardware co-design. We deploy edgeSLAM2 on an industrial drone and conduct comprehensive experiments in a large-scale oil field over three months. The results show that edgeSLAM2 surpasses comparative methods by achieving an 80% reduction in bandwidth consumption, a 32% improvement in accuracy, and a 26% reduction in tracking delay.

Time-Sensitive Networking (TSN) is an enabling technology for Industry 4.0. Traffic scheduling plays a key role for TSN to ensure low-latency and deterministic transmission of critical traffic. As industrial network scales, TSN networks are expected to support a rising number of both time-triggered and event-triggered critical traffic (TCT and ECT). In this work, we present InNetScheduler, the first in-network TSN scheduling paradigm that boosts the throughput, i.e., number of scheduled data flows, of both traffic types. Different from existing approaches that conduct entire scheduling on the server, InNetScheduler leverages the computation resources on switches to promptly schedule latency-critical ECT, and delegate the computational-intensive TCT scheduling to server. The key innovation of InNetScheduler includes a Load-Aware Optimizer to mitigate ECT conflicts, a Relaxated ECT Scheduler to accelerate in-network computation, and End-to-End Determinism Guarantee to lower scheduling jitter. We fully implement a suite of InNetScheduler-compatible TSN switches with hardwaresoftware co-design. Extensive experiments are conducted on both simulation and physical testbeds, and the results demonstrate InNetScheduler’s superior performance. By unleashing the power of in-network computation, InNetScheduler points out a direction to extend the capacity of existing industrial networks.

Time-Sensitive Networking (TSN) is foreseen as a foundational technology that enables Industry 4.0. It offers deterministic data transmission over Ethernet for critical applications such as industrial control and automotive systems. However, TSN is susceptible to hardware and software errors, necessitating an effective diagnostic system. Traditional network diagnostic tools are inadequate for TSN fault localization due to the unique characteristics of TSN. In response, this paper presents TSNCard, a cross-cycle postcard-based diagnostic system tailored for TSN. TSNCard introduces a novel telemetry protocol that leverages the cyclical nature of TSN networks for data collection at each node. This protocol, coupled with dedicated analytic algorithms and hardware innovations within switches, forms a comprehensive system for TSN monitoring and fault localization. Extensive experiments on both simulation and physical testbeds show that TSNCard can 100% localize the root cause of the TSN misbehavior while adhering to industrial bandwidth restrictions. TSNCard not only bridges the gap in the TSN protocol stack, but also serves as a versatile toolkit for time-synchronized network analysis, paving the way for future research.

Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adapt it to the RF domain and propose RF-Diffusion. To accommodate the unique characteristics of RF signals, we first introduce a novel Time-Frequency Diffusion theory to enhance the original diffusion model, enabling it to tap into the information within the time, frequency, and complex-valued domains of RF signals. On this basis, we propose a Hierarchical Diffusion Transformer to translate the theory into a practical generative DNN through elaborated design spanning network architecture, functional block, and complex-valued operator, making RF-Diffusion a versatile solution to generate diverse, high-quality, and time-series RF data. Performance comparison with three prevalent generative models demonstrates the RF-Diffusion's superior performance in synthesizing Wi-Fi and FMCW signals. We also showcase the versatility of RF-Diffusion in boosting Wi-Fi sensing systems and performing channel estimation in 5G networks.

Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radar or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over an edge, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline performance by 86%.

Depth estimation is crucial for numerous applications, including autonomous driving, robotic navigation and aug-mented reality. Existing solutions based on LiDAR and mm Wave technologies are constrained by high deployment costs, while those utilizing monocular vision suffer from limited accuracy. To address these challenges, this paper proposes WiViD, a diffusion-based depth estimation system that leverages commercial Wi-Fi and vision. Diffusion models, with their ability to iteratively refine predictions, offer significant advantages in producing accurate and detailed estimations. We introduce a Multimodal Conditional Diffusion (MMCD) mechanism and design two encoding modules: the Complex-Valued CSI Encoder (CCE) and the Residual Image Encoder (RIE). These components fully exploit the spatio-temporal information inherent in Wi-Fi CSI and enable the effective fusion of Wi-Fi CSI and RGB image data, which results in high-precision and robust depth estimation. Experimental results in real-world scenarios demonstrate that WiViD out-performs state-of-the-art (SOTA) monocular methods, reducing the Absolute Relative Error (ARE) by 67.2 %, highlighting the advantages of WiViD in terms of accuracy and reliability.

Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

Mobile edge computing (MEC) brings abundant computing resources to the edge networks, which supports users in offloading their tasks to the edge instead of the cloud, thereby reducing service delay and improving users' quality of experience. In this paper, we consider a three-tier multi-user multi-task offloading model, which contains multiple users with each user possessing multiple tasks, multiple base stations (BSs) with edge servers and a remote cloud. Taking into account the selfishness of individuals in the MEC system, we respectively formulate optimization problems for users, BSs and the cloud. Users aim to make their offloading strategies to minimize their respective costs, while BSs and the cloud aim to make their computation resource allocation decisions to minimize their respective task completion delays. We model the interaction among these selfish individuals based on Stackelberg game, where users act as leaders and BSs and the cloud act as followers. By using backward induction, we prove the existence of Stackelberg Equilibrium (SE). We further propose a distributed algorithm that enables the system to reach the SE, which includes three user selection strategies for the BSs. The numerical results demonstrate the superiority of the proposed scheme compared with several approaches.

The complexity of traffic scenarios, the spatial-temporal feature correlations pose higher challenges for traffic prediction research. Traffic spatial-temporal model is an essential method in this research field, primarily focusing on capturing the spatial-temporal features among nodes and their neighboring nodes. However, existing methods lack comprehensive consideration of directional and hierarchical features among traffic nodes. They are mostly applicable to scenarios with random uniform distribution of nodes, but not suitable for more complex small-scale aggregation distribution scenarios. Therefore, this study proposes the Tree Convolutional Network (TreeCN), a tree-based structure. The data design and model design of TreeCN focus on capturing the directional and hierarchical features among nodes. The directional and hierarchical relationships among nodes are represented by the plane tree matrix and constructed as the spatial tree matrix. The TreeCN, with a full convolution network, performs a bottom-up convolution structure on the tree matrix to complete the task of node feature capturing. In this study, TreeCN is thoroughly compared with statistical, machine learning, and deep learning methods in traffic time series prediction. The experimental results show that TreeCN not only performs well in scenarios with random uniform distribution but also exhibits outstanding effect in more complex small-scale aggregation distribution. Moreover, TreeCN adheres to the design principles of Graph Convolutional Networks (GCN) in capturing the spatial features of traffic nodes and can further capture directional and hierarchical features among them. This is expected to make TreeCN a new method to handle complex traffic scenarios and improve prediction accuracy.

Environment depth estimation by fusing camera and radar enables a broad spectrum of applications such as autonomous driving, environmental perception, context-aware localization and navigation. Various pioneering approaches have been proposed to achieve accurate and dense depth estimation by integrating vision and LiDAR through deep learning. However, due to the challenges of sparse sampling of in-vehicle LiDARs, high ground-truth annotation overhead, and severe dynamics in real environments, existing solutions have not yet achieved widespread deployment on commercial autonomous vehicles. In this paper, we propose LeoVR, a motion-inspired self-supervised visual-LiDAR fusion approach that enables accurate environment depth estimation. Leveraging the vehicle motion information, LeoVR employs two effective system frameworks to$(i)$optimize the depth estimation results, and$(ii)$provide supervision signals for DNN training. We fully implemented LeoVR on both a robotic testbed and a commercial vehicle and conducted extensive experiments over an 8-month period. The results demonstrate that LeoVR achieves remarkable performance with an average depth estimation error of 0.17$m$, outperforming existing state-of-the-art solutions by$\gt $45.9%. Besides, even cold-start in real environments by self-supervised training, LeoVR still achieves an average error of 0.2$m$, outperforming the related works by$\gt $47.8% and comparable to supervised training methods.

Edge-assisted visual SLAM plays a crucial role in enabling innovative mobile applications, such as autonomous swarm inspection, search-and-rescue, and smart logistics. Constrained by the computational capacities of lightweight mobile devices, current approaches delegate lightweight, time-sensitive tracking tasks to the mobile end while offloading resource-intensive, latency-tolerant map optimization tasks to the edge. However, our pilot study reveals several limitations of the tracking-optimization decoupled paradigm, stemming from the disruption of inter-dependencies between the two tasks. In this paper, we design and implement edgeSLAM2, an innovative system that reshapes the edge-assisted visual SLAM paradigm by tightly integrating tracking and partial-yet-crucial optimization on mobile. edgeSLAM2 harnesses the heterogeneous computing units offered by the commercial systems-on-chip (SoCs) to enhance the computational capacity of mobile devices, which in turn, allows edgeSLAM2 to design a suit of novel algorithms for map sync, optimization, and tracking that accommodate such architectural upgrade. By capitalizing on the full potential of on-chip intelligence, edgeSLAM2 supports both solitary and collaborative SLAM with accuracy and immediacy, underpinned by a cohesive software-hardware co-design. We deploy edgeSLAM2 on drones for industrial inspection. Comprehensive experiments in one of the world’s largest oil fields over three months demonstrate its superior performance.

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

The edge-based multi-agent visual SLAM is crucial for emerging mobile applications like search-and-rescue, inventory automation, and industrial inspection. It uses a central node to manage the global map and schedule tasks for agents. However, as the number of agents increases, the system faces scalability challenges due to operational overhead, such as data redundancy, bandwidth consumption, and localization errors. In this paper, we introduce, a framework designed to enhance the scalability of collaborative visual SLAM service in edge offloading settings. consists of three system modules: a change log-based server-client synchronization mechanism, a priority-aware task scheduler, and a lean global map representation. These modules work together to address the challenges of data explosion problems. is open-source and compatible with the robotic operating system (ROS). Existing visual SLAM applications could incorporate through SwarmAPI, a set of well-packaged APIs, to compose SwarmMap’s function modules to enhance their performance and capacity in multi-agent scenarios. Comprehensive evaluations and a three-month case study at one of the world’s largest oilfields demonstrate that can serve 2$\times$more agents ($>$20 agents) than the state-of-the-arts with the same resource overhead, meanwhile maintaining an average trajectory error of 38$cm$, outperforming existing works by$>$55%.

Drones have witnessed extensive popularity among diverse smart applications, and visual Simultaneous Localization and Mapping (SLAM) technology is commonly used to estimate the six-degrees-of-freedom pose for drone flight control systems. However, traditional image-based SLAM cannot ensure the flight safety of drones, especially in challenging environments such as high-speed flight and high dynamic range scenarios. The event camera, a new vision sensor, holds the potential to enable drones to overcome these challenging scenarios if fused with the image-based SLAM. Unfortunately, the computational demands of event-image fusion SLAM have grown manifold compared with image-based SLAM. Existing research on visual SLAM acceleration cannot achieve real-time operation of event-image fusion SLAM on on-board computing platforms for drones. To fill this gap, we present TrinitySLAM , a high-accuracy, real-time, low-energy consumption event-image fusion SLAM acceleration framework utilizing Xilinx Zynq, an on-board heterogeneous computing platform. The key innovations of TrinitySLAM include a fine-grained computation allocation strategy, several novel hardware–software co-acceleration designs, and an efficient data exchange mechanism. We fully implement TrinitySLAM on the latest Zynq UltraScale+ platform and evaluate its performance on one custom-made drone dataset and four official datasets covering various scenarios. Comprehensive experiments show that TrinitySLAM improves the pose estimation accuracy by 28% with half end-to-end latency and 1.2× energy consumption reduction compared with the most comparable state-of-the-art heterogeneous computing platform acceleration baseline.

Most existing multi-user Augmented Reality (AR) systems only support multiple co-located users to view a common set of virtual objects but lack the ability to enable each user to directly interact with other users appearing in his/her view. Such multi-user AR systems should be able to detect the human keypoints and estimate device poses (for identifying different users) in the meantime. However, due to the stringent low latency requirements and the intensive computation of the preceding two capabilities, previous research only enables either of the two capabilities for mobile devices even with the aid of the edge server. Integrating the two capabilities is promising but non-trivial in terms of latency, accuracy, and matching. To fill this gap, we propose DiTing to achieve real-time ID-aware multi-device visual interaction for multi-user AR applications, which contains three key innovations: Shared On-device Tracking to merge the similar computation for optimized latency, Tightly Coupled Dual Pipeline to enhance the accuracy of each task through mutual assistance, and Body Affinity Particle Filter to precisely match device poses with human bodies. We implement DiTing on four types of mobile AR devices and develop a multi-user AR game as a case study. Extensive experiments show that DiTing can provide high-quality human keypoint detection and pose estimation in real time (30fps) for ID-aware multi-device interaction and outperform the state-of-the-art baseline approaches.

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this article, we propose iToLoc, a deep learning-based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training-based semi-supervised learning framework. Extensive experiments across 7 months with eight different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92 m and >95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains >90%, which significantly outperforms previous works.

Object segmentation aims to perfectly identify objects embedded in the surrounding environment and has a wide range of applications. Most previous methods of object segmentation only use RGB images and ignore geometric information from disparity images. Making full use of heterogeneous data from different devices has proved to be a very effective strategy for improving segmentation performance. The key challenge of the multimodal fusion-based object segmentation task lies in the learning, transformation, and fusion of multimodal information. In this article, we focus on the transformation of disparity images and the fusion of multimodal features. We develop a multimodal fusion object segmentation framework, termed the Hybrid Fusion Segmentation Network (HFSNet). Specifically, HFSNet contains three key components, i.e., disparity convolutional sparse coding (DCSC), asymmetric dense projection feature aggregation (ADPFA), and multimodal feature fusion (MFF). The DCSC is designed based on convolutional sparse coding. It not only has better interpretability but also preserves the key geometric information of the object. ADPFA is designed to enhance texture and geometric information to fully exploit nonadjacent features. MFF is used to perform multimodal feature fusion. Extensive experiments show that our HFSNet outperforms existing state-of-the-art models on two challenging datasets.

This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2 m .

Accurate localization of unmanned aerial vehicle (UAV) is critical for navigation in GPS-denied regions, which remains a highly challenging topic in recent research. This article describes a novel approach to multi-sensor hybrid coupled cooperative localization network (HCCNet) system that combines multiple types of sensors including camera, ultra-wideband (UWB), and inertial measurement unit (IMU) to address this challenge. The camera and IMU can automatically determine the position of UAV based on the perception of surrounding environments and their own measurement data. The UWB node and the UWB wireless sensor network (WSN) in indoor environments jointly determine the global position of UAV, and the proposed dynamic random sample consensus (D-RANSAC) algorithm can optimize UWB localization accuracy. To fully exploit UWB localization results, we provide an HCCNet system which combines the local pose estimator of visual inertial odometry (VIO) system with global constraints from UWB localization results. Experimental results show that the proposed D-RANSAC algorithm can achieve better accuracy than other UWB-based algorithms. The effectiveness of the proposed HCCNet method is verified by a mobile robot in real world and some simulation experiments in indoor environments.

Recent years have witnessed an increasing number of mobile devices, posing a more diversified demand for device localization solutions. While existing wireless localization solutions can obtain the relative locations of connected devices, they fall short in estimating the spatial relationships between devices that are not directly connected. To address this technical gap, we propose Wi-Prox, the first proximity estimation system for non-directly connected devices. Wi-Prox evaluates the spatial proximity of two devices by analyzing their received wireless signals. It integrates a novel multi-resolution spatial encoder that extracts multi-scale spatial features from complex-valued wireless signals, which are then analyzed and transformed into a domain-adaptive proximity metric. To enhance the general-izability of Wi-Prox, we adopt a simulation-to-reality transfer learning framework. Wi-Prox is pre-trained with a large amount of simulated data and then fine-tuned for real-world deployment, significantly reducing the need for real-world data collection. We implement Wi-Prox and evaluate its performance in both simulated and real environments. Our results indicate that a fine-tuned Wi-Prox achieves an average accuracy of 97.2% in selecting the most proximate device. Even without fine-tuning, a pre-trained Wi-Prox still manages an average accuracy of 93.8%, thereby demonstrating impressive performance in terms of both proximity estimation accuracy and domain generalizability.

With the development of intelligent transportation and human-robot interaction systems in recent years, the demand for multi-future trajectory prediction is increasing rapidly. A key challenge is to model the stochasticity of future agents motion, which affects the prediction for Multi-Future Trajectory directly. Numerous trajectory prediction methods have been proposed by using heterogeneous data composed of High Definition (HD) maps and agents' history trajectories to model the future agents motion. As we know, regions where there is an object will enter contain more valuable features than regions where there is an object has already passed through. But existing methods are usually influenced by the passed regions in HD maps, which leads to unreasonable predictions. To mitigate this problem, we present a conditional variational autoencoder based model, called Heterogeneous Aware Network (HANet), to forecast trajectories of agents in real-world scenes. The HANet is embedded with a carefully designed Discriminative Region Exploring module (DRE) to encode heterogeneous data. We also tailor a Predictive Sampling Strategy (PSS) to help the DRE focus on regions that agents will enter. The proposed HANet achieves competitive results on a real-world public dataset nuScenes compared with SOTA methods.

Time-Sensitive Networking (TSN) has been considered the most promising network paradigm for time-critical applications (e.g., industrial control) and traffic scheduling is the core of TSN to ensure low latency and determinism. With the demand for flexible production increases, industrial network topologies and settings change frequently due to pipeline switches. As a result, there is a pressing need for a more efficient TSN scheduling algorithm. In this paper, we propose DeepScheduler, a fast and scalable flow-aware TSN scheduler based on deep reinforcement learning. In contrast to prior work that heavily relies on expert knowledge or problem-specific assumptions, DeepScheduler automatically learns effective scheduling policies from the complex dependency among data flows. We design a scalable neural network architecture that can process arbitrary network topologies with informative representations of the problem, and decompose the problem decision space for efficient model training. In addition, we develop a suite of TSN-compatible testbeds with hardware-software co-design and DeepScheduler integration. Extensive experiments on both simulation and physical testbeds show that DeepScheduler runs >150/5 times faster and improves the schedulability by 36%/39% compared to state-of-the-art heuristic/expert-based methods. With both efficiency and effectiveness, DeepScheduler makes scheduling no longer an obstacle towards flexible manufacturing.

Location awareness in environments is one of the key parts for drones’ applications and have been explored through various visual sensors. However, standard cameras easily suffer from motion blur under high moving speeds and low-quality image under poor illumination, which brings challenges for drones to perform motion tracking. Recently, a kind of bio-inspired sensors called event cameras emerge, offering advantages like high temporal resolution, high dynamic range and low latency, which motivate us to explore their potential to perform motion tracking in limited scenarios. In this paper, we propose FlyTracker, aiming at developing visual sensing ability for drones of both individual and circumambient location-relevant contextual, by using a monocular event camera. In FlyTracker, background-subtraction-based method is proposed to distinguish moving objects from background and fusion-based photometric features are carefully designed to obtain motion information. Through multilevel fusion of events and images, which are heterogeneous visual data, FlyTracker can effectively and reliably track the 6-DoF pose of the drone as well as monitor relative positions of moving obstacles. We evaluate performance of FlyTracker in different environments and the results show that FlyTracker is more accurate than the state-of-the-art baselines.

Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into Time-Sensitive Networking (TSN) switches. By combining control and transmission functions in switches, CaaS virtualizes the industrial TSN network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presents BioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone is two simple yet effective system design inspired by the mammalian visual system, namely, a chiasm-inspired signal processing pipeline for fast event filtering and obstacle detection, and a lateral geniculate nucleus (LGN)-inspired event matching algorithm for accurate obstacle localization. To make BioDrone a practical solution, we further take significant engineering efforts to deploy the software stack on FPGA through software and hardware co-design. The performance comparison with two state-of-the-art event-based obstacle avoidance systems shows BioDrone achieves a consistently high obstacle detection rate of 96.1%. The average localization error of BioDrone is 6.8cm with a 4.7ms latency, outperforming both baselines by over 40%.

Rapid advances in programmable network devices catalyzed the development of in-network computing, which is foreseen as a key enabler to empower the intelligence of production lines and mechanical arms in Industry 4.0. Various pioneering approaches have demonstrated the significant benefits of moving simple yet delay-sensitive industrial control tasks performed by servers to network switches. However, our detailed field study at a top-tier auto glass factory reveals that current practice fails to achieve a real-time and deterministic intelligent decision closure as leaving those complex yet essential planning tasks still on edge or cloud. In this paper, we design and implement a brand-new industrial switch, named Netopia, on a commercial Zynq platform through software and hardware co-design. Netopia enables planning and control to simultaneously perform on a network switch during communication. At the core of Netopia are three simple yet effective modules - a determinism guarantee mechanism, a computing acceleration scheme, and a packet deterministic forwarding framework that work hand-in-hand to ensure mechanical arms obtain intelligent control commands with low and deterministic latency. Comprehensive evaluations in industrial environments demonstrate that Netopia achieves an average end-to-end intelligent decision latency of 3.0ms with a jitter < 0.4ms, reduced by > 86% over existing works.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts. In recent years, visual SLAM has been prevailing for autonomous driving. However, the ever-growing computation resources demanded by SLAM impede its applications to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation ofedgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices.edgeSLAMleverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations ofedgeSLAMinclude an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implementedgeSLAMand plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show thatedgeSLAMcan run on mobile devices at 35fps and achieve 5cm localization accuracy from real-world experiments, outperforming existing solutions by more than 15%. We also demonstrate the usability ofedgeSLAMthrough 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge,edgeSLAMis the first edge-assisted real-time semantic visual SLAM for mobile devices.

Indoor navigation is essential to a wide spectrum of applications in the era of mobile computing. Existing vision-based technologies suffer from both start-up costs and the absence of semantic information for navigation. We observe an opportunity to leverage pervasively deployed surveillance cameras to deal with the above drawbacks and revisit the problem of indoor navigation with a fresh perspective. In this paper, we proposeiSAT, a system that enables public surveillance cameras, as indoor navigating satellites, to locate users on the floorplan, tell users with semantic information about the surrounding environment, and guide users with navigation instructions. However, enabling public cameras to navigate is non-trivial due to 3 factors: absence of real scale, disparity of camera perspective, and lack of semantic information. To overcome these challenges,iSATleverages POI-assisted framework and adopts a novel coordinate transformation algorithm to associate public and mobile cameras, and further attaches semantic information to user location. Extensive experiments in 4 different scenarios show thatiSATachieves a localization accuracy of 0.48m and a navigation success rate of 90.5 percent, outperforming the state-of-th-art systems by$> 30\%$. Benefiting from our solution, all areas with public cameras can upgrade to smart spaces with visual navigation services.

CNN based real-time object detection can facilitate various AI applications that need to understand the surroundings via camera, such as autonomous package delivery robots, augmented reality, and intelligent drone applications. Currently, due to the high computation cost of CNN, accurate real-time object detection is only possible when mobile devices can upload video frames to powerful edge servers through high-speed wireless networks like WiFi. However, for many open-air AI applications, the network conditions (such as cellular networks) are usually unfavorable, far from satisfying the network demands of state-of-the-art systems. In this paper, we focus on the challenges incurred by mobile communication networks and propose WAVEcontaining three novel techniques, which areDeep RoI Encoding,Prioritized Parallel OffloadingandFine-grained Offloading Strategy, to realizereal-time,robustandlow-costobject detection for open-air AI applications. The experimental results show that under LTE networks, WAVE realizes high-accuracy real-time object detection and face recognition and significantly outperforms state-of-the-art systems.

Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks locally on-device. Yet the complexity of these deep models needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies squeeze the redundancy within a single model. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. MTZ supports typical network layers (fully-connected, convolutional and residual) and applies to inference tasks with different input domains. Evaluations show that MTZ can fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet for object classification and CelebA for facial attribute classification, or share$39.61\%$parameters between the two networks with$<0.5\%$increase in the test errors. The number of iterations to retrain the combined network is at least$17.8\times$lower than that of training a single VGG-16 network. Moreover, MTZ can effectively merge nine residual networks for diverse inference tasks and models for different input domains. And with the model merged by MTZ, the latency to switch between these tasks on memory-constrained devices is reduced by$8.71{\times}$.

The past decades have witnessed a surge in human fall detection with sensors, cameras, and wireless signals. Among them, Wi-Fi-based fall detection has been one of the most attractive solutions due to the ubiquitous and pervasive deployment of Wi-Fi infrastructures. However, these approaches are still difficult to be put into practical use. To push forward Wi-Fi-based fall detection for wide deployment, three major limitations concerningenvironmental diversity,motion diversity, anduser diversityare required to be resolved. In this paper, we propose FallDar, a Wi-Fi-based deep learning-assisted fall detection system that outperforms state-of-the-art works on the three criteria simultaneously. First, to deal with environmental diversity, FallDar characterizes falls with the speed of the body, which is the most relevant and inherent feature of falling activities, making the system resilient to environmental changes. Second, to deal with motion diversity, FallDar simulates a large amount of fall data of various falling types with a DNN-based generative model. Training with these data, FallDar is endowed the capability of detecting more types of falls. Third, to deal with user diversity, FallDar proposes to incorporate the fall detection network with a user identification network. The network is designed to extract user-independent features, requiring no fall data from new users for system adjustment. We implement FallDar on commercial Wi-Fi devices and conduct experiments in home and office environments for six months. The evaluation results show that FallDar achieves a false alarm rate of 5.7% and a missed alarm rate of 3.4% across all factors, making a fundamental step towards ubiquitous fall detection with Wi-Fi.

Real-time mobile video analysis like object detection and tracking is key to various household applications such as AR, cognitive assistance and smart home. Such applications rely on heavy DNN models, which are not suitable for mobile devices due to resource limitation. The long latency of cloud offloading is unacceptable for the real-time requirements, and the direct edge offloading relies on powerful edge servers, which is impractical for household scenarios. To solve this challenge, we take advantage of the computing devices that are low-cost or already exist in our lives, and propose Trine, a cloud-edge-device cooperated framework, in which complicated computation tasks are offloaded from the device to the cloud with the edge as the key bond to coordinate. In addition, due to the heterogeneity of edge devices, which leads to no one-fits-all algorithm that is optimal in all situations, we propose a profile-based algorithm to customize trackers for various edge devices. We implemented Trine on an android phone and three edge devices. The experiments demonstrate that Trine achieves 8-36% higher real-time accuracy and 25-89% higher robustness than state-of-the-art.

Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.

Sound-related respiratory symptoms are commonly observed in our daily lives. They are closely related to illnesses, infections, or allergies but ignored by the majority. Existing detection methods either depend on specific devices, which are inconvenient to wear, or are sensitive to noises and only work for indoor environment. Considering the lack of monitoring method for in-car environment, where there is high risk of spreading infectious diseases, we propose a smartphone-based system, named SymListener, to detect respiratory symptoms in driving environment. By continuously recording acoustic data through a built-in microphone, SymListener can detect the sounds of cough, sneeze, and sniffle. We design a modified ABSE-based method to remove the strong and changeable driving noises while saving energy of the smartphone. An LSTM network is adopted to classify the three types of symptoms according to the carefully designed acoustic features. We implement SymListener on different Android devices and evaluate its performance in real driving environment. The evaluation results show that SymListener can reliably detect target respiratory symptoms with an average accuracy of 92.19% and an average precision of 90.91%.

Obtaining fine-grained spatial information is of practical importance in Radio Frequency Identification (RFID)-based systems for enabling multi-object identification. However, as high-precision positioning remains impractical in commercial-off-the-shelf (COTS)-RFID systems, researchers propose to combine computer vision (CV) with RFID and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in harsh conditions like small tag intervals and low reading rates. To address the limitation, we propose TagFocus to achieve fine-grained multi-object identification with visual aids in RFID systems. The key observation is that traces generated through different methods shall be compatible if they are of one identical object. Accordingly, a Transformer-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 91% in harsh conditions, outperforming state-of-the-art schemes by 27%.

Existing device-free localization systems have achieved centimeter-level accuracy and show their potential in a wide range of applications. However, today’s radio-based solutions fail to locate the target in millimeter-level due to their limited bandwidth and sampling rate, which constrains their applications in high-accuracy demand scenarios. We find an opportunity to break the bottleneck of existing radio-based localization systems by reconstructing the accurate signal spectral peak from the discrete samples, without changing either the bandwidth or the sampling rate of the radio hardware. This study proposes milliLoc , a millimeter-level radio-based localization system. We first derive a spectral peak reconstruction algorithm to reduce the ranging error from the previous centimeter-level to millimeter-level. Then, we improve the AoA measurement accuracy by leveraging the signal amplitude information. To ensure the practicality of milliLoc , we further extend our system to handle multi-target situations. We fully implement milliLoc on a commercial mmWave radar. Experiments show that milliLoc achieves a median ranging accuracy of 5.5 mm and decreases the AoA measurement error by 31.2% compared with the baseline. Our system fulfills the accuracy requirements of most application scenarios and can be easily integrated with other existing solutions, shedding light on high-accuracy location-based applications.

Blockchain-based federated learning, also being named as swarm learning, is perceived to have great potential to support decentralized and privacy-enhancing big data processing. However, numerous serious vulnerabilities found on blockchain and federated learning enforce us to concern about the security of swarm learning. Some seemingly-unrelated combinations of known vulnerabilities may derive highly-converted and unknown threats to swarm learning. In this paper, we first investigate the security threats of the swarm learning framework. And then, leveraging backdoor attacks and eclipse attacks, a novel hybrid vulnerability that can furtively propagate backdoors among swarm learning nodes is identified. To speed up the backdoor propagation and reduce attack costs, a sample-specific eclipse (SSE) strategy that can select the swarm network node with a high data contribution rate as the attack object is also proposed. Finally, by adjusting the trigger size, the data distribution rate, and the poisoning ratio, we conduct various comparison experiments to validate the feasibility of the proposed methods. To the best of our knowledge, this is the first article to study the epidemicity of backdoors in swarm learning.

Accurate and real-time instance segmentation on mobile devices enables a wide spectrum of applications such as augmented reality, context-aware inspection and environ-mental cognition. However, the computation resource demanded by instance segmentation impedes its deployment on resource-constrained commercial mobile devices. Prior studies enable smartphones to conduct computational-intensive tasks in real-time with the assistance of an edge server. However, simply applying an edge-assisted framework hardly achieves delightful segmentation performance due to the movements of devices and targets, pixel-level precision requirements, and huge computational overhead even for edge nodes. This work proposes edgeIS, an edge-assisted system that enables real-time and accurate instance segmentation on mobile devices. edgeIS embraces the mobile device sensing ability of surroundings and its own motion, and redesigns an innovative mobile-edge collaboration paradigm suitable for segmentation tasks. We implement edgeIS on a lightweight edge node and different mobile devices. Extensive experiments are conducted under four datasets. The results show that edgeIS can run on mobile devices in real-time and achieve a 0.92 segmentation IoU, outperforming existing state-of-the-art solutions. We further embed edgeIS in an AR-based inspection system deployed in an oil field and the performance of edgeIS meets the demand of the industrial scenario.

Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only schedule the deterministic transmission of time-triggered critical traffic (TCT), neglecting the other type of traffic in industrial cyber physical systems, i.e., event-triggered critical traffic (ECT). So in this work, we propose a new paradigm for TSN scheduling named E-TSN, which can provide deterministic transmission for both TCT and ECT. The three techniques of E-TSN, i.e., probabilistic stream, prioritized slot sharing, and prudent reservation, enable the deterministic transmission of ECT in TSN, and at the same time, protect TCT from the impacts of ECT. We also develop and make public a TSN evaluation toolkit to fill the gap in TSN study between algorithm design and experimental validation. The experiments show that E-TSN can reduce the latency and jitter of ECT by at least an order of magnitude compared to state-of-the-art methods. By enabling reliable and timely delivery of ECT in TSN for the first time, E-TSN can broaden the application scope of TSN in industry.

Most existing multi-user Augmented Reality (AR) systems only support multiple co-located users to view a common set of virtual objects but lack the ability to enable each user to directly interact with other users appearing in his/her view. Such multi-user AR systems should be able to detect the human keypoints and share device poses (for identifying different users) in the meanwhile. However, due to the stringent low latency requirements and the intensive computation of the above two capabilities, previous research only enables either of the two capabilities for mobile devices even with the aid of the edge server. Integrating the above two capabilities is promising but non-trivial in terms of latency, accuracy, and matching. To fill this gap, we propose DiTing to achieve real-time ID-aware multi-device visual interaction for multi-user AR applications, which contains three key innovations: Shared On-device Tracking to merge the similar computation for optimized latency, Tightly Coupled Dual Pipeline to enhance the accuracy of each task through mutual assistance, Body Affinity Particle Filter to precisely match device poses with human bodies. We implement DiTing on four types of mobile AR devices and develop a multi-user AR game as a case study. Extensive experiments show that DiTing can provide high-quality human keypoint detection and pose estimation in real-time (30fps) for ID-aware multi-device interaction and outperform the SOTA baseline approaches.

After years of boom, drones and their applications are now entering indoors. Six-degree-of-freedom (6-DoF) pose tracking is the core of drone flight control, but existing solutions cannot be directly applied to indoor scenarios due to insufficient accuracy, low robustness to adverse texture and light conditions, and signal obstruction in indoor scenarios. To overcome the above limitations, we propose Wi-Drone, a Wi-Fi standalone 6-DoF tracking system for indoor drone flight control. Wi-Drone takes full advantage of both exte-roceptive and proprioceptive measurements of Wi-Fi to estimate the drone's absolute pose and relative motion, and fuse them in a tight-coupling manner to achieve their complementary benefits. We implement Wi-Drone and integrate it into a flight control system. The evaluation results show that Wi-Drone achieves a real-time performance with the average location accuracy of 26.1 cm and the rotation accuracy of 3.8°, which demonstrates its competency of flight control, compared to visual-inertial-based flight control. Such results also outperform existing Wi-Fi-based tracking solutions in terms of both dimensionality and accuracy.

Environment depth estimation by fusing camera and radar enables a broad spectrum of applications such as autonomous driving, environmental perception, context-aware localization and navigation. Various pioneering approaches have been proposed to achieve accurate and dense depth estimation by integrating vision and LiDAR through deep learning. However, due to the challenges of sparse sampling of in-vehicle LiDARs, high ground-truth annotation overhead, and severe dynamics in real environments, existing solutions have not yet achieved widespread deployment on commercial autonomous vehicles. In this paper, we propose LeoVR, a visual-LiDAR fusion based self-supervised approach that enables accurate environment depth estimation. LeoVR digs into the vehicle's motion information and designs two effective system frameworks based on it to (i) optimize the depth estimation results, and (ii) provide supervision signals to train a DNN. We fully implement LeoVR on a robotic testbed and commercial vehicle to conduct extensive experiments across 6 months. The results demonstrate that LeoVR achieves remarkable performance with an average depth estimation error of 0.17m, outperforming existing state-of-the-art solutions by > 43%. Besides, even cold-start in real environments by self-supervised training, LeoVR still achieves an average error of 0.21m, outperforming the related works by > 45% and comparable to those supervised training methods.

Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resourceconstrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN graph rewriters are blind for multi-DNN optimization, while GPU vendors provide inefficient APIs for parallel multi-DNN execution at runtime. A few prior graph rewriters suggest cross-model graph fusion for low-latency multi-DNN execution. Yet they request duplication of the shared weights, erasing the memory saving of weight-shared DNNs. In this paper, we propose MTS, a novel graph rewriter for efficient multitask inference with weight-shared DNNs. MTS adopts a model stitching algorithm which outputs a single computational graph for weight-shared DNNs without duplicating any shared weight. MTS also utilizes a model grouping strategy to avoid overwhelming the GPU when co-running tens of DNNs. Extensive experiments show that MTS accelerates multitask inference by up to 6.0× compared to sequentially executing multiple weightshared DNNs. MTS also yields up to 2.5× lower latency and 3.7× less memory usage compared with NETFUSE, a state-of-the-art multi-DNN graph rewriter.

Unauthorized access attack has always been a critical problem in RFID systems since any illegitimate reader can conduct access commands on tags without authorization and leave no trace. Past solutions for reader authentication require either modifications on EPC-global Gen2 protocol, which are inapplicable to existing infrastructures, or numerous extra customized devices as communication monitors, which incur high overhead. In this paper, we present a universal, low-cost and effective system to authenticate RFID readers, namely ReaderPrint, which only requires an extra passive tag array and is fully compatible with Gen2 protocol. The key insight behind ReaderPrint is that the impedance mismatch degrees (IMD) of different reader antennas across channels are distinguishable. We verify this mechanism through empirical studies using vector network analyzer and further propose two brand-new forms of hardware fingerprints, i.e., IMD-induced transmission power attenuation (ITPA) and phase shifts (IPS) across channels to quantify the IMD. Besides, to address the negative impacts of environmental changes, well-refined fingerprint matching algorithms are designed accordingly. We implement a prototype of ReaderPrint and evaluate it on 96 different readers in three indoor scenarios. Experimental results show that ReaderPrint can achieve fairly high authentication accuracy of up to 97.2%, regardless of environmental or device conditions.

RFID sensing leveraging backscatter signal features (e.g., phase shift) from tags has gained increasing popularity in numerous applications but also suffers from negative impacts of environmental multipaths. Past works to address it rely on extra customized devices, labor-intensive offline training, or frequency channel hopping, all of which are non-ubiquitous or ineffective for real-life adoption. This article presents RoSense, a universal method to alleviate multipath reflections’ impacts by spinning the reader antenna, thus enabling more robust RFID sensing. Besides, RoSense requires no RF devices or offline training and operates in a nonintrusive manner. The key insight of RoSense is to exploit two properties of line-of-sight (LOS) signal when spinning the antenna, i.e., the linearity of phase changes and stability of received signal strength to attenuate the nonlinear and nonmonotonic effect of multipath signals and refine the phase shift of LOS signal. We have implemented a prototype of RoSense with COTS devices and studied two cases for evaluation: 1) material identification and 2) object localization. Experimental results show that RoSense can improve the material identification accuracy by up to 16.22% and reduce the mean localization error by up to 39.93%, greatly outperforming the state-of-the-art solutions.

With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this article, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of 6 online car-hailing applications from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

Ubiquitous mobile sensors on human activity recognition pose the threat of leaking personal information that is implicitly contained within the time-series sensor signals and can be extracted by attackers. Existing protective methods only support specific sensitive attributes and require massive relevant sensitive ground truth for training, which is unfavourable to users. To fill this gap, we propose a novel data transformation framework for prohibiting the leakage of sensitive information from sensor data. The proposed framework transforms raw sensor data into a new format, where the sensitive information is hidden and the desired information (e.g., human activities) is retained. Training can be conducted without using any personal information as ground truth. Meanwhile, multiple attributes of sensitive information (e.g., age, gender) can be collectively hidden through a one-time transformation. The experimental results on two multimodal sensor-based human activity datasets manifest the feasibility of the presented framework in hiding users’ sensitive information (inference MAE increases$\sim$2 times and inference accuracy degrades$\sim$50%) without degrading the usability of the data for activity recognition (only$\sim$2% accuracy degradation).

Indoor localization has gained increasing attention in the era of the Internet of Things. Among various technologies, WiFi fingerprint-based localization has become a mainstream solution. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in the real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this article, we present the design and implementation of ViViPlus, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and localization of the proposed fingerprint form and integrate them all in a practical system. Extensive experiments across 7 months in different environments demonstrate that ViViPlus significantly improves the accuracy in localization scenarios by about 30% to 50% compared with the state-of-the-art approaches.

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

Mobile multiplayer augmented reality(AR) emerges in various applications including games, education, training, etc. Edge computing technique enables real-time environmental perception ability(e.g. object detection and segmentation) of devices by offloading complex computation to the nearby edge server. However, with more players involved and offloading video streams, bandwidth competition intensifies and lengthens the transmission latency, which severely impairs the accuracy of environmental perception in multiplayer AR applications. We realize staggering offloading period of each device can reduce transmission latency and maintain real-time environmental perception when more players involved. We propose Bonus containing two key techniques: collaborative offloading scheduler to eliminate bandwidth competition among multiple devices, which improve the performance of the overall system to achieve “Pareto Efficiency”; time-aware video reshaper based on mainstream 802.11 protocol to enable Bonus compatible with most wireless scenarios. we evaluate Bonus and four SOTA solutions across 24 videos on different environmental perception tasks. Results demonstrate that Bonus achieves 119.5% accuracy and 1.9x player capacity compared to the closest baseline under wireless environment.

This article introduces LinkStream, a liquidity modeling system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity models to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.

RFID tags authentication is always a critical but challenging problem because only checking the EPC is vulnerable to counterfeiting attacks. Past works explore the unique backscat-ter signal features induced by tags' manufacturing imperfection as fingerprints, but fail to support simultaneous authentication for a batch of tags in practice, which is vital for large-scale RFID applications (e.g., warehouse inventory). In this paper, we present a universal architecture, namely B-AUT, to simultaneously authenticate multiple tags even with the same EPC and pinpoint them, which is fully compatible with Gen2 standard and applicable to almost all tags' hardware fingerprints proposed in existing works. The workflow of B-AUT is threefold based on our novel algorithms. First, the extracted fuzzy fingerprint and EPC are jointly exploited to cluster raw data. Second, we extract the tags' fine-grained fingerprints for genuineness validation and obtain the invalid clusters. Third, we harness localization methods to match the invalid cluster to dubious tags and further conduct small-scale re-validation to pinpoint the counterfeit tags. We have implemented a prototype of B-AUT and evaluated it in extreme cases. Experiment results demonstrate that B-AUT can maintain nearly the same authentication accuracy as that of separate authentication and reduce the time overhead by 43.3%. Moreover, the pinpointing accuracy can reach as high as 92.8%, regardless of tags' total quantities or tag models.

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on delightful deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this paper, we propose iToLoc, a deep learning based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training based semi-supervised learning framework. Extensive experiments across 7 months with 8 different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92m and > 95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains > 90%, which significantly outperforms previous works.

Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.

Existing smartphone-based Augmented Reality (AR) systems are able to render virtual effects on static anchors. However, today's solutions lack the ability to render follow-up effects attached to moving anchors since they fail to track the 6 degrees of freedom (6-DoF) poses of them. We find an opportunity to accomplish the task by leveraging sensors capable of generating sparse point clouds on smartphones and fusing them with vision-based technologies. However, realizing this vision is non-trivial due to challenges in modeling radar error distributions and fusing heterogeneous sensor data. This study proposes FollowUpAR, a framework that integrates vision and sparse measurements to track object 6-DoF pose on smartphones. We derive a physical-level theoretical radar error distribution model based on an in-depth understanding of its hardware-level working principles and design a novel factor graph competent in fusing heterogeneous data. By doing so, FollowUpAR enables mobile devices to track anchor's pose accurately. We implement FollowUpAR on commodity smartphones and validate its performance with 800,000 frames in a total duration of 15 hours. The results show that FollowUpAR achieves a remarkable rotation tracking accuracy of 2.3° with a translation accuracy of 2.9mm, outperforming most existing tracking systems and comparable to state-of-the-art learning-based solutions. FollowUpAR can be integrated into ARCore and enable smartphones to render follow-up AR effects to moving objects.

Obtaining fine-grained spatial information is of practical importance in RFID-based applications. However, high-precision positioning remains a challenging task in commercial-off-the-shelf (COTS) RFID systems. Inspired by progress in the computer vision (CV) field, researchers propose to combine CV with RFID systems and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in more harsh conditions such as small tag intervals and low reading rates of tags. To address the limitation, we propose TagFocus, a more robust RFID-enabled system for fine-grained multi-object identification and tracking with visual aids. The key observation of TagFocus is that traces generated by different methods shall be compatible if they are acquired from one identical object. Leveraging this observation, an attention-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 89% in harsh conditions, outperforming state-of-the-art schemes by 25%.

Clone attack is considered as a severely destructive threat in Internet of Things (IoT), because: 1) the attack may be easily launched due to the deficiency of hardware architecture and the limited resources against physical capture and compromise and 2) it may trigger a large variety of insider and outsider attacks. Different from traditional clone attack detection approaches that ground on a large amount of data traversing the network (e.g., locations and identities), this article tackles this problem by answering the following fundamental questions: do we really need so much raw information? whether there is an alternative for local event detection by a node far away from that event? when acquiring/tracing global knowledge of a system/network, do we really need a global collection effort? These questions are of much importance in a large variety of networks. Specifically, this article provides a collective memory design for global topology and identity tracing (GTI Tracing), via a localized computing paradigm within neighborhood. This localized paradigm computationally builds a connection of identity and topology from time and space domain to a new computation domain. Such a computation domain retains four properties: 1) transitivity; 2) global convergence; 3) determinacy; and 4) causality. With byte-size information at an arbitrary device, it can recover and keep tracing global topology and identity information, and thus providing deterministic detection of clones. Both theoretical analysis and experimental study have shown the advantages of the proposed design in both detection accuracy and privacy protection, at a cost of light communication, storage, and computation overhead at each device.

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of CHROMACODE, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that CHROMACODE achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.

Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians; however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this article, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smartphones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68 m, outperforming the state-of-the-art systems by >75%.

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existing infrastructure. In this article, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler’s (i.e., leader) trace experience to navigate future users (i.e., followers) in a Peer-to-Peer mode. Our system leverages the advances of visual simultaneous localization and mapping ( SLAM ) on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings and two standard datasets (TUM and KITTI). Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after 2 weeks since the leaders’ traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.

In most indoor localization systems deployed on commodity WiFi infrastructure, channel state information (CSI) data is usually transmitted over multiple subcarriers of different frequencies. An observation is that there exists a certain subcarrier that can best estimate the location of the target. Based on it, we propose MobiFi to leverage deep learning to automatically select the best subcarrier. MobiFi mainly consists of two steps: First, a lightweight end-to-end Convolution Neural Network (CNN) is taken as the backbone network to extract features and do classification while avoiding serious overfitting. After selecting the best subcarrier by the first two steps, MobiFi calculates the AoA estimation and corresponding location estimation in the same way as SpotFi. Since the backbone network is lightweight, MobiFi can realize near real-time on mobile devices with guaranteed localization performance. Extensive experiments show that MobiFi is comparable to SpotFi; both methods achieve a median AoA estimation error of 8.6° and median location estimation error of 1. 5m in an indoor office scenario. At the same time, MobiFi which consumes less than 0. 21s and 1. 7s on Personal Computer (PC) and mobile devices respectively is 5 times faster than SpotFi. Particularly, because MobiFi enables real-time localization on mobile devices, it provides an economical solution for some cases where a central server is replaced by a mobile device.

Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians, however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this paper, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smart-phones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68m, outperforming the state-of-the-art systems by > 75%.

Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10, 000 gesture samples collected from 10 volunteers.

Visual peer-to-peer navigation is a suitable solution for indoor navigation for it relieves the labor of site-survey and eliminates infrastructure dependence. However, a major drawback hampers its application, as the peer-to-peer mode suffers from a deficiency of paths in large indoor scenarios with multifarious places-of-interest. Nevertheless, we propose one with a profound crowdsourcing scheme that addresses the drawback by merging the paths of different leaders' into a global map. To realize the idea, we further deal with entailed challenges, namely the unidirectional disadvantage, the scale ambiguity, and large computational overhead. We design a navigation strategy to solve the unidirectional problem and turn to VIO to tackle scale ambiguity. We devise a mobile-edge architecture to enable real-time navigation (30fps, 100ms end-to-end delay) and lighten the burden of smartphones (35% battery life for 2h35min) while assuring the accuracy of localization and map construction. Through experimental validations, we show that P2P navigation, previously relying on the abundance of independent paths, can enjoy a sufficiency of navigation paths with a crowdsourced global map. The experiments demonstrate a navigation success rate of 100% and spatial offset of less than 3.2m, better than existing works.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts from both academic and industrial community. In recent years, visual SLAM has been prevailing for robots and autonomous driving cars. However, the ever-growing computation resource demanded by SLAM impedes its application to resource-constrained mobile devices. In this paper we present the design, implementation, and evaluation of edgeSLAM, an edge assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing mechanism, and an adaptive task scheduling algorithm. We fully implement edgeSLAM on an edge server and different types of mobile devices (2 types of smartphones and a development board). Extensive experiments are conducted under 3 data sets, and the results show that edgeSLAM is able to run on mobile devices at 35fps frame rate and achieves a 5cm localization accuracy, outperforming existing solutions by more than 15%. We also demonstrate the usability of edgeSLAM through 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge, edgeSLAM is the first real-time semantic visual SLAM for mobile devices.

This paper presents DeTrack, a tracking system that can continuously trace WiFi objects at decimeter-level in real-time. To enable this, we make three main proposals. The first one is a super-resolution localization scheme that combines compressed sensing and expectation-maximization algorithms to iteratively resolve multi-path, which realizes better resolution compared against traditional MUSIC. The second one is a customized particle filter that takes advantage of WiFi signals and the geometric nature of AOA estimates to properly update location states and particle weights. Finally, an SVD-based multipacket fusion is employed to reinforce the signal space and improve tracking efficiency at the same time. A prototype is built using only commercial WiFi NICs. Extensive experiments demonstrate that DeTrack achieves an 80thpercentile localization accuracy of 0.9 meters and a median latency of around 90 milliseconds. As a result, DeTrack is looking to benefit a wide range of applications, e.g., indoor navigation, intelligent logistics, and smart cities.

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.

Pedestrian detection is a crucial component for intelligent transport system and advanced driver assistance system. In recent years, pedestrian detection methods have achieved higher accuracy. However, the existing algorithms are insufficient for small-scale pedestrian detection that is relatively far from cameras in practical applications. In this paper, we propose a novel deep small-scale sense network (termed SSN) for small-scale pedestrian detection. The proposed architecture could generate some proposal regions which are more effective to detect small-scale pedestrians. Furthermore, we design a novel loss function based on cross entropy loss to increase the loss contribution from hard-to-detect small-scale pedestrians. In addition, a novel evaluation metric is introduced, which can measure the location precision of the pedestrian detection methods. In addition an Asian pedestrian detection dataset named VIP pedestrian dataset is constructed from various road condition data. Our method achieves good detection performance on Caltech pedestrian dataset and our VIP pedestrian dataset.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data and social network check-in data, MFRs can model customer mobility patterns comprehensively at urban scale. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on two large-scale cellular network datasets covering 3.5 million and 1.8 million mobile phone users in two metropolis in China, respectively. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existed infrastructure. In this paper, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler's (i.e. leader) trace experience to navigate future users (i.e. followers) in a Peer-to-Peer (P2P) mode. Our system leverages the advances of visual SLAM on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings. Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after two weeks since the leaders' traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantity where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this paper, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on a large-scale cellular network dataset covering 3.5 million mobile phone users in a metropolis in China. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers, and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviors and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

Ubiquitous connectivity among objects is the future of the coming Internet of Things era. Technologies are competing fiercely to fulfill this goal, but none of them can fit into all application scenarios. However, efforts are still made to expand application ranges of certain technologies. Shortly after the adoption of its newest version, Bluetooth 5.0, the Bluetooth Special Interest Group released another new specification on network topology: Bluetooth Mesh. Combined together, those two bring Bluetooth to a brand new stage. However, current works related to it only focus on part of the new Bluetooth, and discussion over the entire one is lacking. Therefore, in this survey, we conduct an investigation toward the new Bluetooth from a comprehensive perspective. Through this, we show that the new Bluetooth not only consolidates its strengths in original application fields but also brings alterations and opportunities to new ones, making it a strong competitor in the future for providing complete solutions to meet the demands of seamless communications in the Internet of Things area.

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices.Many research efforts have been devoted to promote WiFi sensing by innovating applications, refining models and optimizing algorithms. A critical issue of Cross-Technology Interference (CTI), however, is surprisingly unnoticed and largely unexplored in the existing literature. In this paper, we demonstrate that CTI poses severe impacts on CSI measurements and further degrades the performance of CSI-based sensing. Based on in-depth understanding of such impacts, we present PERFIC to deal with CTI for CSI on commercial WiFi. We first exploit the inherent cyclostationarity property of different signals to detect CTI and further identify the specific distorted subcarriers on CSI. For each interfered CSI, we then propose to mitigate the impacts of CTI by amending the abnormal subcarriers. We conduct experiments on typical wireless sensing applications, including human detection and activity classification, using off-the-shelf WiFi devices. The results demonstrate that PERFIC yields a remarkable performance gain of >30% with high efficiency and outperforms existing robust classifiers.By providing interference-free CSI that is amendable to existing and emerging CSI-based sensing applications, PERFIC underpins new insights for improving the sensitivity and reliability of wireless sensing.

Multimodal features play a key role in wearable sensor based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a “collect fully and select wisely” principle as well as an interpretable parallel recurrent model with convolutional attentions to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the accuracy and interpretability of the proposed model based on extensive experiments. The results show that our model achieves competitive performance on two benchmarked datasets and works well in real life scenarios.

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliable, secure and high quality applications becomes challenging due to the scale. As one of the key cornerstones of IoT device management, automatic cross-device classification aims to identify the semantic type of a device by analyzing its network traffic. It has the potential to underpin a broad range of novel features such as enhanced security (by imposing the appropriate rules for constraining the communications of certain types of devices) or context-awareness (by the utilization and interoperability of IoT devices and their high-level semantics) of IoT applications. We propose an automatic IoT device classification method to identify new and unseen devices. The method uses the rich information carried by the traffic flows of IoT networks to characterize the attributes of various devices. We first specify a set of discriminating features from raw network traffic flows, and then propose a LSTM-CNN cascade model to automatically identify the semantic type of a device. Our experimental results using a real-world IoT dataset demonstrate that our proposed method is capable of delivering satisfactory performance. We also present interesting insights and discuss the potential extensions and applications.

Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementation of MatLoc, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and comparison of the proposed fingerprint form, and integrate them all in a practical system, which follows the classical fingerprinting framework and requires no more inputs than any previous RSS fingerprint based systems. Extensive experiments in different environments demonstrate that MatLoc significantly improves the accuracy in both localization and tracking scenarios by about 30% to 50% compared with five state-of-the-art approaches.

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of ChromaCode, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that ChromaCode achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.

This paper presents Widar2.0, the first WiFi-based system that enables passive human localization and tracking using a single link on commodity off-the-shelf devices. Previous works based on either specialized or commercial hardware all require multiple links, preventing their wide adoption in scenarios like homes where typically only one single AP is installed. The key insight underlying Widar2.0 to circumvent the use of multiple links is to leverage multi-dimensional signal parameters from one single link. To this end, we build a unified model accounting for Angle-of-Arrival, Time-of-Flight, and Doppler shifts together and devise an efficient algorithm for their joint estimation. We then design a pipeline to translate the erroneous raw parameters into precise locations, which first finds parameters corresponding to the reflections of interests, then refines range estimates, and ultimately outputs target locations. Our implementation and evaluation on commodity WiFi devices demonstrate that Widar2.0 achieves better or comparable performance to state-of-the-art localization systems, which either use specialized hardwares or require 2 to 40 Wi-Fi links.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this paper, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of DiDi Chuxing from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Mobile crowdsourcing is an emerging complex problem solving paradigm that makes use of pervasive mobile devices equipped with multi-functional sensors. Recently, vehicles have also been increasingly adopted for mobile crowdsourcing, as the vehicles, as well as drivers, can provide diverse sensing capability and predictable mobility. Existing mobile crowdsourcing algorithms mostly recruit workers to complete one kind of sensing tasks, i.e., location-based query tasks or automatic sensing tasks. In this paper, we investigate the possibility of recruiting a set of vehicles to simultaneously complete these two categories of tasks, so as to maximize the sensing utility of each participant. We first model the worker recruitment for vehicle-based crowdsourcing as a bi-objective optimization problem with respect to the sensing capability and predictable mobility of vehicles. The recruitment problem is proven to be NP-hard, and we design two heuristic algorithms based on the bi-objective greedy strategy and the multi-objective genetic algorithm to find the solutions. The experimental results with a real-world traffic trace data set show that the proposed algorithms outperform some existing algorithms in finding solutions that maximize both objectives.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend DMUSIC to 3-D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 130 with only three measurements and 50 with at most 10 measurements. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across six months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB, outperforming existing approaches. Moreover, AcMu provides 2x improvement on localization accuracy by maintaining an up-to-date radio map.

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate, pervasive and low-cost user interfaces. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions, and propose a light-weight pipeline to detect, segment, and recognize motions without training. On this basis, we present WiDance, a Wi-Fi-based user interface, which we utilize to design and prototype a contactless dance-pad exergame. Experimental results in typical indoor environment demonstrate a superior performance with an accuracy of 92%, remarkably outperforming prior approaches.

In recent years, WiFi-based sensing applications have been proliferated due to growing capacities of the physical layer. Channel State Information (CSI), which depicts the characteristics of propagation environment and reflects different human behaviors, can be easily obtained on commodity WiFi devices with slight driver modification. For the sake of higher accuracy and robustness of CSI-based sensing, a variety of research efforts have been devoted to model refinement, algorithm optimization and data sanitization. Radio frequency interference (RFI) is a crucial problem, which, however, is surprisingly overlooked and largely unexplored. The sensing performance can be significantly boosted by identifying and properly handling the interfered CSI measurements. In this paper, we demonstrate that it is feasible to identify the interfered CSI measurements due to the unique properties induced by RFI. We propose two RFI detection algorithms by utilizing cyclostationary analysis from different angles. Experimental results on off-the-shelf WiFi devices show that both algorithms are robustly stable for different scenarios and can achieve a remarkable overall accuracy of > 90%.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

Sensorless sensing using wireless signals has been rapidly conceptualized and developed recently. Among numerous applications of WiFi-based sensing, human presence detection acts as a primary and fundamental function to boost applications in practice. Many complicated approaches have been proposed to achieve high detection accuracy, which, however, frequently omit various practical constraints like real-time capability, computation efficiency, sampling rates, deployment efforts, etc. A practical detection system that works in real world lacks. In this paper, we design and implement WiSH, a real-time system for contactless human detection that is applicable for whole-day usage. WiSH employs lightweight yet effective methods and thus enables detection under practical conditions even on resource-limited devices with very low signal sampling rates. We deploy WiSH on commodity desktops and customized tiny nodes in different everyday scenarios. The experimental results demonstrate superior performance of WiSH, achieving a detection accuracy of >98% using a sampling rate of 20Hz with an average detection delay of merely 1.5s, which renders it a promising system for real-world deployment.

Cross-device interaction is becoming an increasingly hot topic as we often have multiple devices at our immediate disposal in this era of mobile computing. Various cross-device applications such as file sharing, multi-screen display, and cross-device authentication have been proposed and investigated. However, one of the most fundamental enablers remains unsolved: How to achieve ubiquitous multi-device localization? Though pioneer efforts have resorted to gesture-assisted or sensing-assisted localization, they either require extensive user participation or impose some strong assumptions on device sensing abilities. This introduces extra costs and constraints, and thus degrades their practicality. To overcome these limitations, we propose TUM, an acoustic-assisted localization scheme Towards Ubiquitous Multi-device localization. The basic idea of TUM is to utilize the dual-microphones and speakers to obtain distance cues among devices. At the same time it resolves the location ambiguity with the help of MEMS sensors. We devise techniques for distance constraint extraction, static localization, continuous localization, and multi-device localization, and build a prototype that runs on commodity devices. Extensive experiments show that TUM provides a real-time 3D relative localization service under 10cm mean error for both static and continuous localization.

Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a peer-to-peer navigation system (ppNav), on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips and alerting potential deviations, according to a previous traveller's trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. We further devise techniques to lock on a user to the nearest reference path in case he/she arrives at an uncharted place. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9 m in trace tracking and a maximum delay of nine samples (about 4.5 s) in deviation detection.

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using radio frequency identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called spatial-temporal phase profiling (STPP) to RFID-based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating the tags, for each tag, the reader obtains a sequence of RF phase values, which we call a phase profile, from the tag's responses over time. By analyzing the spatial-temporal dynamics in the phase profiles, STPP can calculate the spatial ordering among the tags. In comparison with prior absolute object localization schemes, STPP requires neither dedicated infrastructure nor special hardware. We implemented STPP and evaluated its performance in two real-world applications: locating misplaced books in a library and determining the baggage order in an airport. The experimental results show that STPP achieves about 84% ordering accuracy for misplaced books and 95% ordering accuracy for baggage handling. We further leverage the controllable reader antenna and upgrade STPP to infer the spacing between each pair of tags. The result shows that STPP could achieve promising performance on distance ranging.

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability detection.

Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted in semantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the surrounding place of interest (POI) with high accuracy and short delay. Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation. Extensive experiments in various environments show that the 80-percentile error is within 0.26m for POIs on the floor plan, which sheds light on sub-meter level indoor localization.

Industry 4.0 brings forward higher requirements on the monitoring of industrial production. Video surveillance based on Wireless Mesh Networks (WMNs) has demonstrated its effectiveness in a number of applications. Different from some typical applications of WMN that solve the "last mile" Internet access problem, WMN-based video surveillance for industrial monitoring is very likely to work in extreme circumstances or requiring high performance. Thus, the guarantee of video quality is the key for the success of industrial video surveillance. Through extensive experimental research, we find that the state of the art mapping and queuing algorithms are approaching complication and the room for improvement is decreasing. The possible solution for performance breakthrough lies in exploiting the potentials of data granularity for mapping. In this work, we propose IMesh, a video transmission solution based on WMN, which takes frame type, frame location, data packets and other factors into consideration and quantifies their impacts on video quality. The proposed approach is particularly suitable for video surveillance in industrial production under aggressive conditions. To the best of our knowledge, IMesh is the first one that differentiates, prioritizes, and schedules video data in the packet level, which is the finest granularity one can achieve while keep the MAC layer protocol unchanged. Experiment results show that the proposed solution outperforms previous works both in terms of video quality and packet delay.

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a Peer-to-Peer navigation system, named ppNav, on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips, and alerting potential deviations, according to a previous traveller's trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9m in trace tracking and a maximum delay of 9 samples (about 4.5s) in deviation detection.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend D-MUSIC to 3-D space and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with an average AoA estimation error of 13°. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

In the problem of routing in multi-hop wireless networks, to achieve high end-to-end throughput, it is crucial to find the “best” path from the source node to the destination node. Although a large number of routing protocols have been proposed to find the path with minimum total transmission count/time for delivering a single packet, such transmission count/time minimizing protocols cannot be guaranteed to achieve maximum end-to-end throughput. In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the median throughput gain is up to 60 percent, and for each source-destination pair, the throughput gain is as high as 5.3x; for anypath routing, the maximum per-flow throughput gain is 71.6 percent, while the median gain is up to 13.2 percent.

Sleep quality plays a vital role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. We present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation, and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.

Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction applications for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight sub carrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0% with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%.

The continual proliferation of mobile devices has stimulated the development of opportunistic encounter-based networking and has spurred a myriad of proximity-based mobile applications. A primary cornerstone of such applications is to discover neighboring devices effectively and efficiently. Despite extensive protocol optimization, current neighbor discovery modalities mainly rely on radio interfaces, whose energy and wake up delay required to initiate, configure and operate these protocols hamper practical applicability. Unlike conventional schemes that actively emit radio tones, we exploit ubiquitous audio events to discover neighbors passively. The rationale is that spatially adjacent neighbors tend to share similar ambient acoustic environments. We propose AIR, an effective and efficient neighbor discovery protocol via low power acoustic sensing to reduce discovery latency. Especially, AIR substantially increases the discovery probability of the first time they turn the radio on. Compared with the state-of-the-art neighbor discovery protocol, AIR significantly decreases the average discovery latency by around 70%, which is promising for supporting vast proximity-based mobile applications.

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across 6 months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB. Moreover, AcMu provides 2x improvement on localization accuracy by maintaining an up-to-date radio map.

WiFi technology has fostered numerous mobile computing applications, such as adaptive communication, finegrained localization, gesture recognition, etc., which often achieve better performance or rely on the availability of Line-Of-Sight (LOS) signal propagation. Thus the awareness of LOS and Non-Line-Of-Sight (NLOS) plays as a key enabler for them. Realtime LOS identification on commodity WiFi devices, however, is challenging due to limited bandwidth of WiFi and resulting coarse multipath resolution. In this work, we explore and exploit the phase feature of PHY layer information, harnessing both space diversity with antenna elements and frequency diversity with OFDM subcarriers. On this basis, we propose PhaseU, a real-time LOS identification scheme that works in both static and mobile scenarios on commodity WiFi infrastructure. Experimental results in various indoor scenarios demonstrate that PhaseU consistently outperforms previous approaches, achieving overall LOS and NLOS detection rates of 94.35% and 94.19% in static cases and both higher than 80% in mobile contexts. Furthermore, PhaseU achieves real-time capability with millisecond-level delay for a connected AP and 1-second delay for unconnected APs, which is far beyond existing approaches.

We introduce SmartGuide, a light-weighted and efficient approach to localize and recognize a distant unknown building. Our approach relies on shooting only a single photo of a target building via a smartphone and a local 2D Google map. SmartGuide first extracts a partial top view contour of a building from its side-view photo by applying vanishing point and the Manhattan World Assumption, and then fetches a candidate building set from a local 2D Google map based on smartphone's GPS readings. Partial top view shape, orientation and distance relative to the camera are used as input parameters in a probability model, which adversely recognizes the best candidate building in the local map. Our model is developed based on kernel density estimation that helps reduce noise in the smartphone sensors, such as GPS readings and camera ray direction reported by noisy accelerometer and compass. Experimental results demonstrate that our approach recognizes buildings ranging from 20m to 520m and achieves 92.7% accuracy in downtown areas where the Manhattan World Assumption is applicable. In addition, the processing time is no more than 6 seconds for 87% of cases. Compared with existing building localization schemes, SmartGuide offers numerous advantages. Our method avoids taking multiple photos, intricate 3D reconstruction or any initial deployment cost of database construction, making it faster and less labor-intensive than existing solutions.

Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Mobile sensing apps have proliferated rapidly over the recent years. Most of them rely on inference components heavily for detecting interesting activities or contexts. Existing work implements inference components using traditional models designed for balanced data sets, where the sizes of interesting (positive) and non-interesting (negative) data are comparable. Practically, however, the positive and negative sensing data are highly imbalanced. For example, a single daily activity such as bicycling or driving usually occupies a small portion of time, resulting in rare positive instances. Under this circumstance, the trained models based on imbalanced data tend to mislabel positive ones as negative. In this paper, we propose a new inference framework SLIM based on several machine learning techniques in order to accommodate the imbalanced nature of sensing data. Especially, guided under-sampling is employed to obtain balanced labelled subsets, followed by a similarity-based sampling that draws massive unlabelled data to enhance training. To the best of our knowledge, SLIM is the first model that considers data imbalance in mobile sensing. We prototype two sensing apps and the experimental results show that SLIM achieves higher recall (activity recognition rate) while maintaining the precision compared with five classical models. In terms of the overall recall and precision, SLIM is around 12 percent better than the compared solutions on average.

Global positioning system (GPS) has enabled a number of geographical applications over many years. Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1 to 20 m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS and human mobility captured by mobile phones. Our key observation is that smartphone-enabled dead reckoning supports accurate but local coordinates of users' trajectories, while GPS provides global but inconsistent coordinates. Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30 percent improvement on average error with respect to GPS. GloCal uses merely mobile phones and requires no infrastructure or additional reference information. As an effective and light-weight augmentation to global positioning, GloCal holds promise in real-world feasibility.

Wireless LANs, particularly WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive communication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We propose LiFi, a statistical LOS identification scheme with commodity WiFi infrastructure, and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate that LiFi achieves an overall LOS detection rate of 90.42% with a false alarm rate of 9.34% for the temporal feature and an overall LOS detection rate of 93.09% with a false alarm rate of 7.29% for the spectral feature.

Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Crossroad is among the most dangerous parts outside for the visually impaired people. Numerous studies have exploited navigating systems for the visually impaired community, providing services ranging from block detection, route planning to realtime localization. However, none of them have addressed the safety issue in crossroad and integrated three key factors necessary for a practical crossroad navigation system: detecting the crossroad, locating zebra patterns, and guiding the user within zebra crossing when passing the road. Our CrossNavi application responds to these needs, providing an integrated crossroad navigation service that incorporates all the essential functionalities mentioned above. The overall service is fulfilled by the collaboration of built-in sensors on commodity phones, and requires minimal human participation. We describe the technical aspects of its design, implementation, interface, and further improvements to make the system practical on a wider basis. Experimental results from three visually impaired volunteers show that the system exhibits promising behavior in both urban and rural areas.

Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored full potentials of CSI for human detection. Moreover, space diversity supported by nowadays popular multi-antenna systems are not investigated to the comparable extent as frequency diversity. In this paper, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness. We prototype PADS on commercial WiFi devices and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Neighbor discovery is crucial for both wireless sensor networks and mobile computing applications. The crux of the problem is to achieve energy efficiency, which has been demonstrated to be difficult by prior work. In this paper we propose Hello, a generic flexible protocol for neighbor discovery. With an unrestricted parameter, it serves as a generic framework that incorporates existing deterministic protocols. Under the framework, we expose optimal parameters for either symmetric or asymmetric duty cycles, which is the first to our knowledge. As a result, it exhibits great flexibility by adjusting itself to any configuration that best meets application demands. Besides, several techniques are applied to removing redundant discoveries without sacrificing the worst-case latency. We evaluate Hello through extensive simulation and real-world sensor experiments. The results show that Hello is highly energy-efficient under symmetric and asymmetric duty cycles. In particular, it is two times better than the state of the art in terms of the worst-case latency under asymmetric duty cycles.

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%.

Near field authentication is of great importance for a range of applications, and has attracted many research efforts in the past decades. Several approaches have been developed and demonstrated their feasibility. The state-of-art works, however, still have much room to improve their automation and usability. First, user assistance is required in most existing approaches, which will be easily observed and imitated by attackers. Second, the authentications of several works heavily depend on special hardware, e.g., Server or high resolution screen, which greatly restricts their application scenarios. In this paper, we present a near field authentication system Tooth that needs little human assistance and is compatible with most smart phones. ToAuth is based on the key insight that the acceleration traces are similar for a pair of smart phones when they are contacting physically and vibrating. The random vibration patterns are sufficiently uncertain to provide high entropy to generate a pair of cryptographic keys yet are inimitable for a third party who does not get in touch with the vibration source. ToAuth leverages the keys to make authentication for smart phones. We implement ToAuth on Android platform and evaluate its performance under various scenarios. Extensive experiments demonstrate ToAuth could achieve around 90% success rate in stable environment, and prevent attacks depended on vibration noise.

Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes.

In many logistics applications of RFID technology, luggage attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport.

In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.

Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance. By grouping nodes into increasingly large rigid components, component-based localization algorithms can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component-based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.

Context-awareness is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. Whereas human-centric contexts (e.g., indoor/ outdoor, at home/in office, driving/walking) have been extensively researched, few attempts have studied from phones' perspective (e.g., on table/sofa, in pocket/bag/hand). We refer to such immediate surroundings as micro-environment, usually several to a dozen of centimeters, around a phone. In this study, we design and implement Sherlock, a micro-environment sensing platform that automatically records sensor hints and characterizes the micro-environment of smartphones. The platform runs as a daemon process on a smartphone and provides finer-grained environment information to upper layer applications via programming interfaces. Sherlock is a unified framework covering the major cases of phone usage, placement, attitude, and interaction in practical uses with complicated user habits. As a long-term running middleware, Sherlock considers both energy consumption and user friendship. We prototype Sherlock on Android OS and systematically evaluate its performance with data collected on fifteen scenarios during three weeks. The preliminary results show that Sherlock achieves low energy cost, rapid system deployment, and competitive sensing accuracy.

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.

Off-the-shelf smartphones have boosted large scale participatory sensing applications as they are equipped with various functional sensors, possess powerful computation and communication capabilities, and proliferate at a breathtaking pace. Yet the low participation level of smartphone users due to various resource consumptions, such as time and power, remains a hurdle that prevents the enjoyment brought by sensing applications. Recently, some researchers have done pioneer works in motivating users to contribute their resources by designing incentive mechanisms, which are able to provide certain rewards for participation. However, none of these works considered smartphone users' nature of opportunistically occurring in the area of interest. Specifically, for a general smartphone sensing application, the platform would distribute tasks to each user on her arrival and has to make an immediate decision according to the user's reply. To accommodate this general setting, we design three online incentive mechanisms, named TBA, TOIM and TOIMAD, based on online reverse auction. TBA is designed to pursue platform utility maximization, while TOIM and TOIM-AD achieve the crucial property of truthfulness. All mechanisms possess the desired properties of computational efficiency, individual rationality, and profitability. Besides, they are highly competitive compared to the optimal offline solution. The extensive simulation results reveal the impact of the key parameters and show good approximation to the state-of-the-art offline mechanism.

Device-free Passive (DfP) human detection acts as a key enabler for emerging location-based services such as smart space, human-computer interaction, and asset security. A primary concern in devising scenario-tailored detecting systems is coverage of their monitoring units. While disk-like coverage facilitates topology control, simplifies deployment analysis, and is crucial for proximity-based applications, conventional monitoring units demonstrate directional coverage due to the underlying transmitter-receiver link architecture. To achieve omnidirectional coverage under such link-centric architecture, we propose the concept of omnidirectional passive human detection. The rationale is to exploit the rich multipath effect to blur the directional coverage. We harness PHY layer features to robustly capture the fine-grained multipath characteristics and virtually tune the shape of the coverage of the monitoring unit, which is previously prohibited with mere MAC layer RSSI. We design a fingerprinting scheme and a threshold-based scheme with off-the-shelf WiFi infrastructure and evaluate both schemes in typical clustered indoor scenarios. Experimental results demonstrate an average false positive of 8 percent and an average false negative of 7 percent for fingerprinting in detecting human presence in 4 directions. And both average false positive and false negative remain around 10 percent even with threshold-based methods.

Indoor localization has enabled a great number of mobile and pervasive applications, attracting attentions from researchers worldwide. Most of current solutions rely on Received Signal Strength (RSS) of wireless signals as location fingerprint, to discriminate locations of interest. Fingerprint uniqueness with respect to locations is a basic requirement in these fingerprinting-based solutions. However, due to insufficient number of signal sources, temporal variations of wireless signals, and rich multipath effects, such requirement is not always met in complex indoor environments, which we refer to as fingerprint ambiguity. In this work, we explore the potential of leveraging user motion against fingerprint ambiguity. Our basic idea is that user motion patterns collected by built-in sensors of mobile phones add to the diversity built by RSS fingerprints. On this basis, we propose MoLoc, a motion-assisted localization scheme implemented on mobile phones. MoLoc can easily be integrated in existing localization systems by simply adding a motion database that is constructed automatically by crowdsourcing. We conducted experiments in a large office hall. The experiment results show that MoLoc doubles the localization accuracy achieved by the fingerprinting method, and limits the mean localization error to less than 1m.

In many logistics applications of RFID technology, goods attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport.

Global Positioning System (GPS) has enabled a number of geographical applications over many years. Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1m to 20m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS and human mobility captured by mobile phones. Our key observation is that smart phone-enabled dead reckoning supports accurate but local coordinates of users' trajectories, while GPS provides global but inconsistent coordinates. Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30% improvement on average error with respect to GPS.

Passive human detection and localization serve as key enablers for various pervasive applications such as smart space, human-computer interaction and asset security. The primary concern in devising scenario-tailored detecting systems is the coverage of their monitoring units. In conventional radio-based schemes, the basic unit tends to demonstrate a directional coverage, even if the underlying devices are all equipped with omnidirectional antennas. Such an inconsistency stems from the link-centric architecture, creating an anisotropic wireless propagating environment. To achieve an omnidirectional coverage while retaining the link-centric architecture, we propose the concept of Omnidirectional Passive Human Detection, and investigate to harness the PHY layer features to virtually tune the shape of the unit coverage by fingerprinting approaches, which is previously prohibited with mere MAC layer RSSI. We design the scheme with ubiquitously deployed WiFi infrastructure and evaluate it in typical multipath-rich indoor scenarios. Experimental results show that our scheme achieves an average false positive of 8% and an average false negative of 7% in detecting human presence in 4 directions.

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address the scalability issue in batch authentications. Deterministic approaches verify them one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fine-grained batch authentication scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. INC also provides authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

Knowing accurate positions of nodes in wireless ad hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality, is often employed in existing approaches. Our study shows that triangle inequality has many limitations, which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles-based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results show that our design significantly improves the localization accuracy by wisely rejecting outliers.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces nontrivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600 m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

Localization is of great importance in mobile and wireless network applications. Time Difference of Arrival (TDOA) is one of the widely used localization schemes, in which the target (source) emits a signal and a number of anchors (receivers) record the arriving time of the source signal. By calculating the time difference of different receivers, the location of the target is estimated. In such a scheme, receivers must be precisely time synchronized. But time synchronization adds computational cost, and brings errors which may lower localization accuracy. Previous studies have shown that existing time synchronization approaches using low-cost devices are insufficiently accurate, or even infeasible under high requirement for accuracy. In our scheme (called Whistle), several asynchronous receivers record a target signal and a successive signal that is generated artificially. By two-signal sensing and sample counting techniques, time synchronization requirement can be removed, while high time resolution can be achieved. This design fundamentally changes TDOA in the sense of releasing the synchronization requirement and avoiding many sources of errors caused by time synchronization. We implement Whistle on commercial off-the-shelf (COTS) cell phones with acoustic signal and perform simulations with UWB signal. Especially we use Whistle to localize nodes of large-scale wireless networks, and also achieve desirable results. The extensive real-world experiments and simulations show that Whistle can be widely used with good accuracy.

To improve safety, a warning message in VANETs should be delivered both reliably and urgently. Existing solutions either tend to compromise propagation delay or do not reach high reliability due to broadcast storm problem caused by excessive retransmissions. We propose ReC, which exploits geographical information to help nodes autonomously achieve agreement on forwarding strategies. Each forwarding candidate ranks itself and its neighbors (who affirmatively or potentially received the message already) by distance to the centroid of neighbors in need of message, to assign different priority in forwarding among neighboring nodes and remarkably suppress unnecessary retransmission, while enabling best nodes to transmit the packet without waiting. The effectiveness and efficiency of this method is validated through extensive simulations under 802.11p settings. The results demonstrate that the proposed protocol achieves the high reliability of leading state-of-the-art solutions, while at the same time significantly enhances timeliness, dedicating itself to disseminating emergency messages in 2D vehicular networks.

Location-based Service (LBS) becomes increasingly important for wireless and mobile networks. In current LBS schemes, Service Providers (SPs) require users report their accurate locations, which can be illegally used by adversaries to infer sensitive information of users. Privacy disclosure raises serious concerns and limits the application of LBS. Previous solutions either rely on pre-installed centralized intermediary or assume cooperative users. Other distributed solutions, lacking of user obligation, only provide relatively poor anonymity. In this study, we propose a new solution of pseudonym change, which works for non-cooperative users and in the absence of intermediary. The basic idea behind our solution is ad-hoc anonymity. The proposed solution allows users to decide whether or not participating according to their own wills. In addition, artificially generated dummies mix up all the users who have participated in pseudonym changes at different times. Theoretical analysis demonstrates that asynchronous pseudonym change and dummy participation notably enhance privacy protection. We implement our solution on a real-world dataset of mobile phone users, which is collected at Boston, MA, and by the Reality Mining, MIT. The simulation results show that our approach significantly outperforms existing solutions.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is not always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services.

Localization is of great importance in mobile and wireless network applications. TDOA is one of the widely used localization schemes, in which a to-be-located object emits a signal and a number of receivers record the arriving time of the signal. By calculating the time difference of different receivers, the location of the object is estimated. In such a scheme, receivers must be precisely synchronized, even slight noises are completely unacceptable for centimeter-level localization. Previous studies have shown that existing time synchronization approaches for low-cost devices are insufficiently accurate and basically infeasible for high accuracy localization. In our scheme (called Whistle), several asynchronous receivers record a target signal and a successive signal that is generated artificially. By two-signal sensing and sample counting techniques, high time resolution can be achieved. This design fundamentally changes TDOA in the sense of releasing the synchronization requirement and avoiding many sources of inaccuracy. We implement Whistle on commercial off-the-shelf (COTS) cell phones. Through extensive real-world experiments in indoor and outdoor, quiet and noisy environments, the mean error is 10~20 centimeters in a 9m × 9m × 4m33D space.

Localization is an enabling technique for many sensor and ad-hoc network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically non-localizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable; however, they are considered to be coarse-grained, since they equally deal with localizable and non-localizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, Localizability-aided Localization (LAL), which basically consists of three phases: node localizability testing, component tree construction, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all adjustments made by LAL are purposefully selected. Simulation results show that LAL effectively guides the adjustment.

In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large scale sensor networks. (1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. (2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. (3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained optimal and thus better adapt to the wild environment when building a large scale sensor network.

A majority of localization approaches of wireless sensor networks rely on the measurements of inter-node distance. Errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, a straight-forward method, triangle inequality, is often employed in previous studies. However, triangle inequality has its own limitations that make itself far from accurate and reliable. In this study, we first analyze how much information are needed to identify outlier measurements. Applying rigidity theory, we propose the concept of verifiable edges and derive the conditions of an edge being verifiable. On this basis, we design a localization approach with outlier detection, which explicitly eliminates the rangings with large errors before location computation. Considering entire networks, we define verifiable graphs in which all edges are verifiable. If a wireless network meets the requirements of graph verifiability, it is not only localizable, but also outlier-resistant. Extensive simulations are conducted to examine the effectiveness of the proposed approach. The results show the remarkable improvements of location accuracy by sifting outliers.

Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance, which can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component- based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.

Accurate self-localization is a key enabling technique for many pervasive applications. Existing approaches, most of which are multilateration based, often suffer ambiguities, resulting in unbounded positioning errors. To address this problem, previous approaches discard those positioning results with possible flip ambiguities, trading the performance for robustness. However, the high false positive rate of flip prediction incorrectly rejects many reliable location estimates. By exploiting the characteristics of flip ambiguity, which causes either huge or zero error, we propose the concept of optimistic localization and design an algorithm, OFA, that employs a global consistency check and a location correction phase in the localization process. We evaluate this design through extensive simulations. The results show that OFA obtains robustness with extremely low performance cost, so as to reduce the requirement on average degree from 25 to 10 for robustly localizing a network.

Knowing accurate positions of nodes in wireless ad-hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality is often employed in existing approaches. Our study shows that triangle inequality has a lot of limitations which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results shows that our design significantly improves the localization accuracy by wisely rejecting outliers.

Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is NOT always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. The real-world experience from GreenOrbs, a sensor network system in the forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper we propose CDL, a Combined and Differentiated Localization approach. The central idea is that ranging quality is the key that determines the overall localization accuracy. In its unremitting pursuit of better ranging quality, CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented CDL and evaluated it by extensive experiments and simulations. The results demonstrate that CDL outperforms current state-of-art approaches with better accuracy, efficiency and consistent performance.

We demonstrate a synchronization-free localization system, called Whistle, for wireless sensor networks. The system is tested in several realistic environments on commercial off-the-shelf(COTS) devices and the mean error is 10~20 centimeters in a 9 x 9 x 4m3 3D space.

The proliferation of wireless and mobile devices has fostered the demand of context-aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases, it wrongly recognizes a localizable graph as nonlocalizable. In this study, we analyze the limitation of trilateration-based approaches and propose a novel approach that inherits the simplicity and efficiency of trilateration and, at the same time, improves the performance by identifying more localizable nodes. We prove the correctness and optimality of this design by showing that it is able to locally recognize all one-hop localizable nodes. To validate this approach, a prototype system with 60 wireless sensors is deployed. Intensive and large-scale simulations are further conducted to evaluate the scalability and efficiency of our design.

The proliferation of wireless and mobile devices has fostered the demand for context-aware applications, in which location is one of the most significant contexts. Multilateration, as a basic building block of localization, however, has not yet overcome the challenges of 1) poor ranging measurements; 2) dynamic and noisy environments; and 3) fluctuations in wireless communications. Hence, multilateration-based approaches often suffer from poor accuracy and can hardly be employed in practical applications. In this study, we propose Quality of Trilateration (QoT) that quantifies the geometric relationship of objects and ranging noises. Based on QoT, we design a confidence-based iterative localization scheme, in which nodes dynamically select trilaterations with the highest quality for location computation. To validate this design, a prototype network based on wireless sensor motes is deployed and the results show that QoT well represents trilateration accuracy, and the proposed scheme significantly improves localization accuracy.

K-coverage is a classic issue in wireless sensor network (WSN) deployment. Existing works typically assumes that every position in the monitoring field is covered by at least k sensor nodes at any given time. This may not always be necessary because certain events to be monitored will last only for a short period of time. Based upon such observation, we include the time dimension in the original k-coverage problem, and denote it as tk-coverage In the context of tk-coverage, sensor nodes can apply periodical sleeping strategies to save energy use. A corresponding tk-coverage (TKC) model is proposed to analyze the energy consumption and detection delay., The proposed tk-coverage can balance energy consumption and detection delay by adjusting the sleeping strategies of sensor nodes. Comprehensive simulations have been conducted to validate the effectiveness and demonstrate the efficiency of this solution.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any pre-knowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces non-trivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

The proliferation of wireless and mobile devices has fostered the demand of context aware applications, in which location is often viewed as one of the most significant contexts. Classically, trilateration is widely employed for testing network localizability; even in many cases it wrongly recognizes a localizable graph as non-localizable. In this study, we analyze the limitation of trilateration based approaches and propose a novel approach which inherits the simplicity and efficiency of trilateration, while at the same time improves the performance by identifying more localizable nodes. We prove the correctness and optimality of this design by showing that it is able to locally recognize all 1-hop localizable nodes. To validate this approach, a prototype system with 19 wireless sensors is deployed. Intensive and large-scale simulations are further conducted to evaluate the scalability and efficiency of our design.

The proliferation of wireless and mobile devices has fostered the demand of context aware applications. Location is one of the most significant contexts. Multilateration, as a basic building block of localization, however, have not yet overcome the challenges of (1) poor ranging measurement; (2) dynamic and noisy environments; (3) fluctuations in wireless communications. Hence, they often suffer poor accuracy and can hardly be employed in practical applications. In this study, we propose quality of trilateration (QoT) that quantifies the geometric relationship of objects and the ranging noise. Based on QoT, we design a confidence based iterative localization scheme, in which nodes dynamically select trilaterations with the highest quality for localization. To validate this design, a wireless sensor network prototype is deployed and results show that QoT well represents trilateration accuracy, and the proposed scheme significantly improve localization performances.

Abstract—We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces non-trivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics. Keywords—navigation; sensor networks; cyber-physical system I.

Sea depth monitoring is a critical task to ensure the safe operation of harbors. Traditional schemes largely rely on labor-intensive work and expensive hardware. This study explores the possibility of deploying networked sensors on the surface of sea, measuring and reporting sea depth of given areas. We propose a restricted floating sensors (RFS) model, in which sensor nodes are anchored to the sea bottom, floating within a restricted area. Distinguished from traditional stationary or mobile sensor networks, the RFS network consists of sensor nodes with restricted mobility. We construct the network model and elaborate the corresponding localization problem. We show that by locating such RFS sensors, the sea depth can be estimated without the help of any extra ranging devices. A prototype system with 25 Telos sensor nodes is deployed to validate this design. We also examine the efficiency and scalability of this design through large-scale simulations.