Current touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.

Pre-harvest protein and moisture content in wheat jointly determine market value and optimal harvest timing. However, existing detection methods are limited to expensive laboratory spectrometers or handheld devices requiring threshed grains, rendering practical in-field quality assessment inaccessible to smallholder farmers. Achieving low-cost, in-situ measurement requires overcoming three critical challenges: strong water absorption in fresh spikes masking weaker protein signals, morphological variations across cultivars causing spectral baseline shifts, and scattering-related distortions introduced by variable probe contact during handheld operation. To address these issues, we present WheatScout, a co-designed system integrating optimized optics with task-aware learning. The hardware features a diffuse optical path to minimize geometric bias, while the processing pipeline employs a moisture-first framework to explicitly decouple water interference prior to protein estimation. Furthermore, we integrate domain-invariant representation learning to enhance cross-cultivar generalization and a trainable scattering correction module to compensate for residual field-trial errors. During a 15-day harvest window, we systematically collected 600 wheat ear samples, covering 10 different varieties and five functional categories, thus constructing a dataset with broad spatiotemporal representativeness and varietal diversity. On this dataset, WheatScout achieved an R2 > 97% for moisture and an R2 > 92% for protein, which are comparable to lab-grade spectrometers at two orders of magnitude lower cost.

Continuous ECG monitoring is vital for early detection of arrhythmias and other cardiac abnormalities—especially during sleep, when symptoms often go unnoticed—yet existing solutions remain expensive, obtrusive, and impractical for long-term daily use. Ballistocardiography (BCG)—a passive, contactless modality that captures cardiac-induced body motion—offers a compelling alternative. However, prior efforts treat ECG reconstruction as waveform regression, leading to overfitting to individual-specific or posture-dependent artifacts. Inspired by the fact that ECG and BCG reflect parallel structures in cardiac event sequences (e.g., P/QRS/T-waves vs. I/J-waves), we design Med2ECG: a structure-aligned system that reconstructs ECG from high-fidelity BCG by explicitly aligning their latent physiological events. Med2ECG incorporates three key designs: (i) Multi-Scale Feature Extractor captures hierarchical temporal dynamics, preserving clinically relevant fine-grained features; (ii) Shared-Personalized Experts employs a Mixture-of-Experts (MoE) to adaptively disentangle signal variations due to individual and environmental factors; (iii) Medical-Informed Strategies introduces a diagnostic-driven multi-objective loss, integrating structural alignment, morphological fidelity, and landmark-aware supervision to preserve clinically critical intervals. Experiments across public and self-collected in-hospital datasets (20 healthy individuals and 10 patients with diverse cardiovascular conditions), Med2ECG achieves > 0.92 Pearson correlation, < 15% amplitude error, and precise PR/QRS/QT/RR interval estimation within 5–20 ms—demonstrating strong generalization across subjects, postures, and environments.

Flour adulteration poses significant health risks and economic losses for consumers, yet current detection methods are often impeded by high costs, limited sensitivity, and impractical laboratory requirements, leaving end users vulnerable. The difficulty is exacerbated by the need for extremely low detection limits, often at parts-per-million (ppm) levels, to identify adulterants. To address these challenges, we introduce FlourSpec, a low-cost and user-friendly system designed for on-site detection of flour adulteration at the ppm level. FlourSpec employs a spectral reconstruction algorithm to extract pertinent information from coarse-grained spectral data collected using a low-cost spectrometer. Recognizing that existing spectral reconstruction algorithms often struggle with significant errors that obscure trace adulterant information, we develop a novel end-to-end architecture that balances spectral fidelity and classification performance while minimizing the propagation of reconstruction errors. Additionally, we incorporate a hybrid attention mechanism to capture harmonic correlations within the spectra, effectively suppressing cross-band reconstruction errors. A supervised contrastive learning module is also incorporated to enhance the discriminability of trace features. Experimental evaluations demonstrate that FlourSpec achieves 97.44% accuracy in detecting multiple adulteration types and 93.12% accuracy in identifying various BPO concentrations. Notably, it is only 0.63% lower than expensive solutions and 18.74% higher than baseline systems at the same price, highlighting its effectiveness for trace-level detection.

The surge in low-altitude economic activities has spurred a significant interest in sensing Unmanned Aerial Vehicles (UAVs). With the widespread deployment of 5G infrastructure and the increasing prominence of integrated sensing and communication, monitoring UAVs via 5G base stations is a natural consideration. However, the rapid Doppler shifts of UAVs and sparse 5G reference signals violate Nyquist sampling requirements. To bridge this gap, we propose LaSen, which merges reference and downlink data signals for sensing. A key challenge stems from the fact that the combination of the periodic reference signals and stochastic data signals constitutes a non-uniform, time-varying measurement matrix. LaSen formulates the tracking of UAVs as a sparse recovery problem, where the target’s kinematics are reconstructed from non-uniform, sub-Nyquist observations. LaSen overcomes the challenges of volatile 5G signal patterns in an iterative way, starting from good measurements as an anchor and progressively refining the suboptimal measurements. Real-world experiments show that LaSen significantly extends the velocity sensing capability, where the measurable speed is up to 20.2 m/s. LaSen can detect drones at a distance of 108 m and can continuously track the distance and velocity of multiple targets, even when the downlink channel is sparsely and dynamically occupied. This work demonstrates the feasibility of high-speed target sensing in next-generation dual-function 5G/6G infrastructures.

The stochastic and asymmetric characteristics of wireless communication can degrade the accuracy of average voltage observation and the optimal dispatch of active power in microgrids, resulting in reduced power quality and higher operational costs. To mitigate these issues, this paper investigates the limitations of conventional distributed average consensus and resource allocation algorithms under asymmetric communication and identifies the factors impeding their convergence. Building on this analysis, improved distributed average consensus and resource allocation algorithms are proposed, incorporating the designed deviation recording and transmission mechanism to counteract communication asymmetry. Leveraging these improved algorithms, a distributed secondary control strategy for microgrids is proposed, ensuring robustness against stochastic asymmetric communication. Subsequently, convergence criteria of distributed control tailored to stochastic asymmetric communication scenarios are then derived, providing a foundation for the design of control parameters. Finally, hardware-in-the-loop (HIL) experiments validate the proposed strategy’s ability to achieve precise average voltage regulation and economically optimal active power dispatch, outperforming existing approaches in stochastic asymmetric communication environments.

Estimating the soluble solids content (SSC) in fruits is essential for meeting consumer expectations, ensuring the quality of processed products, and minimizing food waste. Current methods often rely on destructive sampling, expensive equipment, and complex procedures, which limit their practicality. This paper presents VNiScan-Fruit, a non-invasive, low-cost, and easy-to-use optical sensing system that utilizes visible-near-infrared (Vis-NIR) spectroscopy to estimate SSC by analyzing the interaction of light with fruit tissues to generate characteristic absorption spectra. Our approach diverges from traditional high-precision spectrometers, employing commercial LEDs and photodetectors (PD) to construct the optical sensing unit. We design and implement innovative ring-shaped rubber enclosure, interference elimination algorithms, and reconstruction strategy to extract low-dimensional reflectance spectral features from various fruits. These features are then processed using a specially designed nonlinear regression model, incorporating sucrose values obtained from a commercial refractometer to estimate SSC accurately. We tested VNiScan-Fruit on a range of fruits with diverse peel characteristics, demonstrating its ability to penetrate exocarps and effectively analyze fruits with rough surfaces and delicate tissues. The system achieved normalized mean absolute errors (NMAE) of 8%, imperceptible to consumers in sweetness difference, and remained stable under varying lighting and temperature conditions. Our findings highlight the potential of VNiScan-Fruit as a practical tool for non-destructive fruit quality assessment.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Further, we extend our system to MeatSpec-G to improve its generalizability to varied adulteration conditions and unknown adulterants. To achieve this, we first propose a feature alignment-based training scheme to reduce the feature gap among samples of diverse concentrations and admixture patterns. Then, we propose a cascaded open-set recognition framework that decouples uncertainty quantification and anomaly feature discrimination, to address the limitations of softmax confidence in detecting distribution shifts and reconstruction artifacts. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point. Moreover, our system can generalize to achieve an 88.89% detection accuracy in unknown adulteration conditions with a 27.78% improvement, and an 83.33% detection accuracy for unknown adulterants.

We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suffer from trajectory selection complexity and reduced trajectory quality due to high trajectory divergence and inconsistencies between guidance and scene information. To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories. To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point. GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information. Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates. Our experimental results, validated on the Navsim[7], demonstrate that GoalFlow achieves state-of-the-art performance, delivering robust multimodal trajectories for autonomous driving. GoalFlow achieved PDMS of 90.3, significantly surpassing other methods. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance. The code is available at https://github.com/YvanYin/GoalFlow.

In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, resulting in poor generalization across diverse real-world images. Recent advancements in stable diffusion models, trained on massive data, have shown the ability to generate high-quality images with varied characteristics. Emerging evidence indicates that these models implicitly capture the relationship between camera focal length and image content. Building on this insight, we explore how to leverage the powerful priors of diffusion models for monocular pinhole camera calibration. Specifically, we introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics and integrates seamlessly with the diffusion framework. Using this representation, we reformulate the problem of estimating camera intrinsics as the generation of a dense Camera Image conditioned on an input image. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation. We further demonstrate that our monocular calibration method enhances performance across various 3D tasks, including zero-shot metric depth estimation, 3D metrology, pose estimation and sparse-view reconstruction. Extensive experiments on multiple public datasets show that our approach significantly outperforms baselines and provides broad benefits to 3D vision tasks.

Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-based world models struggle with flexible-length, long-horizon predictions and integrating trajectory planning. This is because conventional video diffusion models rely on global joint distribution modeling of fixed-length frame sequences rather than sequentially constructing localized distributions at each timestep. In this work, we propose Epona, an autoregressive diffusion world model that enables localized spatiotemporal distribution modeling through two key innovations: 1) Decoupled spatiotemporal factorization that separates temporal dynamics modeling from fine-grained future world generation, and 2) Modular trajectory and video prediction that seamlessly integrate motion planning with visual modeling in an end-to-end framework. Our architecture enables high-resolution, long-duration generation while introducing a novel chain-of-forward training strategy to address error accumulation in autoregressive loops. Experimental results demonstrate state-of-the-art performance with 7.4\% FVD improvement and minutes longer prediction duration compared to prior works. The learned world model further serves as a real-time motion planner, outperforming strong end-to-end planners on NAVSIM benchmarks. Code will be publicly available at \href{https://github.com/Kevin-thu/Epona/}{https://github.com/Kevin-thu/Epona/}.

3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial information for robot navigation and autonomous driving systems. However, due to their large overhead from dense network structure designs, existing networks face challenges balancing accuracy and latency. In this paper, we introduce OccRWKV, an efficient semantic occupancy network inspired by Receptance Weighted Key Value (RWKV). OccRWKV separates semantics, occupancy prediction, and feature fusion into distinct branches, each incorporating Sem-RWKV and Geo-RWKV blocks. These blocks are designed to capture long-range dependencies, enabling the network to learn domain-specific representation (i.e., semantics and geometry), which enhances prediction accuracy. Leveraging the sparse nature of real-world 3D occupancy, we reduce computational overhead by projecting features into the bird's-eye view (BEV) space and propose a BEV-RWKV block for efficient feature enhancement and fusion. This enables real-time inference at 22.2 FPS without compromising performance. Experiments demonstrate that OccRWKV outperforms the state-of-the-art methods on the SemanticKITTI dataset, achieving a mIoU of 25.1 while being 20 times faster than the best baseline, Co-Occ, making it suitable for real-time deployment on robots to enhance autonomous navigation efficiency. Code and video are available on our project page: https://jmwang0117.github.io/OccRWKV/.

We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrated that imitation learning-based planners and learning-based trajectory scorers can effectively generate and select safety trajectories that closely mimic expert demonstrations. However, such trajectory planners and scorers face the challenge of generating temporally inconsistent and uncomfortable trajectories. To address these issues, ComDrive first extracts 3D spatial representations through sparse perception, which then serves as conditional inputs. These inputs are used by a Conditional Denoising Diffusion Probabilistic Model (DDPM)-based motion planner to generate temporally consistent multi-modal trajectories. A dual-stream adaptive trajectory scorer subsequently selects the most comfortable trajectory from these candidates to control the vehicle. Experiments demonstrate that ComDrive achieves state-of-the-art performance in both comfort and safety, outperforming UniAD by 17%in driving comfort and reducing collision rates by 25%compared to SparseDrive. More results are available on our project page: https://jmwang0117.github.io/ComDrive/.

Tear Film Break-Up Time (TBUT) is a critical clinical parameter in the management of dry eye disease (DED). However, traditional TBUT assessments rely on costly and time-consuming clinical procedures, while existing home-based solutions fail to provide precise TBUT values. In this work, we present Blinic, a contactless system leveraging commercial millimeter-wave (mmWave) radar to predict precise TBUT values and assess DED severity grades at home. Blinic incorporates detailed blink kinematics that are closely linked to TBUT. To address the challenge of predicting TBUT directly from radar data, we propose a teacher-student learning framework. The teacher model, trained on electronic health records (EHRs) including image-based diagnostic tests, transfers medical insights to the student model, which uses radar-captured blink dynamics. This knowledge transfer is further enhanced by a fine-tuned large language model, DryEye-LLM, which is based on clinical diagnostic reports and employs unsupervised domain adaptation to align EHRs with radar data. To ensure accurate blink motion capture, Blinic employs an antenna-coded MIMO mmWave radar design. Additionally, a query-based multitask learning module simultaneously predicts TBUT and DED severity grades, addressing potential conflicts in feature representation. Evaluated on 192 participants in collaboration with an eye clinic, Blinic demonstrates achieving a mean absolute error of 2.73 seconds for TBUT with an average accuracy of 90.54% for DED grading in real-world settings, providing a practical solution for home-based DED management.

As a crucial tool for assessing health, spirometry provides valuable insights into pulmonary functions. Recent advancements have enabled more convenient measurements by shifting spirometry solutions from cumbersome clinical devices to portable devices. However, the forced maneuvers and burdensome procedures, which necessitate repeated maximal forced breathing, often lead to dizziness and discomfort, rendering them unsuitable for vulnerable populations. In this paper, we present ESPIRO (Earphone-enabled Speech sPIROmetry) system to furnish user-friendly pulmonary function monitoring for diverse populations. Basically, ESPIRO records normal speech using microphone-embedded earphones and characterizes pulmonary function-related glottal flow during speech production. ESPIRO advances existing spirometry solutions in i) leveraging phonetics to associate pulmonary function with glottal flow in normal speech, thereby eliminating the need for forced breathing; ii) identifying effective speech features according to physiological basis, ensuring reliable spirometry measurements; and iii) effectively addressing ambient noise, making it suitable for various real-world settings. Extensive experiments with 38 subjects on 18 commodity earphones confirm that ESPIRO accurately estimates pulmonary function indices in practice.

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

Nasogastric tube (NGT) feeding supports over 10 million patients globally, particularly those with stroke-induced dysphagia, Parkinson's disease, and cognitive impairments. However, current practices face three fundamental limitations. First, caregivers often lack knowledge of the nutritional content of homemade blended meals, leading to high malnutrition rates among long-term NGT patients. Second, commercial solutions struggle to analyze blended meals in large containers due to light path distortion. Third, gastric residual volume (GRV) assessments rely on subjective nurse evaluations, resulting in incomplete records and increased feeding intolerance. To address these challenges, this demo introduces NutriBump, an innovative system designed to enhance NGT feeding through closed-loop control. Utilizing advanced spectral analysis technology, the system accurately assesses food nutrients and employs multi-modal sensors for automatic gastric fluid aspiration and analysis. By integrating long-term nutritional intake, digestion data, and health status, NutriBump leverages large language models and AI agents to generate personalized nutrition reports automatically. This closed-loop nutrition management solution improves the accuracy of blended meal analysis and reduces reliance on subjective digestion assessments.

Tremor, a prevalent symptom in various neurological disorders, significantly impacts patients' quality of life. Regular and precise tremor monitoring is essential for optimizing treatment effectiveness. Existing at-home monitoring solutions have limitations in daily life due to low adherence, privacy concerns, or inevitable interference from confounding body components and overlapped motions. In this paper, we propose mmTremor, the first privacy-preserving contactless system to achieve practical tremor detection during activities of daily living (ADL) in real-world settings. We design a mmWave-depth fusion tracking algorithm to handle the high interference from confounding body components and a multimodal spatiotemporal deep learning pipeline to effectively exploit latent tremor information. Additionally, a spatial contrastive unsupervised adaptation is proposed for better discriminability and adaptation capability to unseen domains. To fully assess mmTremor in real-world scenarios, we collect a diverse dataset of 28 patients and 9 healthy subject simulations in over 20 distinct environments, including offices, hospitals, and homes. Extensive evaluations demonstrate that mmTremor achieves a high macro-F1 of 0.877 in tremor detection, showcasing the potential as a transformative contactless solution for tremor monitoring in daily life. The dataset will be open-source to facilitate future research.

Hypertension affects 1.28 billion adults, but blood pressure alone is insufficient for accurate hypertension detection. In this paper, we identified a range of multi-dimensional hemodynamics as novel biomarkers that can offer more comprehensive cardiovascular insights for detecting hypertension. We design a novel system that collects ballistocardiogram (BCG) signals from an optic fiber sensor mat. It features a multi-task, multi-branch, unsupervised domain adaptation learning framework, enabling simultaneous prediction of five hemodynamic biomarkers for hypertension detection. In a 4-month trial with 85 subjects, Hyde achieved 97.65% average accuracy for hypertension detection.

Conventional pulmonary function tests (PFTs) are important but costly. Hence, prior research has proposed IoT sensor-based solutions to facilitate cost-efficient, at-home PFT. However, these solutions require the subject to perform maximal exhalations, a task often challenging without supervision, compromising test accuracy. In response to this challenge, this study introduces EasySpiro that, for the first time, uses non-maximal exhalations to measure PFT indicators. This is challenging since PFT indicators are only defined for maximal exhalations, and there are no guidelines to derive them from submaximal exhalations. To address that, we observe that pulmonary deficiencies affect all types of breathing, where the underlying pulmonary deficiency should be the same under different breathing efforts. Leveraging this insight, we design a reconstruction model to predict the ideal maximal breathing patterns based on submaximal ones and utilize these reconstructions for PFT. Furthermore, since the body dynamics reflect the exhalation effort, we use self-supervised learning techniques to encode body dynamics into breathing effort representations to guide the reconstruction process. We integrate these designs into earphones with microphones to measure breathing patterns and IMUs to measure body dynamics. We collaborate with a hospital and develop a dataset from 50 patients with various diseases to evaluate EasySpiro's performance, which shows an accurate prediction of PFT indicators based on non-maximal exhalations with an error rate of 7%. In addition, we open-source the collected dataset to encourage future research.

Photovoltaic (PV) defect detection is crucial for preventing power efficiency loss and fire hazards. The industry primarily relies on the fusion of infrared and visible images for defect localization and diagnosis. However, current detection methods exhibit poor generalizability in new site environments or with altered imaging setups. While recent infrared and vision foundation models (FM) facilitate domain-invariant feature maps extraction, directly concatenating them and fine-tuning achieves limited generalizability gain to PV defect detection, due to the asymmetric dual-modal semantics of defects. In this paper, we present the first large infrared-visible model DD-LIVM to enable cross-domain defect detection. The key innovation of DD-LIVM lies in its defect-specific three-step fine-tuning strategy, which utilizes alternating modality masking. Prior to feature fusion and joint fine-tuning, the infrared and visible FM encoders are alternately masked and optimized to enhance their individual semantic utility for defect localization visibility and classification granularity, with feature distances among different defect types regulated through contrastive learning. This approach allows for the extraction of generalizable and defect-specific feature maps. Moreover, for practical employment of DD-LIVM, we propose a domain-agnostic spatial alignment algorithm for infrared-visible images before dual-modal fusion, and develop source data augmentation and adaptive detection head selection schemes based on defects' infrared characteristics to further enhance the generalizability. Extensive experiments on 7,078 dual-modal images from 9 real-world scenarios across 4 cities' PV stations demonstrate that DD-LIVM achieves an accuracy of 87.7% for cross-domain defect detection, surpassing state-of-the-art methods by 17.3%.

Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous Driving. By leveraging 3DGS techniques, we construct a photorealistic digital replica of the real physical world, enabling the AD policy to extensively explore the state space and learn to handle out-of-distribution scenarios through large-scale trial and error. To enhance safety, we design specialized rewards to guide the policy in effectively responding to safety-critical events and understanding real-world causal relationships. To better align with human driving behavior, we incorporate IL into RL training as a regularization term. We introduce a closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS environments. Compared to IL-based methods, RAD achieves stronger performance in most closed-loop metrics, particularly exhibiting a 3× lower collision rate. Abundant closed-loop results are presented in the supplementary material. Code is available at https://github.com/hustvl/RAD for facilitating future research.

Backdoor attacks can exploit vulnerabilities in the training process of Deep Neural Networks (DNNs), introducing hidden malicious functionality that can be activated by a specific input pattern. Existing defenses typically rely on the assumption of a significant difference between poisoned and clean samples. However, subtle differences in dynamic and low poisoning ratio attacks can conceal poisoning features, thereby evading defenses. In this work, we propose a novel backdoor defense approach calledArtemis, which utilizes distribution shifts to eliminate the discrepancy between poisoned and benign samples in the feature space. Additionally,Artemislearns from domain-invariant features after the shift. To further enhance the purification of backdoors in DNNs, we incorporate soft knowledge distillation intoArtemisto guide the alignment of features between the source dataset domain and the generated distribution-shift dataset domain. We extensively compare our proposed method with 5 state-of-the-art (SOTA) defensive techniques under 9 SOTA attacks on 4 datasets to demonstrate its effectiveness and robustness. Our results show that our method,Artemis, can successfully purify dynamic and low poisoning ratio backdoor attacks and outperform existing defenses by a significant margin. We release the code athttps://github.com/xmyun/Artemis.

Monitoring sashimi freshness,i.e., histamine levels, in showcases poses a critical challenge for sushi restaurants and fresh food stores. Current histamine monitoring methods involve labor-intensive chemical experiments or expensive devices, making affordable on-site monitoring difficult. This paper proposes FreshSpec, a low-cost and automatic spectral imaging system capable of precisely monitoring histamine levels in sashimi with minimal human intervention. The low concentration of histamine, combined with the potential for other ingredients to mask its spectral characteristics, complicates precise histamine level predictions using coarse or redundant spectral data from low-cost devices. To address this issue, FreshSpec employs an innovative feature- wise spectral reconstruction (SR) framework that effectively eliminates irrelevant and redundant data while preserving critical histamine-related spectral features. Specifically, we redefine the SR reconstruction target by utilizing features derived from the encoder of the spectral foundation model that is enhanced to focus on histamine-related spectral features. Furthermore, inspired by the monotonic accumulation properties of histamine over time, we propose a histamine regression model with unsupervised continual adaptation to new sashimi samples during practical deployment. Experimental results from 240 samples of salmon, tuna, and snapper demonstrate that FreshSpec achieves an R2 of 0.9319 and an RMSE of 3.101 mg/100 g, comparable to laboratory spectral imaging systems, while outperforming baseline schemes with a 46.95% RMSE reduction and a 0.1631 R2 improvement.

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms.

Behavior biometrics-based user authentication with Wi-Fi gains significant attention due to its ubiquitous and contact-free manners. An individual’s identity can be verified by analyzing activities induced signal variances, excellently balancing the security demands and user experience. However, the inherent complexity of Wi-Fi signals presents significant challenges for behavior biometrics-based user authentication. The susceptibility of Wi-Fi signals results in a poor cross-environment generalization capability, which is overlooked by the existing research. In addition, most existing works of behavior-based user authentication are based on one-off activity. This makes them vulnerable to zero-effort attacks and imitation attacks. To address these issues, we propose a cross-environment continuous gesture-based user authentication framework with Wi-Fi, dubbed Wi-CGAuth. Specifically, the cross-environment generalization capability is enhanced by the cross-layer joint optimization approach. At the lowest signal layer, the signals’ time, spatial, and frequency diversity are extended maximally, by a novel, subcarrier-level, cost-effective signal optimization strategy. At the middle layer, the multi-view fusion method, i.e., multi-transfer component analysis (TCA), is applied to refine the signals from transceiver pairs after signal preprocessing. The continuous gesture segmentation problem is modeled as the classification problem, which is solved by CNN. At the upper layer, a Convolutional Neural Network-Transformer (CNN-Transformer) model is employed to achieve the dual task of effective user authentication and accurate gesture recognition. After extensive experiments in three typical indoor scenarios, Wi-CGAuth can achieve an average authentication accuracy of 92.7%, demonstrating its robustness and effectiveness.

With the development of 5G communication and smart devices, the prosperity of online content has boosted Recommender System (RS) research. Due to the data scarcity problem, researchers employ knowledge transfer techniques to improve the accuracy of RS. Data sharing or data augmentation are promising methods for such a problem, but the data suffers from privacy leakage during sharing. Thus, federated learning has been adopted to collaboratively train recommender models while preserving data privacy. Federated Recommender System (FRS) combines federated learning and RS to provide distributed recommendation services to the users. However, the existing FRS suffers from massive communication and system heterogeneity, where the necessity of model transmission and the diversity of the clients bring significant communication overhead to the system. In this article, we propose Efficient Federated Recommender System with Adaptive Model Pruning and Momentum-based Batch Adjustment ( \(eFRSA^{2}\) ) to reduce the communication overhead of FRS. \(eFRSA^{2}\) contains two modules. Adaptive Model Pruning utilizes magnitude pruning to reduce the communication volume and adaptively modifies the compression ratios of different clients to maintain the model accuracy. Momentum-based Batch Adjustment adjusts the local training batch number by a similar method of gradient descent with momentum to align the local computation time of the clients and reduce the communication overhead. The experimental results demonstrate that \(eFRSA^{2}\) can reduce up to 90% communication volume and mitigate the system heterogeneity by over 75%, demonstrating the priority of \(eFRSA^{2}\) in training efficiency. Source code can be found at https://github.com/shhjwu5/eFRSA2 .

Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this article, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval’s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.

Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20–30 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.

Respiratory diseases, highlighted by the COVID-19 pandemic, are now one of the leading causes of global mortality. Auscultation is an essential diagnostic tool for these conditions. However, its precision is dependent on the proficiency of healthcare providers, which can be a limitation in areas with scarce medical resources. Recently, the advent of sophisticated electronic stethoscopes and the application of deep learning have propelled the development of respiratory sound classification technologies to aid in clinical diagnosis. Yet, the variability of clinical auscultation environments has hindered the performance of these technologies. In this study, we propose a novel approach to enhance the model's performance through multi-task learning (MTL), simultaneously capturing the acoustic features of different respiratory sound types and their corresponding auscultation points. We've observed that normal respiratory sounds differ based on auscultation points, and the scarcity of data can confound models in discerning whether feature variations are point-related or indicative of pathology. By integrating both learning tasks, we overcome this challenge. To reinforce model robustness, we employ hyper-gradient descent (HD) to balance the task weights. Our work achieves a score of 62. 98% in the ICBHI data set, which is the arithmetic mean of Specificity and Sensitivity, surpassing the baseline by 3.43 % and demonstrating state-of-the-art performance. We believe that our findings can serve as inspiration and be integrated with other works employing data augmentation techniques to further enhance the performance and generalizability of models in clinical settings at large.

Accurate channel estimation is crucial for the performance gains of massive multiple-input multiple-output (mMIMO) technologies. However, it is bandwidth-unfriendly to estimate large channel matrix frequently to combat the time-varying wireless channel. Deep learning-based channel prediction has emerged to exploit the temporal relationships between historical and future channels to address the bandwidth-accuracy trade-off. Existing methods with convolutional or recurrent neural networks suffer from their intrinsic limitations, including restricted receptive fields and propagation errors. Therefore, we propose a Transformer-based model, CS3T-UNet tailored for mMIMO channel prediction. Specifically, we combine the cross-shaped spatial attention with a group-wise temporal attention scheme to capture the dependencies across spatial and temporal domains, respectively, and introduce the shortcut paths to well-aggregate multi-resolution representations. Thus, CS3T-UNet can globally capture the complex spatial-temporal relationship and predict multiple steps in parallel, which can meet the requirement of channel coherence time. Extensive experiments demonstrate that the prediction performance of CS3T-UNet surpasses the best baseline by at most 6.86 dB with a smaller computation cost on two channel conditions.

Authenticating RFID-tagged items during mobile inventory, such as entering or leaving the warehouse, is a critical task for anti-counterfeiting. However, past authentication solutions using commercial off-the-shelf (COTS) devices cannot be applied in mobile scenarios, such as conveyors or tunnels, due to either high latency or non-robustness to tag movement. This paper introduces LoPrint, the first system to effectively authenticate mobile tagged items using the COTS orthogonal antennas existing in most infrastructures. The key insight of LoPrint is to randomly attach multiple tags on each item as a tag group and leverage the stable layout relationships of this tag group as novel fingerprints, including the relative distance matrix (RDM) and relative orientation matrix (ROM). Additionally, a new hardware fingerprint called cross-polarization ratio (CPR) is proposed to help distinguish the tag category. Furthermore, a lightweight approach is designed to robustly extract RDM, ROM, and CPR from RSSI and phase sequences under various environmental factors. LoPrint is prototyped and deployed on a conveyor in a lab environment and a tunnel in a real-world RFID warehouse, where 726 tagged items with random layouts are used for evaluation. Experimental results show that LoPrint can achieve a high authentication accuracy of 82.92% on the fixed conveyor and 79.48% on the random warehouse trolley when the size of tag group is three, outperforming the transferred stateof-the-art solution by over 10×.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point.

Motion tracking is an important aspect of human-computer interaction (HCI) and recent research focuses on motion tracking using earphones' embedded acoustic sensors. However, these solutions can only be deployed on wired ear-phones, while most of the commercial earphones are wireless ones. This limitation arises because wireless earphones utilize the Bluetooth Low Energy (BLE) protocol for handling audio data, which blocks the usage of existing acoustic sensing solutions. Firstly, the low sampling rate of BLE prevents the system from processing high-frequency ultrasounds. However, the sensing signal for earphones must be ultrasonic to prevent disturbance to the user. Secondly, BLE employs an audio compression process that is applied with different compression rates with different bandwidths. This will break the structure of wideband signals usually used for acoustic sensing. To overcome these challenges, we present BLEAR, the first earphone-tracking system compatible with the BLE audio recording protocol. To let BLE earphones receive ultrasounds, BLEAR utilizes a specially designed bandwidth conversion scheme that uses a mask signal to trigger a non-linear effect that converts high-frequency components to low-frequency ones, thereby overcoming the low audio sampling rate restriction of BLE. Additionally, by strategically designing beacon signals to align with BLE's subband compression pattern, BLEAR mitigates the influence of audio compression and achieves accurate wireless earphone tracking. We implement a wireless earphone prototype for BLEAR and conduct extensive experiments involving 8 subjects to demonstrate its feasibility. The experimental results show that BLEAR achieves a mean distance tracking error of 3.37 cm, an angle tracking error of 5.3 degrees, and an accuracy of 97.14% in recognizing 7 common user activities. This work not only introduces a BLE-compatible earphone tracking solution but also establishes a foundation for broader BLE device tracking applications.

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

Head tracking is a technique that allows for the measurement and analysis of human focus and attention, thus enhancing the experience of human-computer interaction (HCI). Nevertheless, current solutions relying on vision and motion sensors exhibit limitations in accuracy, user-friendliness, and compatibility with the majority of commercial off-the-shelf (COTS) devices. To overcome these limitations, we present, an earphone-based system that achieves head tracking exclusively through acoustic signals. employs acoustic sensing to measure the movement of a pair of earphones, subsequently enabling precise head tracking. In particular, a pair of speakers generates a periodically fluctuating sound field, which the user’s two earphones detect. By assessing the distance and angle alterations between the earphones and speakers, we propose a model to determine the user’s head movement and orientation. Our evaluation results indicate a high degree of accuracy in both head movement tracking, with an average tracking error of 2.98 cm, and head orientation tracking, with an average error of 1.83 degrees. Furthermore, in a deployed exhibition scenario, we attained an accuracy of 89.2% in estimating the user’s focus direction.

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

In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose, an in-air gesture recognition system leveraging the screen and ambient light sensor (ALS), which are ordinary modalities on mobile devices. For the transmitter side, we design a screen display mechanism to embed spatial information and preserve the viewing experience; for the receiver side, we develop a framework to recognize gestures from low-quality ALS readings. We implement and evaluate on both a tablet and several smartphones. Results show that can recognize$9$types of frequently used in-air gestures with an average accuracy of$96.1\%$.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. can also detect fleeting radar signals. We implement and perform extensive evaluations with both controlled experiments and field tests. Results show that can sense up to$900$MHz bandwidth with frequency range from 0.5GHz to 7GHz, and the power estimation error is less than$3$dB. can also accurately detect busy 5G channels and can classify the encrypted traffic from the channel usage patterns. We believe that provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

The massive MIMO gain for wireless communication has been greatly hindered by the feedback overhead of channel state information (CSI) growing linearly with the number of antennas. Recent efforts leverage the DNN-based encoder-decoder framework to exploit correlations within the CSI matrix for better CSI compression. However, existing works have not fully exploited the unique features of CSI, resulting in an unsatisfactory performance under high compression ratios and sensitivity to multipath effects. Instead of treating CSI as common 2D matrices like images, we reveal the intrinsic stripe-based correlation across the CSI matrix. Driven by this insight, we propose CSI-StripeFormer, a stripe-aware encoder-decoder framework to exploit the unique stripe feature for better CSI compression. We design a lightweight encoder with asymmetric convolution kernels to capture various shape features. We further incorporate novel designs tailored for stripe features, including a novel hierarchical Transformer backbone in the decoder and a hybrid attention mechanism to extract and fuse correlations in angular and delay domains. Our evaluation results show that our system achieves an over 7dB channel reconstruction gain under a high compression ratio of 64 in multipath-rich scenarios, significantly superior to current state-of-the-art approaches. This gain can be further improved to 17dB given the extended embedded dimension of our backbone.

Multi-user indoor localization is considered to be one of the most useful wireless applications. Low latency and high robustness to dynamic interference from surrounding people are essential requirements for multi-user localization. However, state-of-the-art (SOTA) indoor localization systems cannot satisfy both requirements at the same time. In this paper, we propose RIScan, a Reconfigurable Intelligent Surface (RIS)-aided localization system that can achieve both low latency and high reliability. We leverage RIS to perform Wi-Fi beam scanning so all clients can figure out their direction in a single scan. However, compared with traditional AP-based systems, the introduction of RIS creates a more complicated signal superposition at the receiver, preventing clients from directly obtaining target beams for direction derivation and localization. To overcome this challenge, we fully utilize the reconfigurability of RIS to endow target beams with distinguishing features, so that RIScan can extract stable and accurate direction information from complex and dynamic environments. RIScan is implemented in the real system with our own developed 16 × 16 RIS prototype and COTS Wi-Fi devices. Extensive experiments show that RIScan achieves a median localization error of 47cm and 71cm in static and dynamic environments with only two RIS anchors. Compared to the SOTA methods, RIScan reduces the localization latency by more than an order of magnitude.

In-time disease assessment is essential to better customize the medication scheme and improve the quality of life for chronic diseases like Parkinson's disease (PD). Toward the inconvenience problem in current clinical assessment practice, mobile sensing solutions based on detecting Parkinson's vocal changes are proposed. However, current solutions either can only achieve binary disease detection task or require patients to perform specific speaking tasks, which is not effective and practical for disease stage assessment in daily scenario. Moreover, most of existing solutions do not take speech privacy into consideration. In this work, we present PDAssess, a free speech-based daily assessment system that can perform 4-stage Parkinson's disease assessment in a privacy-preserving manner. We observe that current solutions did not fully leverage the rich information embedded in free speech due to the linguistic content variations, and therefore leverage a pre-trained automatic speech recognition (ASR) model to achieve a content variation-aware feature-extraction. In order to distinguish subtle stage-wise differences, we design a novel attention-based neural network architecture with a customized loss function for disease assessment task. Towards the potential privacy leakage problem, we design a Split Learning-based framework with pseudo-labeling and local domain adversarial training to better preserve speech content privacy. We collaborate with a medical center and evaluate the performance of PDAssess on real-world speech data collected from 50 PD subjects and 50 healthy subjects. The evaluation result shows that PDAssess can perform 4-stage PD assessment with an average person-wise F1 score of 89.1% and voice sample-wise F1 score of 75.1%.

The emerging reconfigurable intelligent surface (RIS) technique introduces novel threats to wireless sensing owing to its channel customization ability. Unlike active radios, the RIS's interference behaves akin to natural reflections, exhibiting a higher level of stealthiness and difficulty in detection. However, the majority of current RIS-based attacks lack generalizability to real-world scenarios, as they assume complete coverage of the RIS over objects and develop their techniques within electromagnetic-controlled environments such as an anechoic chamber. To bridge this gap, we present RIStealth, a practical and covert attack that leverages RIS technology to render a moving individual undetectable by WiFi-based intrusion detection systems in real-life scenarios. RIStealth integrates the strengths of both motion reduction and threshold lifting strategies to address challenges of limited RIS affordability, constrained cooperation in adversary settings, and complex and unpredictable environments. Through real-world evaluations conducted with our RIS prototype, we demonstrate that RIStealth effectively reduces the victim's intrusion detection rate from 95.1% to 16.4%. Our findings shed light on the practical threats posed by RIS, thereby encouraging further countermeasure development.

Transforming physical surfaces into virtual interfaces can extend the interaction capability of many exciting metaverse applications in the future. Recent advances in vibration-based tap sensing show promise for this vision using passive vibration signals. However, current approaches based on Time-Difference-of-Arrival (TDoA) triangulation suffer the impact of fluctuant wave velocity due to the dispersive and heterogeneous nature of solid mediums, failing to meet the performance requirement for practical use. In this article, we present MM-Tap, a vibration-based tap localization system that can transform ubiquitous surfaces into virtual touch screens with low overhead. A novel localization scheme is proposed based on the finding of spatiotemporal mapping between tap locations and TDoA values, which pushes the accuracy limits of vibration-based tap sensing from unstable cm-level to mm-level. We investigate the geometry of the sensor layout and design a model-based method to synthesize tap data, which enables MM-Tap to adapt to various surface materials and respond to arbitrary sensing scales after a few seconds of calibration. We combine MM-Tap with a COTS projector and facilitate a digitally augmented surface where users can play video games with low latency.

Asthma is a common respiratory disease in modern society. However, people are rarely aware of the symptoms of asthma because the early stage of asthma is similar to that of the common cold (e.g., wheeze, cough, and shortness of breath). To tackle this challenge, we propose an asthma detection system, Ubi-Asthma, based on the smartwatch. Ubi-Asthma combines breathing signals and guttural sound (e.g., cough sound and throat-clearing sound) signals to realize passive and accurate asthma detection without interrupting the user. Not only can Ubi-Asthma extract breathing signals from the user even if the user is walking but also recognize guttural sound signals when the user is engaged in voice communication without being disturbed by noise. The features of breathing and guttural sound are combined to improve the accuracy of asthma detection. We have implemented a fully functional prototype using an off-the-shelf smartwatch. Fifty volunteers participate in an extensive experiment of up to 150 h to train Ubi-Asthma. As a result, Ubi-Asthma can reach a detection accuracy of 98.4%, which is higher than breath- or guttural-based asthma detection systems. Ubi-Asthma is expected to provide a potential solution for smart-home applications in the future.

Wireless home surveillance cameras are gaining popularity in elderly/baby care and burglary detection in an effort to make our life safer than ever before. However, even though the camera’s traffic is encrypted, attackers can still infer what the residents are doing at home. Although this security loophole has been reported, it does not attract much attention from the public, as it requires the attacker to be in close proximity to the camera and have some prior knowledge about the victims. Due to these requirements, the attacker has a low chance of success in the real world. In this article, we argue that the capability of attackers has been greatly underestimated. First, the attacker can leverage the characteristics of video transport protocols to recover the metadata of missing packets. Second, the attacker can build the inference model using the public datasets and adapt the model to the real traffic. Thus, the attacker can launch the attack at a distance from the camera, without prior knowledge about the victim. We also implement this attack scenario and verify that the attacker can infer the victims’ activities at a distance as large as 40 m without any knowledge about the victim, neither personal nor environmental.

Blood pressure (BP) is an essential vital sign related to many severe diseases, such as heart failure, kidney failure. Frequent BP detection can provide doctors more information to treat the disease. However, conventional at-home BP detection devices require completely blocking blood flow, which can lead to discomfort and disruption of normal activity when users want to perform frequent assessments. So a convenient solution should reduce the trouble of detecting BP in the daily life. In this work, we have designed and evaluated a smartphone-based BP detection application named WIB . WIB utilizes the smartphone’s acoustic sensors to obtain the chest motion caused by the heart beating and the smartphone’s camera to capture the pulse at the fingertip. We have recruited 30 volunteers who come from the author’s institution, to carry out comprehensive evaluations of WIB . We perform BP perturbation experiments to obtain different blood pressure data from these volunteers. The experiment results show that the average of Pearson correlation coefficient across all volunteers of the blood pressure estimation is 0.42–0.74 (α =0.6, σ =0.12), the average of RMSE across all volunteers is 4.2–8.8 mmHg (α =5.8, σ =1.8).

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

The traditional interaction method for smart eyewear is by touching a control panel located at the temple front of the eyeglass. This method can be unnatural since the control panel and the display are not within the same plane. In this paper, we propose a new and natural interaction technology for smart eyewear that allows users to interact with the rim of the eyeglass without adding additional hardware to the rim. This design is based on an observation that a finger touch would slightly alter the channel frequency response (CFR) of the eyeglass. We use one pair of piezoelectric (PZT) transducers to measure the CFR, and we recognize the tiny CFR changes by analyzing the complex representation of the CFR. The system detects five touch locations using a deep learning classifier. We recruit ten subjects to evaluate the system and the result shows that the system can recognize the five touch locations with an F1 score of 0.91.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, Due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

With the arrival of the Internet of Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with a single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.

The calculation of calorie consumption is of paramount importance for the human diet and health management. Most existing solutions use image-processing techniques to identify the food type and refer to the nutrition table to compute the total calorie, which is quite challenging to differentiate foods that look the same but contain vastly different quantities of calories. To address this issue, we propose to leverage near-infrared spectroscopy (NIRS) to derive the concentration of nutrients based on the unique absorption spectrum of foods. Instead of using the professional NIRS system that is bulky, expensive, and impractical for daily use by nonexpert users, we develop a low-cost portable NIRS system using commercial LEDs. As the quality of the signals of the low-power LEDs is relatively poor, we carefully design modulation schemes and interference elimination algorithms to improve the signal quality and remove the interference. Extensive experiments show that NIRSCAM outperforms the image-based baseline in achieving more accurate calorie estimation, especially for look-alike foods and is robust to various environmental factors.

Reputation/trust-based blockchain systems have attracted considerable research interests for better integrating Internet of Things with blockchain in terms of throughput, scalability, energy efficiency, and incentive aspects. However, most existing works only consider static adversaries. Hence, they are vulnerable to slowly adaptive attackers, who can target validators with high reputation value to severely degrade the system performance. Therefore, we introduce$\textsf{zkRep}$, a privacy-preserving scheme tailored for reputation-based blockchains. Our basic idea is to hide both the identity and reputation of the validators by periodically changing the identity and reputation commitments (i.e., aliases), which makes it much more difficult for slowly adaptive attackers to identify validators with high reputation value. To realize this idea, we utilize privacy-preserving Pedersen-commitment-based reputation updating and leader election schemes that operate on concealed reputations within an epoch. We also introduce a privacy-preserving identity update protocol that changes the identity and time-window-based cumulative reputation commitments during each epoch transition. We have implemented and evaluated$\textsf{zkRep}$on the Amazon Web Service. The experimental results and analysis show that$\textsf{zkRep}$achieves great privacy-preserving features against slowly adaptive attacks with little overhead.

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (also known as verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV, which motivates the participation of both provers and verifiers. More importantly, we consider practical situations, where the provers have various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties, includingtruthfulness, individual rationality, computational efficiency, budget balance,andnonnegative social welfare.The desired properties are validated through both theoretical analysis and extensive simulations.

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

Fatigue is believed to be the leading factor for traffic accidents (e.g., fatigue driving) and health problems (e.g., heart disease and diabetes). However, fatigue-related risks are difficult to quantify because there is no efficient and reliable fatigue detection method comparable to blood alcohol testing for drunk drivers. Conventional fatigue detection methods either require wiring of sensors (e.g., EEG and ECG) that are inconvenient or leverage video camera systems that are lighting sensitive and may leak privacy. We present Ubi-Fatigue, a comfortable and contactless fatigue monitor system using wireless signals. Ubi-Fatigue combines both vital signs and facial features to achieve reliable fatigue detection. A series of novel signal recovery algorithms is developed to extract the heartbeat signal and the eye blink signal from the same raw signal captured by the single-antenna radar. We have implemented a fully functional prototype of Ubi-Fatigue using off-the-shelf radar. Twenty volunteers are involved in extensive experiments for a total duration of 480 h with more than 60 h of collected time-series data. The results demonstrate that Fatigue-Radio can reach a detection accuracy of 81.4%, which is higher than ECG- or visual-based fatigue detection systems, and is approximated to the ECG + visual-based fatigue detection system. Ubi-Fatigue is expected to provide a potential solution for smart home applications in the coming days.

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this article, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low computation and communication overheads.

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

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

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we presentDetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver’s psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants,DetectDUIachieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of$2\sim 5mg/dl$. These promising results provide a highly encouraging case for continued development.

Supporting a massive amount of Internet of Things applications requires a large pool of spectrum. DSM is a promising ecosystem to improve the spectrum efficiency. In the era of LoRaWAN, the physical hardware constraints, along with the bandwidth-hungry applications pose new challenges. In this article, we investigate a novel deep-reinforcement-learning-based spectrum-sharing paradigm, termed Intelligent Overlapping, that explores partially overlapping channels for concurrent spectrum access in LoRaWAN. Our key insight is to leverage the coding redundancy to expand the available spectrum without complicated data processing algorithms. In particular, we learn the extra coding redundancy from the data on the non-overlapping spectrum via a deep-Q-learning network, and we apply such redundancy to recover the data on the overlapping spectrum. In the Media Access Control layer, we predict the channel condition and strategically learn and assign the appropriate overlapping portion to the concurrent access end devices. In the Physical layer, we harness interleaving to randomize the mutual interference to ensure that all the data remains decodable. Simulation results demonstrate that Intelligent Overlapping greatly improves the spectrum efficiency with a fast convergence rate compared to the conventional DSM mechanisms.

The emerging paradigm of Federated Learning enables mobile users to collaboratively train a model without disclosing their privacy-sensitive data. Nevertheless, data collected from different mobile users may not be independent and identically distributed. Thus directly applying the trained model to a new mobile user usually leads to performance degradation due to the so-called domain shift. Unsupervised Domain Adaptation is an effective technique to mitigate domain shift and transfer knowledge from labeled source domains to the unlabeled target domain. In this article, we design a Federated Domain Adaptation framework that extends Domain Adaptation with the constraints of Federated Learning to train a model for the target domain and preserve the data privacy of all the source and target domains. As mobile devices usually have limited computation and communication capabilities, we design a set of optimization methods that significantly enhance our framework’s computation and communication efficiency, making it more friendly to resource-constrained edge devices. Evaluation results on three datasets show that our framework has comparable performance with the standard centralized training approach, and the optimization methods can reduce the computation and communication overheads by up to two orders of magnitude.

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

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order matrix. Inspired by KFAC, we propose a novel Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation method, called THOR, to make the second-order optimization applicable in the real application models. Specifically, we gradually increase the update interval and use the matrix trace to determine which blocks of Fisher Information Matrix (FIM) need to be updated. Moreover, by resorting the power of hardware, we have designed a Hardware-driven approximation method for computing FIM to achieve better performance. To demonstrate the effectiveness of THOR, we have conducted extensive experiments. The results show that training ResNet-50 on ImageNet with THOR only takes 66.7 minutes to achieve a top-1 accuracy of 75.9 % under an 8 Ascend 910 environment with MindSpore, a new deep learning computing framework. Moreover, with more computational resources, THOR can only takes 2.7 minutes to 75.9 % with 256 Ascend 910.

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

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

In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications?

In-air gesture control extends a touch screen and enables contact-less interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications?

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

Recently, significant efforts have been made to enable WiFi-based gesture recognition. However, models trained with data collected from specific domain suffer from significant performance degradation when applied in a new domain. In practice, various WiFi sensing techniques have provided us with a full knowledge of domain information including discrete variables, i.e., environment and subject, as well as continuous variables, i.e., location and orientation. Previous works haven't fully explored these domain information or need to integrate substantial links' information to use them. Intuitively, we can boost gesture recognition accuracy by accounting for all these domain information with different properties. We propose a new framework not being restricted to link number which combines an adversarial learning scheme with feature disentanglement modules. They together conduct two-stage alignment between each of the source domains and the target domain to eliminate all gesture irrespective information. We also present an attention scheme based on discriminative information of each source and target domain to promote positive transfer from source to target domain. Our model is evaluated on the Widar 3.0 data set and achieves an improvement of 3%-12.7% in cross-domain average accuracy, demonstrating the superiority.

Recent works have shown that acoustic signals can be leveraged to perform respiration monitoring with high accuracy and low energy consumption. Since smartphones, smart speakers, and many other IoT devices are already equipped with microphones and speakers, it is convenient to implement the acoustic sensing solutions on those devices. However, the existing technologies require the speaker to transmit certain ultrasonic signals to detect respiration. Although these signals are inaudible to adults, they are audible to children and pets and they may even have negative impacts on plants. In this article, instead of using ultrasonic signals, we are trying to leverage audible signals in daily lives, e.g., music or broadcasting audios, to detect human respiration. We design a respiration detection system which derives the respiration rate by continuously estimates the channel impulse response (CIR) using music and broadcast signals. We study the intersymbol interference (ISI) brought by the randomness of music and broadcast signal and give our strategy to minimize the interference. We also propose several techniques to resolve some practical issues, such as the multipath effect and sampling frequency offset between the speaker and the microphone. Extensive experiments are conducted to demonstrate the feasibility of our system. The result shows that our system can achieve high respiration detection accuracy with the mean error of less than 0.5 BPM when different audio signals are used.

The time and monetary costs of training sophisticated deep neural networks are exorbitant, which motivates resource-limited users to outsource the training process to the cloud. Concerning that an untrustworthy cloud service provider may inject backdoors to the returned model, the user can leverage state-of-the-art defense strategies to examine the model. In this paper, we aim to develop robust backdoor attacks (named RobNet) that can evade existing defense strategies from the standpoint of malicious cloud providers. The key rationale is to diversify the triggers and strengthen the model structure so that the backdoor is hard to be detected or removed. To attain this objective, we refine the trigger generation algorithm by selecting the neuron(s) with large weights and activations and then computing the triggers via gradient descent to maximize the value of the selected neuron(s). In stark contrast to existing works that fix the trigger location, we design a multi-location patching method to make the model less sensitive to mild displacement of triggers in real attacks. Furthermore, we extend the attack space by proposing multi-trigger backdoor attacks that can misclassify inputs with different triggers into the same or different target label(s). We evaluate the performance of RobNet on MNIST, GTSRB, and CIFAR-10 datasets, against four representative defense strategies Pruning, NeuralCleanse, Strip, and ABS. The comparison with two state-of-the-art baselines BadNets and Hidden Backdoors demonstrates that RobNet achieves higher attack success rate and is more resistant to potential defenses.

Presents the introductory editorial for this issue of the publication.

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO, which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO, and extensive experiments confirm its effectiveness and robustness.

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

Choosing the best cloud configuration for large-scale data analytics jobs deployed in the cloud can substantially improve their performance and reduce costs. However, current cloud providers offer a wide variety of instance types and customized cluster sizes, making it both time-consuming and costly to pinpoint the optimal cloud configuration. This article presents the design, implementation, and evaluation of Silhouette, a cloud configuration selection framework based on performance models for various large-scale analytics jobs with minimal training overhead. The essence of Silhouette is to build performance prediction models with carefully selected small-scale experiments on small subsets of input data to estimate the performance with entire input data on larger cluster sizes. To reduce the training time and cost, Silhouette incorporates new statistical techniques to select those experiments that yield the best possible information for performance prediction. Moreover, we develop a novel model transformer to convert a prediction model built on one instance type to a different instance type with only one extra experiment, which significantly reduces the training overhead. We evaluate Silhouette with an extensive array of large-scale data analytics jobs on Amazon EC2. Our experimental results have shown convincing evidence that Silhouette is effective in optimizing cloud configuration while saving both training time and costs compared with existing solutions.

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

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

With the explosion of the Internet-of-Things (IoT) technology, numerous IoT terminal devices generate tremendous traffic. Device-to-device (D2D)-enabled caching can greatly relieve the pressure of massive resource-limited terminal devices in the IoT network. This article proposes a novel distributed framework, termed crowdcaching, which motivates selective file caching and cooperative file sharing among terminal devices via short-range (e.g., D2D) communications. After modeling file preference distributions and local connectivities, we optimize the caching strategy for any given coalition of cooperative users to minimize their total delay cost. In particular, if users have homogeneous file preferences and local connectivities, we can mathematically define a popularity index, according to which files are chosen to be cached in user devices. In a more general setting where users have heterogeneous file preferences and local connectivities, we propose a greedy algorithm of low complexity to determine the optimal caching strategy. Based on the cooperative caching strategy for any given coalition, we further investigate users' incentive to form crowdcaching coalitions through the coalitional game theory and propose a distributed algorithm to yield a stable coalition formulation. The simulation results show that crowdcaching can effectively reduce the average delay cost of users by as much as 45.64%.

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

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

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

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

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

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upper-layer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challenge-response protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.

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

Visible light positioning (VLP) is a promising direction for indoor localization. VLP depends on Visible light communication (VLC) to receive location anchors sent by light bulbs. In order to decode high-frequency VLC signals, today's VLP systems require the receiver to equip either rolling shutter cameras or high-frequency light sensors, which bring considerable overhead or are even unavailable on many mobile devices. This paper introduces ALS-P, a lightweight VLP approach which only requires the commercially widely available ambient light sensor (ALS). ALS is conventionally not treated as a feasible VLC receiver as its sampling rate is far less than that of VLC signals. Our basic idea is to leverage the property of frequency aliasing. Through dynamically adjusting the sampling rate of the ALS sensor, the down-converted signals can be uniquely distinguished. To realize this idea, we propose novel designs to address challenges which stem from ALS hardware, high-order light aliasing, and environmental interference cancellation. Besides, ALS can also enable a lightweight VLC via changing LED frequencies. We implement the ALS-P on commercial LED bulbs and the ALS of existing smartphones. The evaluation shows that the ALSP can enable robust and efficient LED frequency decoding with at least 4LEDs bulbs in practical scenarios. As a result, ALS-P can enable VLC at a data rate of 5bit/s per each LED bulb and achieve a sub-meter level indoor localization accuracy.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden for receiving with cameras. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for devices with a camera. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we developed 1) a novel color-based modulation scheme to handle user's mobility and 2) a sensor-assisted positioning algorithm to ease user's burden in capturing multiple location anchors. Our experiments based on the prototype show that PIXEL can provide 10 cm-level real-time VLP for wearables and smartphones with camera resolution as coarse as 60 pixel × 80 pixel and CPU frequency as low as 300 MHz.

In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. With mobile social networking, a user enjoys an intrinsic benefit when she aligns her behavior with the behavior of others. Referred to as network effects , such an intrinsic benefit becomes more significant as more users join and contribute to the crowdsourcing system. But should a crowdsourcer design her extrinsic rewards differently when such network effects are taken into consideration? In this article, we incorporate network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. We show that the number of participating users and their contributions to the crowdsourcing system evolve to a steady equilibrium, thanks to subtle interactions between intrinsic rewards due to network effects and extrinsic rewards offered by the crowdsourcer. Taken network effects into consideration, we design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Through simulations and examples, we demonstrate that with our new strategies, a crowdsourcer is able to attract more participants with higher contributed efforts; and the participants gain higher utilities from both intrinsic and extrinsic rewards.

Over the past decades, there have been extensive research endeavors in spectrum auction design. However, most solutions only focus on the allocation efficiency while ignoring the privacy leakage inherent in the process of spectrum auction. So far, the very few existing works on secure spectrum auctions either provide inadequate privacy protection or incur performance loss in terms of spectrum reusability. In this paper, for the first time, we propose PS-TAHES, a privacy-preserving and truthful double auction mechanism for heterogeneous spectrum. PS-TAHES is constructed based on our carefully designed security primitives, which can support various arithmetics over encrypted data, including multiplication, bid comparison, and sorting matrix, and they are well applicable in other contexts. We theoretically analyze the security and efficiency of PS-TAHES, which is proved to ensure a full and strong privacy protection for bidders while preserving the allocation efficiency of the original auction mechanism. Experimental results, consistent with the theoretical analysis, further validate the practical use of PS-TAHES in real-world applications.

Efficient spectrum sensing is essential for the successful application of the Dynamic Spectrum Assignment (DSA) technology in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing so that the spectrum opportunities are thoroughly exploited while the primary users are not harmed. In this article, we propose a new sensing service model in which a Spectrum Sensor Network (SSN) is employed for spectrum sensing tasks. We will describe the general framework for this SSN-enabled CRN and present the major challenges in such an architecture. We will address one of these challenges and formulate it as a boundary detection problem with unknown erroneous inputs. A novel cooperative boundary detection algorithm is designed which explores recent advances in Support Vector Machines (SVM) and computational geometry. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. Real testbed as well as comprehensive simulation experiments are conducted, and the results show that, compared with the traditional schemes, cooperative boundary detection can dramatically reduce the spectrum sensing overhead and improve the effectiveness of DSA.

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

This paper presents DriveSafe Inspector, a fine-grained driver hand position monitoring system, which continuously detects a driver's hand position on the steering wheel. The steering wheel is divided into twelve 30° sectors like a clock. Our system can be applied on off-the-shelf hardware and works without extra modification to vehicles. In our system, sensor readings from both a wearable and its paired smartphone are fused to infer hand posture and turning angle between static holding states. With both static holding and dynamic turning information, our system achieves fine-grained hand position prediction in the presence of diverse road conditions and inter-individual differences. The on-road evaluation shows that our system can achieve an average 91.59% hand position detection accuracy with only static information, and can be further improved to 94.63% accuracy combined with dynamic turning information.

Mobile sensing enables unobtrusive monitoring of our daily activities, sleep quality, breathing and heart rate, revolutionizing the health-care system. Dietary information is also a critical dimension for health management but has no convenient solution yet. In this paper, we ask whether we can track meal composition unobtrusively. We introduce Smart-U, a new utensil design that can recognize meal composition during the intake process, without user intervention or on-body instruments. Smart-U makes use of the fact that light spectra reflected by foods are dependent on the food ingredients. By analyzing the reflected light spectra, Smart-U can recognize what food is on top of the utensil. We describe the prototype design of Smart-U and the food recognition algorithm. We demonstrate that Smart-U can recognize 20 types of foods with 93 % accuracy. It can work robustly under different conditions. We envision that Smart-U can enable automatic food intake tracking, and provide personalized food suggestions based on nutrient recommendations and prior consumption. In the long run, Smart-U can contribute to the study of chronic diseases.

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

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

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

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

In this paper, a non-orthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast scheme for cellular networks is proposed. This scheme combines the successive video-layer decoding in SVC with the successive interference cancellation (SIC) in NOMA, which enables a further reduction of the bottleneck effect imposed by cell-edge user equipments (UEs). Aiming at maximizing the overall video quality experienced by UEs in multiple multicast groups, the resource allocation for multiple groups and the scalable multicast scheduling within each group are formulated as a joint mixed-integer nonlinear programming problem. The formulated optimization problem is decoupled into a multi-group resource allocation (MRA) problem and multiple independent intra-group scalable multicast scheduling (IGSMS) subproblems. To solve IGSMS subproblems, we propose an optimal recursive algorithm, for which the optimal transmit power for each layer of the superposition coding needed in each iteration is derived in a closed form. The MRA problem is optimally solved via a knapsack approach. Extensive numerical results demonstrate the improved performance of the proposed NOMA-enhanced SVC multicast scheme over several baseline schemes.

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

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

Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision, and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves suboptimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.

Nowadays, wrist wearables are widely used in applications containing privacy sensitive data. Protecting these data from unauthorized access is highly demanded. However, Personal Identification Number(PIN)-based certificate is not convenient to use for wearables are small in size and have no/very limited input ways. Like signing on paper, signing a pattern in the air and authenticate the user according to the trait from motion sensors is a potential solution for the authentication needs on wearables. However, we observe the successful authentication rate of such approaches in existing designs is not high enough under different postures. We identify the problem is caused by the residue gravity acceleration in motion sensors which introduces inaccuracy in capturing signing dynamic. To this end, this paper proposes a robust gesture-based authentication method that can be used under different postures. Our key idea to use and only use the gyroscope sensor to capture the signing dynamics, so that to get rid of the impact from gravity. To realize this idea, we propose an accurate wrist dynamic capturing method with gyroscope sensor, through which behavior traits are extracted from both time and shape domain. Finally, by comparing the Dynamic Time Warping distance between the real time input and the traits template, signing user can be identified. We implement our method on android smart watch. Our evaluation involves 11 users and lasts for more than one month. The results show that our method can achieve 90.1% balanced accuracy and have stable performance under different postures.

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

Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

Today's mobile devices contain sensitive data, which raises concerns about data security. This paper discusses a covert channel threat on existing mobile systems. Through it, malware can wirelessly leak information without making network connections or emitting signals, such as sound, EMR, vibration, etc., that we can feel or are aware of. The covert channel is built on a communication method that we call NICScatter. NICScatter transmitter malware forces mobile devices, such as mobile phones, tablets or laptops, to reflect surrounding RF signals to covertly convey information. The operation is achieved by controlling the impedance of a device's wireless network interface card (NIC). Importantly, the operation requires no special privileges on current mobile OSs, which allows the malware to stealthily pass sensitive data to an attacker's nearby mobile device, which can then decode the signal and thus effectively gather the guarded data. Our experiments with different mobile devices show that the covert channel can achieve 1.6 bps and transmit as far as 2 meters. In a through-the-wall scenario, it can transmit up to 70 cm.

This paper presents Shadow, a novel display-to-device communication system working in radio frequency. It leverages the Electromagnetic Radiation (EMR) emanated from display to transmit information. We modulate the signal transmitted to display to make the corresponding EMR signal fall into the FM band. In this way. when users' devices with FM ability are approaching to the display, it can automatically receive information from the display. Since Shadow does not rely on camera, wearable devices can also obtain information from display. In addition to the lightweight communication ability, Shadow keeps the communication truly invisible by only transmitting in the period that will not be shown on the display panel. Our system requires no modification on hardware, and we implement it with commodity display system and mobile devices. Results show it can achieve 1.5 kbps at distances of up to 20cm from the display panel.

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

Wireless mesh networks (WMNs) connect to the Internet via access gateways. This paper studies multi-source video multicast in Internet-connected WMNs. The focus is on the design of a shareable integrated multicast that allows the multicasts of video sources to employ common Internet shortcuts or WMN paths to avoid potentially high WMN overheads and excessive Internet usage. Several algorithms are described that together form a video multicast framework running a controlled number of shareable multicasts under the constraint of Internet availability. These algorithms are the resource-efficient source group algorithm, the efficient integrated architecture algorithm, and the interference-controlled multicasting tree algorithm. These algorithms represent different approaches to overcoming various costs arising from multi-source video multicast, enabling multiple video sources to distribute delay, and throughput-guaranteed videos to receivers across large-scale areas. Simulation results are presented that quantify the performance gains that can be achieved.

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

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

Recent advances in rateless codes facilitate the utilization of dense constellation. By exploiting more constellation points with controlled symbol distance, they ensure higher transmission date rate and better diversity gain against fading. In this paper, we observe that there exists certain redundancy in the existing dense constellation diagram. With proper design, such redundancy can be can be optimized, and exploited to enable a new orthogonal dimension for multiple access. We termed it as hash division multiple access (HDMA). HDMA incorporates a orthogonal, linear and random encoder in PHY layer to construct orthogonal hash space for user separation. An AP-driven MAC protocol is then proposed to fully utilize this transmission concurrency for uplink and downlink multiple access. We verify the feasibility of HDMA via a GNU radio testbed, and further conduct trace-driven simulations to evaluate the multiplexing gain of HDMA. The results reveal that HDMA provides a maximum number of 3 to 5 concurrent transmissions, with performance gain over 131% and 39% compared to 802.11 and AutoMAC.

Performance anomaly is a well-known problem in 802.11 networks. It refers to the case where the aggregated throughput of the multirate network is degraded by slow rate links due to the fair sharing of transmission opportunities. As a result of growing heterogeneity in 802.11 hardware, the coexistence of slow rate and fast rate links are more common and performance anomaly is considered severer. In this paper, we propose to leverage the emerging full duplex technology to relief the penalty of performance anomaly for 802.11 network. Although in general cases, full duplex gain is limited by the uplink-downlink interference (UDI), we find in performance anomaly, it is possible to eliminate UDI and achieve full duplex communication in slow rate links through successive interference cancellation (SIC). To realize such idea, we design a novel MAC protocol for full duplex AP and half duplex clients. When performance anomaly occurs, the protocol leverages a distributed pairing algorithm to select slow rate links for full duplex communication to increase the network throughput. We implement the full MAC design in NS3 network simulator, results show it has an average throughput gain of 30% over legacy protocol.

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

Genome-wide expression data consists of millions of measurements towards large number of genes, and thus it is challenging for human beings to directly analyze such large-scale data. Clustering provides a more convenient way to analyze gene expression data because it can subdivide raw data into comprehensive classes. However, the number of probed genes is rather greater than the number of samples, and this makes conventional clustering methods perform unsatisfactorily. In this paper, we propose a Gauss-Seidel based non-negative matrix factorization (GSNMF) method to overcome such imbalance deficiency between features and samples. In particular, GSNMF it-eratively projects gene expression data onto the learned subspace followed by adaptively updating the cluster centroids based on the projected data. Since this data projection strategy significantly reduces the influence of imbalance between the number of samples and the number of genes, GSNMF performs better than traditional clustering methods in gene expression clustering. Since GSNMF updates each factor matrix by solution of a linear system obtained by the Gauss-Seidel method, it converges rapidly without neither complex line search nor matrix inverse operators. Experimental results on several cancer expression datasets confirm both efficiency and effectiveness of GSNMF comparing with the representative NMF methods and conventional clustering methods.

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

In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. With mobile social networking, a user enjoys an intrinsic benefit when she aligns her behavior with the behavior of others. Referred to as network effects, such an intrinsic benefit becomes more significant as the number of users grows in the crowdsourcing system. But should a crowdsourcer design her extrinsic rewards differently when such network effects are taken into account? In this paper, we, for the first time, consider network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. Rather than assuming a fixed participant population, we show that the number of participating users evolves to a steady equilibrium, thanks to subtle interactions between intrinsic rewards due to network effects and extrinsic rewards offered by the crowdsourcer. Taken network effects into consideration, we design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Via extensive simulations, we demonstrate that with our new strategies, a crowdsourcer is able to attract more participants with higher contributed efforts; and participants gain higher utilities from both intrinsic and extrinsic rewards.

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

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

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

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

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

We consider the crucial problem of maximizing the system-wide performance which takes into account request processing throughput, smartphone user experience and system stability in a participatory sensing system with cooperative smartphones. Three important controls need to be made, i.e., 1) request admission control, 2) task allocation, and 3) task scheduling on smartphones. It is highly challenging to achieve the optimal system-wide performance, given arbitrary unknown arrivals of sensing requests, intrinsic tradeoff between request processing throughput and smartphone user experience degradation, and heterogenous requests. Little existing work has studied this crucial problem of maximizing the system-wide performance of a participatory sensing system as a whole. In response to the challenges, we propose an optimal online control approach to maximize the system-wide performance of a participatory sensing system. Exploiting the stochastic Lyapunov optimization techniques, it derives the optimal online control strategies for request admission control, task allocation and task scheduling on smartphones. The most salient feature of our approach is that the achieved system-wide performance is arbitrarily close to the optimum, despite unpredictable and arbitrary request arrivals. Rigorous theoretical analysis and comprehensive simulation evaluation jointly demonstrate the efficacy of our online control approach.

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

Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or under-select modulation schemes which limits throughput performance. To fill-in the gap, we propose time-line modulation (TiM), a novel three-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200 percent.

This paper studies the feasibility of using mobile phone to achieve low-cost and accurate three-dimensional (3D) road modeling, which provides nutritious supplement to current digital map. Instead of using expensive tools, our method only needs a mobile phone fixed on a moving vehicle. Road surface variation, reflected by vehicle dynamic state, is captured by sensors in the mobile phone.The road in 3D is reproduced by combining sensing data from accelerometer, magnetometer, gyroscope, and GPS trace. Several compensation and calibration techniques are developed to improve modeling accuracy based on the systematic study of sensor error properties. A learning step is used to automatically unify the user dependent factors to avoid manual adjustment. We evaluate our method on a 2 km real road. Compared with the field measured baseline, it successfully generates a fine-grained 3D road model with maximum height error less than 4 m and 90 degree slope angle error less than 2 degrees.

The papers in this special issue focus on cloud computing-based video processing and content sharing. With the rapid growth of IPTV and mobile video applications and driven by urgent demands from industry and users, video processing and content sharing technologies have received significant research attention in recent years. Cloud-based video processing and content sharing networks are promising technologies to orchestrate large-scale and efficient video distribution between mobile clients and multimedia cloud systems. The objective of this special issue is to identify and promote advancements in media cloud-based video processing and content sharing technologies to advance current and enable future anywhere and anytime video processing and streaming applications.

Auctions are among the best-known market-based tools to solve the problem of dynamic spectrum redistribution. In recent years, a good number of strategy-proof auction mechanisms have been proposed to improve spectrum utilization and to prevent market manipulation. However, the issue of privacy preservation in spectrum auctions remains open. On the one hand, truthful bidding reveals bidders' private valuations of the spectrum. On the other hand, coverage/interference areas of the bidders may be revealed to determine conflicts. In this paper, we present PISA, which is a PrIvacy preserving and Strategy-proof Auction mechanism for spectrum allocation. PISA provides protection for both bid privacy and coverage/interference area privacy leveraging a privacy-preserving integer comparison protocol, which is well applicable in other contexts. We not only theoretically prove the privacy-preserving properties of PISA, but also extensively evaluate its performance. Evaluation results show that PISA achieves good spectrum allocation efficiency with light computation and communication overheads.

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

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

Rate adaptation is an essential component in today's wireless standards, which help approach the channel capacity and maximize the throughput. However, how to estimate the optimal data rate in a fluctuated channel remains of great concern. Previous wisdoms leverage PHY layer information for rate estimation, including confidence information like SoftPHY hints, and channel state information (CSI) measurements. However, when experiencing rapid time varying and frequency selective fading channel, the above metrics can be inaccurate. The reason roots from the fact that there are not enough cost-efficient pilots, which are pre-known symbols inserted in a packet for channel estimation. In this paper, we observe that by digging into both PHY layer decoder and upper layer protocol headers, more reliable data bits with high confidence level can be exploited. These data bits, termed smart pilot, can be used to calibrate the channel estimation measurements cost-efficiently. Based on the calibrated estimation, we further propose a novel greedy rate selection algorithm to track the optimal data rate, which successfully avoids the impact of deep fading subcarriers in both legacy 802.11a/g and 802.11n MIMO systems. Our experiments on GNU radio testbed show that SmartPilot quickly tracks the link variance, and improve the channel estimation accuracy by 87%. Furthermore, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

Dynamic video streaming protocols allow video clients to adaptively choose video rates to improve viewer Quality-of- Experience (QoE). Existing works on rate adaptation choose the video rate according to network capacity or buffer state, however, they didn't consider the early quitting problem. Most viewers quit before the video ends, in which case optimal solutions for complete video sessions may become suboptimal. In this paper, we design a video rate adaptation scheme which considers incomplete video sessions and aims at maximizing the quality of the video chunks that are actually watched by viewers. We formulate the video rate adaptation problem as a Markov Decision Process (MDP), in which the viewers' decision of quitting depends on the video quality and the rebuffer time. To solve the MDP problem, we propose two online learning algorithms for regular viewers and new viewers with no viewing history, respectively. The simulation results show that the proposed scheme increases the video quality {truly} consumed by the viewers, thus improving the viewer QoE.

Much of today's online social network (OSN) system relies on advertising for financial support. To improve the effectiveness of advertising, online advertisers tend to leverage influential users to deliver ads. Most of existing efforts on online advertising have focused on single-shot scenarios or assume static OSN models, while they overlook the fact that actions of advertising affect users' behaviors. In this paper, we investigate the behaviors of Sina Weibo users over three months, and make the observation that advertising affects the behaviors of the user's followers, which in turn has an impact on the effectiveness of future advertising. Based on this observation, we propose TiSA, a time-dependent advertising framework, which considers the future impact of advertising. Under this framework, the advertiser and the user make their decisions based on their instant utilities as well as future utilities. We also devise a learning algorithm with provable convergence to obtain the optimal policies. Evaluations using three month traces of 975 Sina Weibo users have been conducted, and the results validate the effectiveness of the proposed framework by showing that the utilities of all entities are significantly improved compared with traditional systems.

Reflective wrist photoplethysmograph (PPG), obtained by a watch or wristband, can provide a natural and unconstrained way for daily life heart rate monitoring. However, reflective wrist PPG often suffers from poor signal quality and various distortions due to daily life motion artifact. In this paper, we analyze the influence of motion artifact on reflective wrist PPG signals, and propose a method to extract reliable heart rate from such distorted PPG signals. The proposed method consists of adaptive filtering, heart rate selection, and motion identification. Experimental results show that our proposed method can generate reliable heart rate values from wrist PPG signals with different types of motion artifact.

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

This paper studies the critical problem of maximizing the aggregate data utility under the practical constraint on budget in mobile crowd sourced sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behaviors, private cost information of smartphones and budget constraint. In this paper, we propose a combinatorial auction mechanism based on a redundancy-aware reverse auction framework. It consists of an approximation algorithm for winning bids determination and a critical payment scheme. Our mechanism achieves truthfulness, individual rationality, computational efficiency, budget feasibility and high redundancy-aware data utility.

Partially overlapped channels are barely used for concurrent transmission in WiFi networks, since they lead to collisions where the collided packets cannot be decoded successfully. In this paper, we observe that the actual corrupted symbols by partial-channel interference in OFDM-based WiFi networks are not as severe as we expected. There remains extra coding redundancy that can be exploited from the corrupted symbols, and utilized for packet recovery. Accordingly, we present a novel paradigm termed Piros, in order to Push the lImits of partially concurrent transmission in WiFi networks. Piros strategically leverages the coding redundancy according to the overlap portion in a distributed manner, and extracts useful decoding information from the corrupted symbols to decode the packet with partial-channel interference.

Non-negative matrix factorization (NMF) decomposes any non-negative matrix into the product of two low dimensional non-negative matrices. Since NMF learns effective parts-based representation, it has been widely applied in computer vision and data mining. However, traditional NMF has the risk learning rank-deficient basis on high-dimensional dataset with few examples especially when some examples are heavily corrupted by outliers. In this paper, we propose a Logdet divergence based sparse NMF method (LDS-NMF) to deal with the rank-deficiency problem. In particular, LDS-NMF reduces the risk of rank deficiency by minimizing the Logdet divergence between the product of basis matrix with its transpose and the identity matrix, meanwhile penalizing the density of the coefficients. Since the objective function of LDS-NMF is nonconvex, it is difficult to optimize. In this paper, we develop a multiplicative update rule to optimize LDS-NMF in the frame of block coordinate descent, and theoretically prove its convergence. Experimental results on popular datasets show that LDS-NMF can learn more stable representations than those learned by representative NMF methods.

Non-negative matrix factorization (NMF) decomposes a group of non-negative examples into both lower-rank factors including the basis and coefficients. It still suffers from the following deficiencies: 1) it does not always ensure the decomposed factors to be sparse theoretically, and 2) the learned basis often stays away from original examples, and thus lacks enough representative capacity. This paper proposes a local coordinate projective NMF (LCPNMF) to overcome the above deficiencies. Particularly, LCPNMF induces sparse coefficients by relaxing the original PNMF model meanwhile encouraging the basis to be close to original examples with the local coordinate constraint. Benefitting from both strategies, LCPNMF can significantly boost the representation ability of the PNMF. Then, we developed the multiplicative update rule to optimize LCPNMF and theoretically proved its convergence. Experimental results on three popular frontal face image datasets verify the effectiveness of LCPNMF comparing to the representative methods.

This paper studies the critical problem of maximizing the aggregate data utility under budget constraint in mobile crowd sourced sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behaviors, and private cost information of smartphones. Most of existing approaches do not consider the important performance objective - maximizing the redundancy-aware data utility of sensing data collected from smartphones. Furthermore, they do not consider the practical constraint on budget. In this paper, we propose a combinatorial auction mechanism based on a reverse auction framework. It consists of an approximation algorithm for winning bids determination and a critical payment scheme. The approximation algorithm guarantees a constant approximation ratio at polynomial-time complexity. The critical payment scheme guarantees truthful bidding. The rigid theoretical analysis demonstrates that our mechanism achieves truthfulness, individual rationality, computational efficiency, and budget feasibility. Extensive simulations show that the proposed mechanism produces high redundancy-aware data utility.

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

Major cellular operators are planning to upgrade to high-speed 4G networks, but due to budget constraints, they have to dynamically plan and deploy the 4G networks through multiple stages of time. By considering one-time deployment cost, daily operational cost and 3G network congestion, this paper studies how an operator financially manages the cash flow and plans the 4G deployment in a finite time horizon to maximize his final-stage profit. The operator provides both the traditional 3G service and the new 4G service, and we show that users will start to use the 4G service only when it reaches a sizable coverage. At each time stage, the operator first decides an additional 4G deployment size, by predicting users' responses in choosing between the 3G and 4G services. We formulate this problem as a dynamic programming problem, and propose an optimal threshold-based 4G deployment policy. We show that the operator will not deploy to a full 4G coverage in an area with low user density or high deployment/operational cost. Perhaps surprisingly, during the 4G deployment process, we show that the 4G subscriber number first increases and then decreases, as the 4G service helps mitigate 3G network congestion and increases its QoS.

Stimulating both service users and service providers is of paramount importance to mobile crowdsourcing. A few incentive mechanisms have been proposed, but all of them have focused only on one-sided interactions either among service users or among service providers. For the first time, to the best of our knowledge, we investigate the important two-sided online interactions among service users and service providers in mobile crowdsourcing. We model such interactions as online double auctions, explicitly taking the dynamic nature of both users and providers into account We propose a general framework for the design of truthful online double auctions for dynamic mobile crowdsourcing. The framework is expressive and can work with different price schedules. We propose price-ranked online double auctions with four price schedules to implement the framework, which are suitable for different scenarios. With theoretical analysis and extensive simulations we demonstrate that the proposed auctions are strategy-proof, individual rational, and ensure budget balance.

Today's WLANs are struggling to provide desirable features like high efficiency, fairness and QoS because of the use of Distributed Coordination Function (DCF). In this paper we present OpenTDMF, an architecture to enable TDMA on commodity WLAN devices. Our hope is to provide the desirable features without entirely rebuilding the WLAN infrastructure. OpenTDMF is inspired by and architecturally similar to Software Defined Networking (SDN). Specifically, we leverage the backhaul of WLAN to coordinate all the stations for channel access. This fine-grained coordination is performed in a decoupled control plane which includes a central controller and programmable APs. To realize OpenTDMF on commodity WLAN devices, we develop several novel techniques to achieve μs-level time synchronization among all the APs. We also enable AP-triggered uplink transmission so that all the transmissions in the WLAN can be determined. We implemented a prototype of OpenTDMF based on commodity WLAN devices. Empirical results validate the OpenTDMF design and demonstrate its benefits.

Similar to TV bands, the guardband frequencies are not occupied therefore are whitespace that potentially allows additional communication activities. Considering the difference to TV whitespace, we propose independent communication for guardband whitespace. In this paper, we present the Pilotfish system which realizes independent communication and turns guardband whitespace into new communication channels. To address the big challenges of interference mitigation, we employ novel PHY design which includes specially customized FBMC and an Nulled Decoding technique to null the strong background signal in guardbands. We implemented Pilotfish using software radio system. Empirical evaluation results validate the Pilotfish design in both PHY and MAC.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resource-constrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED.

Non-negative matric factorization (NMF) decomposes a given data matrix X into the product of two lower dimensional non-negative matrices U and V. It has been widely applied in pattern recognition and computer vision because of its simplicity and effectiveness. However, existing NMF methods often fail to learn the sparse representation on high-dimensional dataset, especially when some examples are heavily corrupted. In this paper, we propose a robust local coordinate NMF method (RLCNMF) by using the maximum correntropy criteria to overcome such deficiency. Particularly, RLCNMF induces sparse coefficients by imposing the local coordinate constraint over both factors. To solve RLCNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular image datasets verify the effectiveness of RLCNMF comparing with the representative methods.

To match wireless users' soaring traffic demand, spectrum regulators are considering allocating additional spectrum to the wireless market. There are two major directions for the spectrum allocation: licensed (e.g., 4G cellular service) and unlicensed services (e.g., Super Wi-Fi service). The 4G service provides a ubiquitous coverage, has a higher spectrum efficiency, and often charges users a high service price. The Super Wi-Fi service has a limited coverage, a lower spectrum efficiency, but often charges users a low service price. The spectrum regulator now simply allocates the spectrum to maximize its income, but such an income-centric allocation does not ensure the best spectrum utilization by the users. This motivates us to design a new spectrum allocation scheme which jointly considers the spectrum regulator's income and the users' aggregate utility by investigating three market tiers: the spectrum regulator, 4G and Super Wi-Fi operator coalitions, and all the wireless users. We formulate it as a three-stage game and derive the unique subgame perfect equilibrium. Compared with the traditional income-centric allocation, we prove that the proposed scheme significantly improves users' aggregate utility with a limited spectrum regulator's income loss.

In cognitive radio networks (CRNs), secondary users (SUs) can flexibly access primary users' (PUs') idle spectrum bands, but such spectrum opportunities are dynamic due to PUs' uncertain activity patterns. In a multihop CRN consisting of SUs as relays, such spectrum dynamics will further cause the invalidity of predetermined routes. In this paper, we investigate spectrum-mobility-incurred route-switching problems in both spatial and frequency domains for CRNs, where spatial switching determines which relays and links should be reselected and frequency switching decides which channels ought to be reassigned to the spatial routes. The proposed route-switching scheme not only avoids conflicts with PUs but also mitigates spectrum congestion. Meanwhile, tradeoffs between routing costs and channel switching costs are achieved. We further formulate the route-switching problem as the Route-Switching Game, which is shown to be a potential game and has a pure Nash equilibrium (NE). Accordingly, efficient algorithms for finding the NE and the$\epsilon $–NE are proposed. Then, we extend the proposed game to the incomplete-information scenario and provide a method to compute the Bayesian NE. Finally, we prove that the price of anarchy of the proposed game has a deterministic upper bound.

In the spectrum auction, if a buyer locates in a “critical” place, interfering with a lot of other buyers, his occupancy of the spectrum may deprive many other transmission opportunities. In this paper, we propose a Location-aware Online Truthful doUble auction Scheme (LOTUS), which incorporates the buyers' location information into auction mechanism design. In the online auction, the biggest challenge is how to allocate the spectrums based on the knowledge in the current time slot, without knowing the spectrum requests that may come afterward. To solve this problem, we propose considering the opportunity cost of allocating the spectrum to a buyer based on his local interference conditions. We introduce the “interference discount” to markdown a buyer's bid if he induces a wide range of interference. Furthermore, we take into account the spectrum heterogeneity and design mechanisms that guarantee the economic robustness of the auction. The simulation results show that LOTUS outperforms the existing online auction mechanism, significantly improving buyers' and sellers' utility.

In secondary spectrum trading markets, auctions are widely used by spectrum holders (SHs) to redistribute their unused channels to secondary wireless service providers (WSPs). As sellers, the SHs design proper auction schemes to stimulate more participants and to maximize the revenue from the auction. As buyers, the WSPs determine the bidding strategies in the auction to better serve their end users. In this paper, we consider a three-layered spectrum trading market consisting of the SH, the WSPs, and the end users. We jointly study the strategies of the three parties. The SH determines the auction scheme and spectrum supplies to optimize its revenue. The WSPs have flexible bidding strategies in terms of both demands and valuations considering the strategies of the end users. For this market, we design a novel auction mechanism called FlexAuc to enable dynamic supplies and demands in the auction. We theoretically prove that FlexAuc not only maximizes the social welfare but also preserves other nice properties such as truthfulness and computational tractability.

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

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

For wireless service providers (WSP), the emerging femtocell market brings new opportunities as well as challenges. It is difficult for a WSP to have complete information of the technical strength of other WSP in the market, and it is hard to estimate the user demand, which is influenced by the price and service quality of all competing WSP in the market. To address these problems, in this paper, we propose an economic framework for the WSP to maximize their utility, via a joint pricing and spectrum allocation strategy, under the condition of incomplete information of other rival WSP in the market. We study two scenarios: 1) all WSP enter the market at the same time; 2) the WSP enter the market at different times. In both scenario, we formulate the problem as a Bayesian game, and derive the Bayesian Nash equilibrium. The simulation results verify that the proposed joint pricing and spectrum allocation strategy outperforms sole pricing or sole spectrum allocation strategy. Interestingly, when the WSP enter the market at different times, the later entered WSP chooses more aggressive pricing and spectrum allocation strategy than the early entered WSP.

Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or under-select modulation schemes which limits throughput performance. To fill-in the gap, we propose TiM (Time-line Modulation), a novel 3-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200%.

Stimulating participation from smartphone users is of paramount importance to mobile crowd sourcing systems and applications. A few incentive mechanisms have been proposed, but most of them have made the impractical assumption that smartphones remain static in the system and sensing tasks are known in advance. The existing mechanisms fail when being applied to the realistic scenario where smartphones dynamically arrive to the system and sensing tasks are submitted at random. It is particularly challenging to design an incentive mechanism for such a mobile crowd sourcing system, given dynamic smartphones, uncertain arrivals of tasks, strategic behaviors, and private information of smartphones. We propose two truthful auction mechanisms for two different cases of mobile crowd sourcing with dynamic smartphones. For the offline case, we design an optimal truthful mechanism with an optimal task allocation algorithm of polynomial-time computation complexity of O (n+γ)3, where n is the number of smartphones and γ is the number of sensing tasks. For the online case, we design a near-optimal truthful mechanism with an online task allocation algorithm that achieves a constant competitive ratio of 1:2. Rigorous theoretical analysis and extensive simulations have been performed, and the results demonstrate the proposed auction mechanisms achieve truthfulness, individual rationality, computational efficiency, and low overpayment.

Rate adaptation is an essential component in today's wireless standards, for its ability to adaptively approach the channel capacity, and maximize the system throughput. The difficulty in rate adaptation stems from estimating the optimal data rate in a fluctuated channel. Previous wisdoms leverage PHY layer information for rate estimation, such as Soft PHY hints or Channel State Information. These information solely comes from one same layer, which are insufficient to track the optimal data rate. We observe that by investigating the information in both PHY layer decoder and upper layer protocol headers, more pilots can be exploited to estimate the optimal data rate across both time and frequency domain. These smart pilots help remove the residual channel effect and calibrate the CSI with minimum overhead. Based on the calibrated CSI, we propose a novel greedy rate selection algorithm to harness frequency diversity, which obtains the optimal data rate over all the subcarriers. Our experiments on GNU radio test bed show that Smart Pilot quickly tracks the link variance, and reduces the residual channel effect by 87%. Further, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

Recently, directional antenna has shown great potential to deploy in indoor environments and significantly improve wireless network capacity. Due to its directionality, the orientation of antenna becomes a critical issue especially for moving devices while exchanging data with directional AP. Previous work largely focused on scenarios, in which the clients are stable at most of the time and addressed the issue of moving device by remeasurement of the whole network information such as received signal strength, conflict graph. However, re-collection of such information will introduce significant overhead. To adjust directional antenna quickly and wisely in real-time, in this paper, we introduce ADAS, a system that adjust orientation of directional antenna with sensor hints from mobile devices. The key idea of ADAS system is to obtain the movement behavior and location information from sensors equipped in mobile devices, then the AP adapts its orientation accordingly. We implement ADAS system on commercial directional antenna and evaluate its performance under different configurations. The experiment results demonstrate that directional AP in ADAS system could adapt to mobility with less overhead.

In this paper, we tackle the problem of stimulating smartphone users to join mobile crowdsourcing applications with smartphones. Different from existing work of mechanism design, we uniquely take into consideration the crucial dimension of location information when assigning sensing tasks to smartphones. However, the location awareness largely increases the theoretical and computational complexity. In this paper, we introduce a reverse auction framework to model the interactions between the platform and the smartphones. We rigorously prove that optimally determining the winning bids is NP hard. In this paper we design a mechanism called TRAC which consists of two main components. The first component is a near-optimal approximate algorithm for determining the winning bids with polynomial-time computation complexity, which approximates the optimal solution within a factor of 1 + ln(n), where n is the maximum number of sensing tasks that a smartphone can accommodate. The second component is a critical payment scheme which, despite the approximation of determining winning bids, guarantees that submitted bids of smartphones reflect their real costs of performing sensing tasks. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanism achieves truthfulness, individual rationality and high computation efficiency.

Cognitive radio (CR) technique is considered an effective mechanism to relieve the spectrum scarcity issue, where the secondary users (SUs) can utilize the idle spectrum of the primary users (PUs). How the performance of the wireless network will be influenced by the introduction of CR technique has been attracting much attention in past years. While many efforts have been made to study the cognitive radio network, where the data source and the destination (S-D) is homogeneously distributed, the research on cognitive radio networks (CRN) with heterogeneous S-D distribution is still very limited. In this paper, we investigate the throughput and delay scaling law in the heterogeneous cognitive radio network (HCRN), where the S-D pair follows a rank based model and SUs provide relay service for PUs in reciprocating the utilization of PUs' idle spectrum. By applying a cellular TDMA scheduling scheme, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with respect to the S-D heterogeneity exponent denoted by α. In addition, the delay scaling are derived for both primary and secondary networks and shown to be altering in accordance with α. Further, we reveal that the density of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput.

In spectrum trading markets, auctions are organized by spectrum holders (SHs) to distribute channels. As buyers, wireless service providers (WSPs) acquire channels to deploy services to end users. To optimize the profits, it is essential for the WSPs to determine their bidding strategies, which are affected by two key aspects: the service position to the end users and the auction schemes enforced by the SH. In this paper, we jointly study the strategy of the SH in the auction design and the WSPs' strategies in the service provisions and biddings. The WSP's optimal strategy in the auction can be flexible in term of demands and valuations. To optimize social welfare and enable the WSPs to reveal truthful flexible demands, we design Flexauc, a novel auction mechanism for the SH. We prove theoretically that Flexauc not only maximizes the social welfare but also preserves other nice properties: truthfulness and computational tractability.

Wireless video stream delivery is choppy. This problem becomes much severer in MU-MIMO's simultaneous video transmission within the same band. Conventional schemes achieve graceful video delivery by harnessing from high data redundancy. However, with concurrent transmission in the same band, the leverage of high data redundancy leads to high probability of collisions and packets loss, which limits the performance. In concurrent video transmission, achieving efficiency over varied link condition is the main issue. To address this issue, this paper presents SimCast (Simultaneous), a cross-layer design for achieving efficient concurrent video uploading/downloading in MU-MIMO WLANs. The key idea of SimCast is to harness frequency diversity of the channel and spatial similarity of users. We implemented SimCast on USRP2 and conducted extensive simulations. Result shows that SimCast achieves higher throughput than traditional schemes by 1.2× on average. Video quality of SimCast outperforms competitive schemes, which is up to 5 dB in PSNR.

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

We analyze the scaling law in wireless ad hoc networks with the correlated mobility model. The former work about correlated mobility has shown the maximum throughput and the corresponding delay of several sub-cases, but the optimal throughput performances under various delay tolerant condition (the optimal delay-throughput tradeoff) remains open. We study the properties of correlated mobility model and establish the upper bound of delay-throughput tradeoff for several sub-cases. Then we find out an achievable lower bound by studying the optimal scheduling parameters and their constrains. We exploit the node correlation to achieve the delay-throughput tradeoff and give a picture that how node correlation impacts the packet delay, asymptotic throughput, and their tradeoff.

ZigBee and other wireless technologies operating in the (2.4GHz) ISM band are being applied in Wireless Personal Area Networks (WPAN) for many medical applications. However, these low duty cycle, low power, and low data rate medical WPANs suffer from WiFi co-channel interferences. WiFi interference can lead to longer latency and higher packet losses in WPANs, which can be particularly harmful to safety-critical applications with stringent temporal requirements, such as ElectroCardioGraphy (ECG). This paper exploits the Clear Channel Assessment (CCA) mechanism in WiFi devices and proposes a novel policing framework, WiCop, that can effectively control the temporal white-spaces between WiFi transmissions. Such temporal white-spaces can be utilized for delivering low duty cycle WPAN traffic. We have implemented and validated WiCop on SORA, a software-defined radio platform. Experimental results show that with the assistance of the proposed WiCop policing schemes, the packet reception rate of a ZigBee-based WPAN can increase by up to 116% in the presence of a heavy WiFi interferer. A case study on the medical application of WPAN ECG monitoring demonstrates that WiCop can bound ECG signal distortion within 2% even under heavy WiFi interference. An analytical framework is devised to model the CCA behavior of WiFi interferers and the performance of WPANs under WiFi interference with or without WiCop protection. The analytical results are corroborated by experiments.

Existing work on indoor localization achieves accuracy by conducting site surveys or deploying additional infrastructures. In this paper, we study the feasibility of designing a low cost scheme, which is based on pedestrian dead reckoning and free from these requirements. Our method targets the scenarios where there are a large number of pedestrians in the public places holding their mobile phones while walking. First, we make one key observation that positioning errors brought by different pedestrians are diverse and can be mutually compensated. Second, we leverage the acoustic communication ability in mobile phone to enable users to exchange their locations when they meet with each other within a short distance. The received location information is crowdsourced to get a better location estimate. Finally, we use extensive experiments to evaluate the proposed scheme; the experiments are conducted in a 3,500 square-meter area with more than 18 hours (more than 100 km) real walking traces. Results show that our method is able to reduce the median localization error to 4 m when the user density of the area is more than 50/3,500 per square-meter.

To accommodate the soaring mobile broadband traffic, the Federal Communications Commission (FCC) in the U.S. sets out to retrieve under-utilized spectrum (e.g., TV Whitespace) and lay the groundwork for spectrum redistribution. Auction is an efficient way to allocate resources to those who value them the most. The large pool of spectrums to be released, especially the ones in TV Whitespace, consist of wide-range frequencies. Apart from spatial reuse, spectrum heterogeneity imposes new challenges for spectrum auction design: 1) Wireless service providers with different targeted cell coverages have different spectrum frequency preferences; 2) interference relationship is frequency-dependent due to frequency-selective signal fading. Unfortunately, existing spectrum auction mechanisms either assume spectrum valuation is homogeneous or use homogeneous interference graph to group buyers who can reuse the same spectrum. In this paper, we propose TAMES, an auction framework for heterogeneous spectrum transaction. We consider a multi-seller-multi-buyer double auction, in which every buyer submits a bid, consisting of the spectrum demand and a bidding profile of prices for spectrums contributed by all sellers. A novel buyer grouping approach is proposed to tackle the problem of heterogeneous interference graph. TAMES is proved to be truthful as well as individually rational. The simulation results show that TAMES significantly improves spectrum utilization, sellers' revenue and buyers' utility by making smart use of spectrum heterogeneity, while keeping low running time comparable with existing auction mechanisms. Moreover, via simulation, we show how to help buyers obtain continuous spectrums which further improves buyers' satisfaction.

Improving channel utilization is a well-known issue in wireless networks. In traditional point-to-point wireless communication, significant efforts had been made by the existing studies on enhancing the utilization of the channel access time. However, in the emerging wireless network using MU-MIMO, considering only the time domain in channel utilization is not sufficient. As multiple transmitters are allowed to transmit packets simultaneously to the same AP, allowing more antennas at AP would lead to higher channel utilization. Thus, the channel utilization in MU-MIMO should consider both time and spatial domains, i.e., the channel access time and the antenna usage, which have not been considered in the existing methods. In this paper, we point out that the fundamental problem is lacking of the antenna information of contention nodes in channel contention. To address this issue, we propose a new MAC-PHY architecture design, CUTS, to allow distributed nodes effectively contend for the channel and utilize the channel in both domains. Particularly, CUTS adopts interference nulling to attach the antenna information in channel contention. Meanwhile, techniques such as channel contention in frequency domain and ACK in frequency domain using self-jamming are adopted. Through the software defined radio-based real experiments and extensive simulations, we demonstrate the feasibility of our design and illustrate that CUTS provides better channel utilization with the gain over IEEE 802.11 reaching up to 470 percent.

In this paper, we study the problem of achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators. To achieve the target above, we propose a risk-constrained stochastic programming decision framework. Then, we formulate a risk-constrained expected energy cost minimization problem with the uncertainties in spot price and workload. To solve the formulated problem, we use a decomposition-based cutting plane algorithm. Finally, extensive evaluations based on real-life data show the effectiveness of the proposed decision framework.

Efficient identification of tags has been an essential operation for Radio Frequency IDentification (RFID) systems. In this paper, we consider the crucial problem of collecting all tags in a large-scale system through a handheld RFID reader. The reader has to move around due to the limited communication range of tags. We focus on the minimization of power consumption of the reader given the constraint on its movement distance. Two challenges must be addressed. First, the communication range of a tag is dependent on the reader. There is an intrinsic tradeoff between power saving and movement distance. Second, the number of sites at which the reader can collect tags can be numerous and the problem complexity is extremely high. We theoretically prove that the problem of minimizing the energy consumption of the reader is NP Complete (NPC). To solve the problem, we first analytically reveal that the time needed for reading a given number of tags is linearly proportional to the number of tags only. With this insight, we next propose an approach called ePath by constructing an energy-efficient candidate path and then incrementally pruning the path when the tag locations are given. We further relax the assumption on tag locations by extending ePath to exploit the tag distribution density knowledge only. Extensive simulations have been performed, and results show that our approach significantly reduces the power consumption of the reader comparing to an existing approach.

According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are therefore essential for database operators to manage geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSDs). In the service plan scheme, the WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans.

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

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

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

Nowadays, cardiovascular disease (CVD) has become a disease of the majority. As an important instrument for diagnosing CVD, electrocardiography (ECG) is used to extract useful information about the functioning status of the heart. In the domain of ECG analysis, cluster analysis is a commonly applied approach to gain an overview of the data, detect outliers or pre-process before further analysis. In recent years, to provide better medical care for CVD patients, the cardiac telehealth system has been widely used. However, the extremely large volume and high update rate of data in the telehealth system has made cluster analysis challenging work. In this paper, we design and implement a novel parallel system for clustering massive ECG stream data based on the MapReduce framework. In our approach, a global optimum of clustering is achieved by merging and splitting clusters dynamically. Meanwhile, a good performance is gained by distributing computation over multiple computing nodes. According to the evaluation, our system not only provides good clustering results but also has an excellent performance on multiple computing nodes.

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide improved indoor coverage and higher voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto networks is regarded as the most ideal access control mechanism to help offload macrocell traffic to femtocell, thus enhancing overall network performance. However, without suitable incentive mechanism, the Femtocell Service Providers (FSPs) are not willing to share their femtocell resource with the Macrocell Service Provider (MSP). To address this problem, in this paper, we propose an ACcess Permission (ACP) transaction framework, in which a single MSP purchases ACP from multiple FSPs in various locations throughout T timeslots, and FSPs who have overlapped coverage compete with each other for selling their ACP. However, we are facing the challenge that the demand of MSP in each location dynamically changes at each timeslot. At the start of each timeslot, FSPs are unaware of the demand of MSP, which impedes them to choose an ideal strategy that yields high payoff. To address the problem of information incompleteness, we propose an adaptive strategy updating algorithm, which is based on online learning process and enables FSPs to obtain guaranteed payoff. We conduct simulations to evaluate the payoff and the payoff gap of the FSPs when the MSP's demand is constant, quasi-constant or probabilistic. We also show that the payoff of the FSPs is affected by the learning speed of the proposed algorithm.

In this paper, we investigate the coexistence problem between the 802.22 and the 802.11af systems in the TV White Spaces (TVWS). We focus on the design of a co-channel coexistence scheme for the 802.22 customer-premises equipments (CPE) and the 802.11af systems. 802.22 and 802.11af are two typical standards envisioned to be widely adopted in the future. However, these two standards are heterogeneous in both power level and PHY/MAC design, making their coexistence challenging. To avoid mutual interference between the two systems, existing solutions have to allocate different channels for the two networks. Due to the city-wide coverage of the 802.22 base station (BS), the spectrum utilization is compromised with existing schemes. In this paper, we first identify the challenges to enable the co-channel coexistence of the 802.22 and the 802.11af systems and then propose a busy-tone based framework. We design a busy-tone for the 802.22 CPEs to exclude the hidden 802.11af terminals. We also show that it is possible for the 802.11af systems to identify the exposed 802.22 CPE transmitters and conduct successful transmissions under interference. We show through extensive simulations that the spectrum utilization can be increased with the proposed co-channel coexistence scheme.

In this paper, we consider the scenario where a secondary operator leases licensed spectrum from a spectrum owner and then serves its end users with both licensed and unlicensed bands. An end user can decide its traffic segmentation in terms of the percentage of its total amount of traffic demand transmitted via licensed and unlicensed band respectively. The optimal spectrum investment and pricing decision of the secondary operator is studied considering the user-determined traffic segmentation. We model and analyze the interactions among the spectrum owner, the secondary operator and the end users with Stackelberg game. By deriving optimal strategies for the three types of game players, we show that the end users can increase their utility by determining a proper traffic segmentation on the licensed and unlicensed band considering both the service price and the quality of service (QoS). Also the secondary operator and the spectrum owner can make more profit leveraging dynamic spectrum leasing.

To accommodate users' ever-increasing traffic in wireless broadband services, the Federal Communications Commission (FCC) in the U.S. is considering allocating additional spectrum to the wireless market. There are two major directions: licensed (e.g. 3G) and unlicensed services (e.g. Wi-Fi). On the one hand, 3G service can realize a high spectrum efficiency and provide ubiquitous connection. On the other hand, the Wi-Fi service (often with limited coverage) can provide users with high-speed local connections, but is subject to uncontrollable interferences. Regarding spectrum allocation, prior studies only focused on revenue maximization. However, one of FCC's missions is to better improve all wireless users' utilities. This motivates us to design a spectrum allocation scheme that jointly considers social welfare and revenue. In this paper, we formulate the interactions among the FCC, typical 3G and Wi-Fi operators, and the endusers as a three-stage dynamic game and derive the equilibrium of the entire game. Compared to the benchmark case where the FCC only maximizes its revenue, the consideration of social welfare will encourage the FCC to allocate more spectrum to the service which lacks spectrum to better serve its users. Such consideration for the social welfare, to our delight, brings limited revenue loss for the FCC.

Spectrums are heterogeneous, especially from the aspect of their central frequency. According to signal propagation properties, low-frequency spectrum generally has lower path loss, thus longer transmission range, compared with high-frequency spectrum. Cellular operators with different targeted cell size will have different preferences for spectrums with different frequencies. Furthermore, the transmission range also affects the interference relationships among transmitters. Transmitters who can reuse the same high-frequency spectrum may interfere with each other when reusing the low-frequency spectrum, so it is difficult to decide how to construct the interference graph to exploit spectrum reusability among transmitters. Auction is considered as an efficient way for spectrum allocation. However, most of the previous works only considered homogenous spectrum auction, failing to address the problem of spectrum heterogeneity. In this paper, we propose TAMES, a Truthful Auction Mechanism for hEterogeneous Spectrum allocation, which allows buyers to freely express their different preferences towards different spectrums. Frequency-specific interference graphs are constructed to determine buyer groups. The proposed heterogeneous spectrum auction is theoretically proved to be truthful and individual rational. The simulation results verifies that the proposed auction mechanism outperforms other auction mechanisms with homogenous bid or homogenous interference graph. The proposed auction mechanism is able to yield higher buyers' satisfaction, seller's revenue and spectrum utilization.

According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are essential for database operators to manage the cost of maintaining geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSD) in a soft-license way. In the service plan scheme, WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve the Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines the pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans.

Improving channel utilization is a well-known issue in wireless networks. In traditional point-to-point wireless communication, significant efforts had been made by the existing study on enhancing the utilization of the channel access time. However, in the emerging wireless network using MU-MIMO, considering only the time domain in channel utilization is not sufficient. As multiple transmitters are allowed to transmit packets simultaneously to the same AP, allowing more antennas at AP would lead to higher channel utilization. Thus the channel utilization in MU-MIMO should consider both time and spatial domains, i.e., the channel access time and the antenna usage, which has not been considered in the existing methods. In this paper, we point out that the fundamental problem is lacking of the antenna information of contention nodes in channel contention. To address this issue, we propose a new MAC-PHY architecture design, CUTS, to utilize the channel in both domains. Particularly, CUTS adopts interference nulling to attach the antenna information in channel contention. Meanwhile, techniques such as channel contention in frequency domain and ACK in frequency domain using self-jamming are adopted. Through the software defined radio based real experiments and extensive simulations, we demonstrate the feasibility of our design and illustrate that CUTS provides better channel utilization with the gain over IEEE 802.11 reaching up to 470%.

Spectrum auction is widely applied in spectrum redistributions, especially under the dynamic spectrum management context. However, due to the high price asked by the spectrum holders, secondary users (SUs) with limited budget cannot benefit from such auction directly. Motivated by the recent group-buying behaviors in the Internet based service, we advocate that SUs can be grouped together to take part in the spectrum auction as a whole to increase their chances to win the channel. The cost and benefit of the won spectrum are then shared evenly among the SUs within the group. None of the existing auction models can be applied in this scenario due to three unique challenges: how can a group leader select the winning SUs and charge them fairly and efficiently; how to guarantee truthfulness of users' bids; how to match the heterogeneous channels to groups when one group would like to buy at most one channel. In this paper, we propose TASG, a Three-stage Auction framework for Spectrum Group-buying to address the above challenges and enable group-buying behaviors among SUs. In the first stage, we propose an algorithm to decide the group members and bids for the channels. In the second stage, we conduct auction between the group leaders and the spectrum holder, with a novel winner determination algorithm. In the third stage, the group leaders further distribute spectrum and bills to the SUs in the group. TASG possesses good properties such as truthfulness, individual rationality, improved system efficiency, and computational tractability.

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

Vehicles are becoming powerful mobile sensors, and vehicular networks provide a promising platform to support a wide range of existing large-scale monitoring applications such as road surface monitoring, and etc. In vehicular networks, inter-vehicle contacts are scarce resources for data delivery. This presents a major challenge for monitoring applications with vehicular networks. By analyzing a large dataset of taxi traces collected from around 2,600 taxis in Shanghai, China, we reveal that there is strong correlation with data readings on vehicles. Motivated by this important observation, we propose a compressive sensing based approach called CSM to monitor with vehicular networks. Two key issues must be addressed. First, there is an intrinsic tradeoff between communication cost and estimation accuracy. Second, guaranteed estimation accuracy should be provided over the highly dynamic network. To address the above issues, we first characterize the relationship between estimation error (12 error) and sparsity property of a dataset. Then, we determine two critical parameters: the minimum number of seeds and the minimum transmission hop length for compressive measurements in the network. The selection of the two parameters can reduce the communication cost while guaranteeing the required estimation accuracy. Extensive simulations based on real vehicular GPS traces collected in Shanghai, China have been performed and results demonstrate that CSM achieves much higher estimation accuracy at the same communication cost compared with other alternative schemes.

Sleep apnea, which is a common sleep disorder characterized by the repetitive cessation of breathing during sleep, can result in various diseases, including headaches, hypertension, stroke and cardiac arrest, as well as produce severe consequences such as impaired concentration and traffic accidents. A traditional diagnosis method of sleep apnea is polysomnography, which can only be conducted in sleep center with specialized personals, thus is expensive and inconvenient. Moreover, it is only used for understanding the conditions, without treatment function. Some other methods or devices have been developed to alleviate sleep apnea, such as continuous positive airway pressure (CPAP) and intraoral mandibular advancement device and surgery. However, they only provide a treatment method without detection or monitoring function. There is no existing device which can provide both apnea detection and treatment functionality. In this paper, we propose and implement a smart phone-based auto-adjustable pillow system, which enables both sleep apnea detection and treatment. Sleep apnea events can be detected in real-time using the blood oxygen sensor, accordingly, the height and shape of the pillow can be automatically adjusted to terminate the sleep apnea event. On the other hand, after the adjustment, the sensor can continuously monitor the blood oxygen signal to evaluate the effectiveness of the pillow adjustment and to help in selecting a suitable adjustment scheme. Therefore, a real-time feedback control system is formed. Besides, compared with existing diagnosis or treatment devices, our system is non-invasive, inexpensive and portable, which can be used at home or during traveling. In this paper, a real-time sleep apnea detection and classification algorithm is proposed to decide whether the pillow should be adjusted or not. We also design a real-time feedback pillow adjustment algorithm, to decide when and how to adjust the pillow and how to evaluate the effectiveness of the adjustment. We conducted experiments on 40 patients, which demonstrate that using our novel smart pillow system, both the sleep apnea duration and the number of sleep apnea events are dramatically reduced by more than 50%.

In this paper, we propose an automatic approach to segment object from stereo videos, for which the viewpoints are widely apart. We first present a novel saliency analysis to emphasize the foreground object. The saliency map is estimated by combining the depth information recovered by feature matching and the boundary information revealed by color segmentation. The object mask is extracted initially based on the saliency map and then refined by graph-cut segmentation, where color and motion information are efficiently incorporated in both data and smoothness terms. Moreover, a background image is gradually reconstructed during video segmentation, based on which an additional constraint is imposed on the data term to further improve the video segmentation. The proposed method is tested on stereo videos with widely separated viewpoints and severe background clutters. Good experimental results demonstrate the feasibility of the proposed method.

In this paper, the authors proposed to construct a high quality image based on multiple low quality input images. The relationship between one pixel and its neighbourhood should be consistent between different degraded images. Therefore, the reconstruction method is proposed by enforcing the pixel consistency property, which is ensured by estimating the parameters of piecewise image model for each pixel. Subsequently, the reconstructed coefficients are regularized within a reasonable range. Experimental results on multiple images (with different distortions of different levels) have demonstrated that the proposed method can effectively alleviate the noises meanwhile preserve the detailed information. Better quality images in terms of both objective and subjective measurements can be generated.

In Cognitive Radio Networks (CRNs), Secondary Users (SUs) are provided with the flexibility of accessing Primary Users' (PUs') idle spectrum bands but the availability of spectra is dynamic due to PUs' uncertain activities of channel reclamation. In the multi-hop CRNs consisting of SUs as relays, such spectrum mobility will cause the invalidity of pre-determined routes of data flows since some of the channels pre-assigned to the routes become unavailable. In this paper, we investigate the spectrum-mobility-incurred route-switching problem in both spatial and frequency domains for CRNs, where the spatial switching determines which relays and links should be re-selected and the frequency switching decides which channels ought to be re-assigned to the spatial routes. We further formulate the route-switching problem as the Route-Switching Game which is shown to be a potential game and has a pure Nash Equilibrium (NE). Accordingly, an efficient algorithm for finding the NE is proposed. The proposed route-switching scheme not only avoids conflicts with PUs but also mitigates spectrum congestion. Meanwhile, tradeoffs between routing costs and channel switching costs are achieved.

Recently, the peer-to-peer (P2P) live streaming system has benefited from the advent of network coding. However, it was demonstrated that malicious nodes could significantly reduce the network throughput by launching pollution attacks or entropy attacks. In this paper, we propose an efficient symmetric-key based authentication scheme for P2P live streaming system with network coding, to provide in-network detection against pollution attacks and entropy attacks simultaneously. Since the nature of P2P live streaming requires that the detection scheme has high computation efficiency and small communication overheads, we firstly propose a homomorphic message authentication code (MAC), called as PMAC, which has small key size and low computation overhead. Then, the proposed PMAC and the delayed key disclosure technique are employed to make sure that the peers could not only detect the corrupted blocks, but also upload blocks in accordance with random linear network coding. Furthermore, the performance evaluation demonstrates that the proposed scheme has both low communication and computation overheads.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multistation wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper, we propose a new communication model where the control frames can be "attachedâ to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 200 percent compared with the existing 802.11 family protocols.

Traffic estimation is crucial to a number of tasks such as traffic management and road engineering. We propose an approach for metropolitan-scale traffic estimation with probe vehicles that periodically send location and speed updates to a monitoring center. In our approach, we use the flow speed on a road link within a time slot to indicate the traffic condition of the road segment at the given time slot, which is approximated by the average value of probe speeds. By analyzing a large data set of two-year probe data collected from a fleet of around 4,000 taxis in Shanghai, China, we find that a set of probe data may contain a lot of spatiotemporal vacancies over both time and space. This raises a serious missing data problem for road traffic estimation, which results from the naturally uneven distribution of probe vehicles over both time and space. Through empirical study based on the data set of real probe data using principal component analysis (PCA), we have observed that there are hidden structures within the traffic conditions of a road network. Inspired by this observation, we propose a compressive sensing-based algorithm for solving the missing data problem, which exploits the hidden structures for computing estimates for road traffic conditions. Different from existing approaches, our algorithm does not rely on complicated traffic models, which usually require costly training with field study and large data sets. With extensive experiments based on the data set of real probe data, we demonstrate that our proposed algorithm performs significantly better than other completing algorithms, including KNN and MSSA. Surprisingly, our algorithm can achieve an estimate error of as low as 20 percent even when more than 80 percent of probe data are missing.

Providing field coverage is a key task in many sensor network applications. With unevenly distributed static sensors, quality coverage with acceptable network lifetime is often difficult to achieve. Fortunately, recent advances on embedded and robotic systems make mobile sensors possible, and we suggest that a small set of mobile sensors can be leveraged toward a cost-effective solution for field coverage. There are, however, a series of fundamental questions to be answered in such a hybrid network of static and mobile sensors: (1) Given the expected coverage quality and system lifetime, how many mobile sensors should be deployed? (2) What are the necessary coverage contributions from each type of sensors? (3) What working and moving patterns should the sensors adopt to achieve the desired coverage contributions? In this article, we offer an analytical study on these problems, and the results lead to a practical system design. Specifically, we present an optimal algorithm for calculating the contributions from different types of sensors, which fully exploits the potentials of the mobile sensors and maximizes the network lifetime. We then present a random walk model for the mobile sensors. The model is distributed with very low control overhead. Its parameters can be fine-tuned to match the moving capability of different mobile sensors and the demands from a broad spectrum of applications. A node collaboration scheme is then introduced to further enhance the system performance. We demonstrate through analysis and simulation that, in our mobile assisted design, a small set of mobile sensors can effectively address the uneven distribution of the static sensors and significantly improve the coverage quality.

Traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization, and real-time vehicle navigation. In this work, we develop a system called POVA for traffic light sensing in large-scale urban areas. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. POVA has advantages of wide coverage and low deployment cost. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges: 1) Probe reports are by nature discrete while the goal of traffic light sensing is to determine the state of a traffic light at any time; 2) there may be a very limited number of probe reports in a given duration for traffic light state estimation; and 3) a traffic light may change its state with a variable interval. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. We have implemented the system and tested it with a fleet of around 4,000 probe taxis and 2,000 buses in Shanghai, China. Trace-driven experimentation and field study show that nearly 60 percent of traffic lights have an estimation error lower than 19 percent if 20,000 probe vehicles would be employed in the urban area of Shanghai. We further demonstrate that the estimation error rate is as low as 18 percent even when the number of available reports is merely 1 per minute.

In cognitive radio networks, secondary users need to first discover neighbours and form communication links, referred to as the rendezvous process. Rendezvous between any two secondary users can only be achieved on the same channel. However, spectrum heterogeneity in cognitive radio networks complicates the rendezvous process. While most of the existing works study pair-wise rendezvous and design channel hopping sequence, in this paper we focus on the general rendezvous problem for multiple users where each user needs to discover all of its neighbours. We propose to maintain and exchange rendezvous information among encountered users, and leverage rendezvous information spread within the network to accelerate the rendezvous process. With such an idea, we propose a general message passing based framework for rendezvous protocol design, which is flexible to incorporate the existing sequence generation algorithms. For the framework, we formulate rendezvous problems from the perspective of individual user, prove the NP-completeness, and propose an efficient greedy channel switching algorithm. Based on the framework, we design several rendezvous protocols for single-hop and multi-hop networks. When channel hopping sequence generation algorithms with guaranteed rendezvous between any two users are used, the rendezvous with the proposed protocols can still be guaranteed. Simulations demonstrate that the rendezvous performance is greatly improved.

Femtocells' ability to improve the in-building coverage and capacity in a cost-efficient way has drawn significant attention from mobile operators. However, a mobile operator may lack a fixed-line network infrastructure, which is indispensable for enabling femtocell service. In this paper, we propose a hybrid cooperation framework where a mobile operator can collaborate with a fixed-line operator (as a virtual integrated operator) to provide femtocell service to indoor users. The framework consists of sequential game and Nash bargaining. The sequential game models the interactions of the operator and users. Specifically, the operator announces the price for wireless services first and then the users decide their spectrum demands in response to the given price. Then the two operators divide the cooperation benefit according to the Nash bargaining model, which makes the profit sharing fair and cooperation framework amenable to operators. We theoretically derive the unique closed-form equilibrium for the framework as well as the conditions that promote the cooperation. The simulation results verify that the cooperation framework can make more revenue for the operators and the spectrum efficiency is significantly improved.

Information platform is managed by IPSPs (Information Platform Service Providers) who aggregate and compile information from a large pool of source websites supported by Source Service Providers (SSPs). While information platform makes it much more convenient for users to browse information, there exists such an economic dilemma: Information platform may send extra user clicks to source websites (thus increasing SSPs' advertising revenue), some users get stuck on the information platform since information excerpts are presented. In this case, SSPs tend to accuse IPSPs of intellectual property infringement. To boost the development of information platform, it is better to make IPSPs and SSPs allies rather than enemies. In this paper, we propose a business framework which involves a single IPSP and multiple SSPs. Within the framework, IPSP gets paid for the user clicks sent from information platform to the source websites and SSPs donate certain amount of information to information platform. We formulate the framework as a twostage game, assuming that IPSP and SSPs are selfish and rational players who target at utility maximization. We use backward induction to get Nash Equilibrium of the game and give the best strategies for IPSP and SSPs. The numerical results have shown that the enhancement of information quality provided by source websites and increased non-material profit of information platform will improve IPSP's utility. In addition, the price charged by IPSP for user clicks and aggregated information contributed by SSPs are also affected by these factors.

The stable subscription relationships between users and Wireless Service Providers (WSPs) are established on the long-term service contracts and SIM (Subscriber Identity Module) cards. However, Apple's “Soft Sim” plan will break the subscription relationships. With SoftSim devices, users can flexibly switch WSPs at fine time scales, which provides more choices to users and benefits them. Thus more and more users will turn to SoftSim devices. But due to many challenges unsettled, it is likely that SoftSim users will coexist with regular users who are bound to certain WSPs for a long time. How to form best strategies to attract more demand, fully utilize network capacity and thus improve the revenue in the market with heterogeneous users remains a challenge for WSPs. In this paper, we address the problem of WSPs' revenue maximization in a duopoly market with the coexistence of regular and SoftSim users. We formulate the scenario as a non-cooperative two-stage game where the WSPs first announce their prices and then users decide the subscription and demand response. We derive the unique Nash Equilibrium (NE) under our framework and also give a convergence algorithm based on best response functions. The simulation results verify our theoretic conclusions.

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

The available spectrum in cognitive radio networks is usually discontinuous but wide, which provides abundant frequency domain diversity. In this paper, we identify the opportunity of leveraging the newly-emerged technique interference alignment to exploit such diversity to support concurrent transmission and improve the network throughput in secondary networks. To enable interference alignment, independent-fading subcarriers should be grouped together to provide sufficient dimensions for intended signals and non-intended interferences at the receiver side. We formulate the subcarrier grouping problem for interference alignment to maximize the number of concurrent transmissions, and propose a greedy-based algorithm to solve it, which is proved to be optimal. Simulation results show that using the proposed scheme, the total throughput in cognitive radio networks can be greatly improved.

Excessive stress may lead to health problems like headache, trouble sleeping, depression and chronic diseases such as cardiovascular and cerebrovascular diseases. In this paper we present deStress, the mobile and remote stress monitoring, alleviation and management system, whose features are: firstly it is wearable and inexpensive, which uses only one wearable stress monitor sensor and a mobile phone-based application (Android OS) to monitor stress. Secondly, deStress quantitatively assesses the user's stress level continuously, not just classifies the users into stressed or non-stressed state. Thirdly, deStress provides a system for stress monitoring and management, through which the stress data could be recorded, analyzed and shared with medical professionals. Last but not least, a novel adaptive respiration-based bio-feedback approach is implemented to alleviate stress. To the best of our knowledge, deStress is the first telehealth system dedicated to mobile and remote stress monitoring, alleviation and management. Extensive experiment are conducted in 30 persons to demonstrate the feasibility and effectiveness of deStress, and the result shows that the stress level assessment of deStress correctly indicates the mental states of the users, and under the guidance of deStress the users could alleviate their stress level dramatically.

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide better coverage and improved voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto network is regarded as the most ideal access control mechanism to enhance overall network performance. But the implementation of hybrid access is hindered by a lack of market that can motivate ACcess Permission (ACP) trading between Wireless Service Providers (WSPs) and private femtocell owners. In this paper, we propose a reverse auction framework for fair and efficient ACP transaction. Unlike strict outcome (the demand of bidder must be fully satisfied) in most of the existing works on auction design, the proposed auction model allows range outcome, in which WSP accepts partial demand fulfillment and femtocell owners makes best-effort selling. We first propose a Vickery-Clarke-Grove (VCG) based mechanism to maximize social welfare. As the VCG mechanism is too time-consuming, we further propose an alternative truthful mechanism (referred to as suboptimal mechanism) with acceptable polynomial computational complexity. The simulation results have shown that the suboptimal mechanism generates almost the same social welfare and the cost for WSP as VCG mechanism.

In this paper, we tackle the spectrum allocation problem in cognitive radio (CR) networks with time-frequency flexibility consideration using combinatorial auction. Different from all the previous works using auction mechanisms, we model the spectrum opportunity in a time-frequency division manner. This model caters to much more flexible requirements from secondary users (SUs) and has very clear application meaning. The additional flexibility also brings theoretical and computational difficulties. We model the spectrum allocation as a combinatorial auction and show that under the time-frequency flexible model, reaching the social welfare maximal is NP hard and the upper bound of worst-case approximation ratio is √m, m is the number of time-frequency slots. Therefore, we design an auction mechanism with near-optimal winner determination algorithm, whose worst-case approximation ratio reaches the upper bound √m. Further we devise a truthful payment scheme under the approximation winner determination algorithm to guarantee that all the bids submitted by SUs reflect their true valuation of the spectrum. To further address the issue and reach optimality, we simplify the general model to that only frequency flexibility is allowed, which is still useful, and propose a truthful, optimal and computationally efficient auction mechanism under modified model. Extensive simulation results show that all the proposed algorithms generate high social welfare as well as high spectrum utilization ratio. What's more, the actual approximation ratio of near-optimal algorithm is much higher than the worst-case approximation ratio.

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflicts among spectrum buyers since different frequencies have distinct communication ranges. Under such realistic scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction for HEterogeneous Spectrum, called TAHES. TAHES allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance.

The promise of high speed (over 1Gbps) wireless transmission rate at the physical layer can be significantly compromised with the current design of 802.11 DCF. There are three overheads in the 802.11 MAC that contribute to the performance degradation: DIFS, random backoff and ACK. Motivated by the recent progress in OFDM and self-interference cancellation technologies, in this paper, we propose a novel MAC design called REPICK (REversed contention and PIggy-backed ACK) to collectively address all the three overheads. The key idea in our proposal is to take advantage of OFDM subcarriers in the frequency domain to enhance the MAC efficiency. In REPICK, we propose a novel reverse contention algorithm, which enables and facilitates receivers to contend channel in the frequency domain (reversed contention). We also design an efficient mechanism which allows ACKs from receivers to be piggy-backed through subcarriers together with the contention information (piggy-backed ACK). We prove through rigorous analysis that the proposed scheme can substantially reduce the overheads associated with 802.11 DCF and a guaranteed throughput gain can be obtained. In addition, results from extensive simulations demonstrate that REPICK can improve the throughput by up to 170%.

Market based spectrum trading has been extensively studied to realize efficient spectrum utilization in cognitive radio networks (CRNs). In this paper, we utilize the concept of insurance in spectrum trading so as to improve spectrum efficiency in CRNs. We show that by additionally purchasing a specifically designed insurance contract from a PU, an SU can improve its utility since it will be insured against the potential accident, i.e., transmission failure incurred by excessively low SINR. Therefore insurance provides SUs more incentive to purchase PUs' channels and spectrum utilization in CRNs can be improved. In this paper, the original spectrum market including multiple PUs and multiple SUs are modeled as a hybrid market consisting of a spectrum market and an insurance market. In this hybrid market PUs serve as spectrum sellers as well as insurers and SUs act as spectrum buyers as well as insureds. We further model the hybrid market game as a four-stage Bayesian game between PUs and SUs. We characterize the second-best Pareto optimal (SBPO) market allocations and players' perfect Bayesian equilibrium (PBE) strategies. Furthermore, through extensive simulation, we have demonstrated that at the PBE, high risk and low risk SUs will respectively experience improvement in their utilities for approximately 23.5% and 4.6%.

We develop a system called POVA for traffic light sensing in large-scale urban areas, where traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization and real-time vehicle navigation. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges, i.e., discrete probe reports, uneven distribution of reports over time and space, and variable interval of light states. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior (MAP) estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. Trace-driven experimentation and field study show that the estimation error rate is as low as 21% even when the number of available reports is merely one per minute.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multi-station wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper we propose a new communication model where the control frames can be “attached” to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 72% compared with the existing 802.11 family protocols.

The concept of femtocell that operates in licensed spectrum to provide home coverage has attracted interest in the wireless industry due to high spatial reuse, and extensive deployments of femtocells is expected in the future. In this paper, we consider the scenario that a femtocell service provider (FSP) expects to rent spectrum from the coexisting macrocell service provider (MSP) to serve its end users. In addition to the spectrum leasing payment, the FSP may allow hybrid access of macrocell users to improve the utilities of itself and MSP, which are defined as the sum of data traffic and payment/revenue. We propose the spectrum leasing framework taking hybrid access into consideration. The whole procedure is modeled as a three-stage Stackelberg game, where MSP and FSP determine the spectrum leasing ratio, spectrum leasing price and open access ratio sequentially to maximize their utilities, and the existence of the Nash Equilibrium of the sequential game is analyzed. We characterize the equilibrium, in terms of access price, spectrum acquisition of FSP, the open access ratio, and price of anarchy via simulation. Numerical results show that both MSP and FSP can benefit from spectrum leasing, and hybrid access of femtocell can further improve their utilities, which provide sufficient incentive for their cooperation.

In this paper, we propose the depth estimation and view synthesis approaches for the scene rendering of narrow-baseline videos. Depth estimation is performed by global energy minimization using graph cut technique. A novel smoothness energy term is proposed to enforce powerful smoothness of the estimated depth. In view synthesis, smart hole filling is proposed to efficiently remove the burr artifacts along the object boundary. Experimental results show that compared with the MPEG reference software, our method can generate more accurate depth maps and achieve much better objective (PSNR) and subjective quality in view synthesis.

Retransmissions reduce the efficiency of data communication in wireless networks because of: (i) per-retransmission packet headers, (ii) contention overhead on every retransmission, and (iii) redundant bits in every retransmission. In fact, every retransmission nearly doubles the time to successfully deliver the packet. To improve spectrum efficiency in a lossy environment, we propose a new in-frame retransmission scheme using uACKs. Instead of waiting for the entire transmission to end before sending the ACK, the receiver sends smaller uACKs for every few symbols, on a separate narrow feedback channel. Based on these uACKs, the sender only retransmits the lost symbols after the last data symbol in the frame, thereby adaptively changing the frame size to ensure it is successfully delivered. We have implemented uACK on the Sora platform. Experiments with our prototype validate the feasibility of symbol-level uACK . By significantly reducing the retransmistion overhead, the sender is able to aggressively use higher data rate for a lossy link. Both improve the overall network efficiency. Our experimental results from a controlled environment and an 9-node software radio testbed show that uACK can have up to 140% throughput gain over 802.11g and up to 60% gain over the best known retransmission scheme.

Retrieving data from mobile source vehicles is a crucial routine operation for a wide spectrum of applications of vehicular networks, such as road surface monitoring and sharing. The key to improving the data retrieval performance is to increase the data availability such that a retrieving node can easily acquire its desired data from the vehicles that it contacts. Network coding has widely been exploited as an effective technique for defusing information over a network. In this paper we explore the exploitation of network coding for improving data availability in vehicular networks. By random linear codes, simple replication is avoided, and instead a node forwards a coded block which is a random combination of all data received by the node. We implement a network coding based approach for improving data availability in vehicular networks. To understand the performance of this approach, we have conducted empirical study with extensive simulations based on real vehicular GPS traces from around 2,000 taxis in Shanghai, China. We make the important observation that in spite of significant improvement up to 300% in data availability, there is a serious issue with linear correlation among the received codes, which degrades the performance of data retrieval success rate. By analyzing the real vehicular traces, we reveal that there is strong community structure with a real vehicular network and then explain that such community structure may contribute to the issue of linear dependence. Then, we point out the opportunities for improving the network coding based approach by developing community aware codes distribution techniques.

It is a well known fact that the quality of sleep is an important factor in health-related quality of life (HRQoL), and people could prevent potential problems by tracking the quality of their sleep. Unfortunately sleep scoring, which is a systematic way to address the sleep staging as well as the scoring of arousals, respiratory, cardiac, and movement events, is usually conducted with specialized equipment which is expensive and operated by specialists in dedicated sleep centers. Related research studies and products (e.g. ZEO) tried to solve this problem, but they either used multiple probes that cause discomfort to the patient, or could not score in real time. In this paper, we design and implement RASS, a portable Real-time Automatic Sleep Scoring system. RASS only requires one probe, which is inexpensive and, as a result, may be used at home or during travel. RASS accurately scores the sleeping state and detects sleep apnea in real-time based on the sensing results of pulse, blood oxygen, activity, sound and light signals. An alarm will be generated when a severely abnormal sleep state is detected. RASS has been tested with 48 patients, and the test results show that RASS could achieve higher than 84% accuracy.

The 28 papers in this special issue focus on cooperative networking. The papers can be divided into three categories. The ten papers in the first category focus on relay selection and routing in the network layer. The nine papers in the second category deal with coding and power allocation in the physical layer. The nine papers in the third category study application performance.

The articles in this special issue are devoted to the topic of cooperative networking where individual network nodes can cooperate to achieve desired network performance in a coordinated manner.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference,” can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free” coordination channel can be built. Based on this idea, we propose a DC-MAC to leverage this "free” channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250 percent.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literatures calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper, we present a detailed spectrum measurement study, with data collected in the 20 MHz to 3 GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the statistics of the collected data, including channel vacancy statistics, channel utilization within each individual wireless service, and the spectral and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2D frequent pattern mining algorithm that can predict channel availability based on past observations with considerable accuracy.

Intrusion detection is an important problem in sensor networks. Prior works in static sensor environments show that constructing sensor barriers with random sensor deployment can be effective for intrusion detection. In response to the recent surge of interest in mobile sensor applications, this paper studies the intrusion detection problem in a mobile sensor network, where it is believed that mobile sensors can improve barrier coverage. Specifically, we focus on providing$k$-barrier coverage against moving intruders. This problem becomes particularly challenging given that the trajectories of sensors and intruders need to be captured. We first demonstrate that this problem is similar to the classical kinetic theory of gas molecules in physics. We then derive the inherent relationship between barrier coverage performance and a set of crucial system parameters including sensor density, sensing range, and sensor and intruder mobility. We examine the correlations and sensitivity from the system parameters, and we derive the minimum number of mobile sensors that needs to be deployed in order to maintain the$k$-barrier coverage for a mobile sensor network. Finally, we show that the coverage performance can be improved by an order of magnitude with the same number of sensors when compared to that of the static sensor environment.

To reduce the long term electricity cost of smart appliances (SAs) with deferrable operation time in smart grid, we propose a novel energy buffering framework to intelligently schedule the distributed energy storage (DES) for the cost reduction of SAs in this paper. The proposed energy buffering framework determines the action policy (e.g., charging or discharging) and the power allocation policy of the DES to provide DES power to proper SAs at proper time with lower price than that of the utility grid, resulting in the reduction of the long term financial cost of SAs. Specifically, we first formulate the optimal decision problem in the energy buffering framework as a discounted cost Markov decision process (MDP) over infinite-horizon. Then, we propose an optimal scheme for the energy buffering framework to solve the discounted cost MDP based on online learning approach. Extensive simulation results show that the proposed optimal scheme for the energy buffering framework can significantly reduce the long term financial cost comparing with the baseline schemes and the myopic scheme.

In this paper, we focus on critical event monitoring in wireless sensor networks (WSNs), where only a small number of packets need to be transmitted most of the time. When a critical event occurs, an alarm message should be broadcast to the entire network as soon as possible. To prolong the network lifetime, some sleep scheduling methods are always employed in WSNs, resulting in significant broadcasting delay, especially in large scale WSNs. In this paper, we propose a novel sleep scheduling method to reduce the delay of alarm broadcasting from any sensor node in WSNs. Specifically, we design two determined traffic paths for the transmission of alarm message, and level-by-level offset based wake-up pattern according to the paths, respectively. When a critical event occurs, an alarm is quickly transmitted along one of the traffic paths to a center node, and then it is immediately broadcast by the center node along another path without collision. Therefore, two of the big contributions are that the broadcasting delay is independent of the density of nodes and its energy consumption is ultra low. Exactly, the upper bound of the broadcasting delay is only 3D+2L, where D is the maximum hop of nodes to the center node, L is the length of sleeping duty cycle, and the unit is the size of time slot. Extensive simulations are conducted to evaluate these notable performances of the proposed method compared with existing works.

We are interested in the sensor networks for scientific applications to cover and measure statistics on the sea surface. Due to flows and waves, the sensor nodes may gradually lose their positions; leaving the points of interest uncovered. Manual readjustment is costly and cannot be performed in time. We argue that a network of mobile sensor nodes which can perform self-adjustment is the best candidate to maintain the coverage of the surface area. In our application, we face a unique double mobility coverage problem. That is, there is an uncontrollable mobility, U-Mobility, by the flows which breaks the coverage of the sensor network. Moreover, there is also a controllable mobility, C-Mobility, by the mobile nodes which we can utilize to reinstall the coverage. Our objective is to build an energy efficient scheme for the sensor network coverage issue with this double mobility behavior. A key observation of our scheme is that the motion of the flow is not only a curse but should also be considered as a fortune. The sensor nodes can be pushed to some locations by the U-Mobility that potentially help to improve the overall coverage. With that taken into consideration, more efficient movement decision can be made. To this end, we present a dominating set maintenance scheme to maximally exploit the U-Mobility and balance the energy consumption among all the sensor nodes. We prove that the coverage is guaranteed in our scheme. We further propose a fully distributed protocol that addresses a set of practical issues. Through extensive simulation, we demonstrate that the network lifetime can be significantly extended, compared to a straightforward back-to-original reposition scheme.

In this paper, we propose a service differentiation incentive for P2P streaming system, according to peers' instant contributions. Also, a repeated game model is designed to analyze how much the peers should contribute in each round under this incentive. Simulations show that satisfying streaming quality is achieved in the Nash Equilibrium state.

Femtocell technology addresses the problem of poor indoor coverage, benefiting both wireless service provider (WSP) and end users. With the introduction of femtocell, the cross-tier interference between macro link and femto link becomes a major factor which greatly impacts the network performance. Different access control approaches, by generating different interference patterns, also severely affect the overall throughput of the network and need to be carefully investigated. Among all the access control mechanisms, hybrid access is the most promising one, which allows roaming unregistered users (referred to as macro users) to access the nearby femto base station (BS) while reserving certain resource for registered home users (referred to as femto users), improving overall network capacity. However, to successfully leverage hybrid access is challenging because the femto holders (FHs) are selfish, unwilling to share their femto facilities and spectrum resource with macro users without any incentive mechanism. In this paper, we propose a novel utility-aware refunding framework to motivate hybrid access in femtocell. Within the framework, both WSP and FHs are assumed to be selfish, and target at maximizing their own utilities. WSP provides certain refunding to motivate FHs to open their resource for macro users. FHs decide the resource allocation among femto and macro users according to the amount of refunding WSP offers. Under this framework, the optimal strategies of both WSP and FHs are analyzed by formulating the problem as a Stackelberg Game. A unique Nash Equilibrium is achieved and a hybrid access protocol is designed according to the analysis. Extensive simulations have been conducted and the results show that the utilities of both WSP and FHs are significantly improved exploiting the hybrid access mechanism.

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that all spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflict relationships among spectrum buyers since different frequencies have distinct communication ranges. Under such a scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction mechanism for HEterogeneous Spectrum, called TAHES, which allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance. Results from extensive simulation studies demonstrate the truthfulness, effectiveness and efficiency of TAHES.

In traditional cellular networks, the network planning scheme is imperative for satisfying the coverage and traffic requirement. The rapidly growing number of users in today's cellular networks demands more base stations (BSs) to accommodate the increasing traffic load. The dense deployment results in severe energy consumption, which is often overlooked by existing network planning schemes. In this paper, an energy-aware network planning scheme is proposed to reduce the energy consumption of BSs by leveraging the coverage extension functionality of the inter-cell cooperation. The network planning problem is formulated as a mixed integer programming problem, which is solved with the Lagrangian relaxation method. Numerical results show that the energy efficiency can be enhanced by as much as 20% compared with the non-cooperative scheme without violating the QoS requirement. It is also shown that the proposed scheme is robust to the energy consumption structure of the BSs, and thus can be used with various types of BSs.

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance [L/2π] + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm.

Target Tracking is an important problem in sensor networks, where it dictates how accurate a target's position can be measured. In response to the recent surge of interest in mobile sensor applications, this paper studies the target tracking problem in a mobile sensor network (MSN), where it is believed that mobility can be exploited to improve the tracking resolution. This problem becomes particularly challenging given the mobility of both sensors and targets, in which the trajectories of sensors and targets need to be captured. We derive the inherent relationship between the tracking resolution and a set of crucial system parameters including sensor density, sensing range, sensor and target mobility. We investigate the correlations and sensitivity from a set of system parameters and we derive the minimum number of mobile sensors that are required to maintain the resolution for target tracking in an MSN. The simulation results demonstrate that the tracking performance can be improved by an order of magnitude with the same number of sensors when compared with that of the static sensor environment.

Existing works in wireless sensor networks exploit many theoretical assumptions of transmission links. However, in practical system, these assumptions are more complicated and even could not hold for the unexpected impact from the extraneous factors. In this article, we conduct a comprehensive measurement study in the real application to observe, verify, discover and model the feature of transmission link with outdoor environment factors in wireless sensor networks. We design a large scale measurement system to continuously monitor the link transmission in a forest. According to more than 10 million records [2], we analyze and obtain several observations such as the RSSI and its correlation model on pair-link, bursty link in bursty mode, RSSI with environment factors, etc. These observations provides a guideline for better design of new upper-layer algorithms and protocols in sensor applications.

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

Femtocell technology is regarded as a promising way to deal with poor indoor coverage and increase spectrum spatial reuse. In this paper, we focus on the scenario that macro and femto base stations are deployed by the same Wireless Service Provider (WSP), which treats the revenue maximization as its ultimate target. In such a system, there are several design factors which will affect the overall revenue, including price decision and resource allocation between macrocell and femtocell. In this paper, we propose an economic framework, where users choose either macrocell or femtocell service to optimize their own utility and the monopolistic WSP tries to maximize its revenue via pricing and spectrum allocation strategy. Theoretical results of optimal prices for macrocell and femtocell are given. Extensive theoretical analysis is carried out to determine the spectrum allocation strategy and evaluate the revenue of the WSP. The system capacity and the ratio of macrocell and femtocell users are also discussed. The results have indicated that the revenue of the WSP is significantly improved by combining the pricing strategy and the spectrum allocation strategy.

Dynamic spectrum auction is an effective way to stimulate primary users to lease their idle spectrum and meanwhile solve the competitions among secondary users through bidding. Due to the spatial reusability of spectrum, multiple users which are separate enough can have access to the same spectrum simultaneously without interfering each other. In this paper, we propose a spectrum auction framework based on physical interference model. Instead of conflict graph we propose coexistent matrix which can characterize the cumulative interference effect to achieve a reliable allocation. A third party interference management institute is employed to compute the coexistent matrix in our protocol. Based on coexistent matrix, we study the truthful rules for our spectrum auction. We design our spectrum auction protocol to make bidders hard to form a collusive group. For the third party institute, an algorithm which can generate the coexistent matrix with polynomial time complexity is also presented. Numerical experiments are employed to evaluate the performance of our spectrum auction.

In this paper, we focus on the dynamic spectrum access of infrastructure-based cognitive radio networks, a primary network and multiple secondary networks, which are collocated with each other. To improve network performance of all networks, we propose a cooperative communication-aware spectrum leasing framework. In the proposed framework, the primary network leverages secondary APs as cooperative relays, and decides the optimal strategy on the relay selection and the price for spectrum leasing. Based on primary network's strategy, secondary networks determine the length of spectrum access time they purchase from the primary network. Finally, each network allocates the total spectrum access time of the network among its end users. The above sequential decision procedure is formulated as a Stackelberg game, with primary network acting as the leader and secondary networks as the followers, and a unique Nash Equilibrium (NE) point is achieved through backward induction analysis. At this NE point, all networks maximize their utilities in terms of transmission rate and revenue/payment. Simulation results show that the primary network and secondary networks achieve higher utilities by exploiting cooperative transmission under our proposed framework, which gives all networks incentive for cooperation.

The promise of high speed (over 1Gbps) wireless transmission rate at the physical layer can be significantly compromised with the current design in 802.11 DCF. There are three overheads in the 802.11 MAC that contribute to the performance degradation: DIFS, random backoff and ACK. Motivated by the current progress in OFDM and self-interference cancellation technologies, in this poster, we propose a novel MAC design called REPICK (REversed contention and PIggy-backed ACK) to collectively address these problems. The key idea in our proposal is to take advantage of OFDM subcarriers in the frequency domain to enhance the MAC efficiency. Specifically, in REPICK, we propose a novel reverse contention algorithm which enables receivers to contend for channel access with subcarriers in the frequency domain (reversed contention). We also design a mechanism which allows ACKs from receivers to be piggy-backed through subcarriers together with the contention information (piggy-backed ACK). We demonstrate REPICK's efficiency through both analysis and simulations.

Vehicular Ad-hoc Networks(VANETs) can facilitate many applications such as road safety, intelligent transportation and advertising. These applications usually call for multi-hop data delivery from access points to moving vehicles with user specified delay requirements. However, most existing routing protocols for VANETs only focus on message forwarding from vehicles to access points or take no account of the delay constraint. In this paper, we focus on the development of a carry-and-forward scheme that delivers data from access points to vehicles. Utilizing the vehicle's trajectory obtained from the navigation system, we propose TaDB, a Trajectory-assisted Delay Bounded Message Delivery Algorithm. To choose delivery route within delay constraint while minimizing transmission cost, TaDB uses a Cluster-Aware Link Delay Model to estimate link delay for both the Carry and the Forward strategies on each road segment. TaDB also leverages the vehicle's planned trajectory to estimate its future location. Simulation results show that TaDB can achieve a delivery ratio very close to optimal.

We investigate the fundamental relationship between node density and transmission delay in large-scale wireless ad hoc networks with unreliable links from percolation perspective. Previous works[11][2][10] have already showed the relationship between transmission delay and distance from source to destination. However, it still remains as an open question how transmission delay varies in accordance with node density. Answering this question can provide guidance for determining the number of nodes to meet the delay requirement when designing ad hoc networks. In this paper, we study the impact of node density λ on the ratio of delay and distance, denoted by γ(λ). We analytically characterize the properties of γ(λ) as a function of λ. And then we present upper and lower bounds to γ(λ). Next, we take propagation delay into consideration and obtain further results on the upper and lower bounds of γ(λ). Finally, we make simulations to verify our theoretical analysis.

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.

In this paper, we present a multiview approach to segment the foreground objects consisting of a group of people into individual human objects and track them across the video sequence. Depth and occlusion information recovered from multiple views of the scene is integrated into the object detection, segmentation, and tracking processes. Adaptive background penalty with occlusion reasoning is proposed to separate the foreground regions from the background in the initial frame. Multiple cues are employed to segment individual human objects from the group. To propagate the segmentation through video, each object region is independently tracked by motion compensation and uncertainty refinement, and the motion occlusion is tackled as layer transition. The experimental results implemented on both our sequences and other's sequence have demonstrated the algorithm's efficiency in terms of subjective performance. Objective comparison with a state-of-the-art algorithm validates the superior performance of our method quantitatively.

In this paper, we study the message delivery capacity problem in delay-constrained mobile sensor networks. The message delivery capacity specifies the maximum percentage of sensing messages that can be successfully delivered to sink nodes within a given time constraint. This captures the overall system capacity in terms of successful sensing message delivery. By taking full advantage of sensor mobility and rendezvous during senor node encounters, messages can be delivered to a sink node either directly or through relays by other sensor nodes. In this paper, for the first time, we present the delay-constrained message delivery capacity formulation in mobile sensor networks. The objective is to maximize the message delivery capacity subject to the delay and buffering constraints. We first identify a number of unique challenges involved in such systems including message relay and buffer replacement mechanisms, and we derive the capacity bound under perfect message relay and buffer replacement mechanisms. Due to an unrealistic assumption about the foreknowledge of sensor moving trajectories, we next proceed to propose a practical algorithm to approximate the maximal message delivery capacity based on current global network knowledge. Furthermore, a distributed algorithm is proposed to reduce the control overhead for information exchange. Finally, we evaluate the algorithms and examine their sensitivity with respect to delay constraints, buffer size and message relay, and replacement schemes.

This paper studies the multicast capacity gain with network coding in static ad hoc wireless network. We consider $n$ nodes deployed in a square with $k$ nodes formulating multicast tree to employ network coding. In static unicast network, Liu et al. \cite{Liujunning},\cite{Liujunning2} have proved that the capacity gain with network coding is a constant without order change. In our work, we focus on the capacity gain with network coding in static multicast network, under the system model of Li \cite{xiangyang}'s. Firstly, we modify the routing algorithm of multicast model and propose that the multicast capacity bound in \cite{xiangyang} is just a theoretical result but not very tight in practical scenario. Then in static multicast transmission we calculate the probability of existing the butterfly model to employ network coding. Our result shows that there is no order change of the capacity. Finally, We prove that the bound of the capacity gain with network coding is $\frac{\tau}{\sqrt{2}}$, where $\tau=\frac{\sqrt{3}}{2}\cdot\tau(2)$, and $\tau(2)$ is a constant in dimension two, when network coding is applied to static multicast network.

In this paper, we study the delay-constrained message delivery capacity formulation in mobile wireless networks. The message delivery capacity specifies the maximum percentage of messages that can be successfully delivered to base stations within a given time constraint. By taking full advantage of node mobility and rendezvous during mobile node encounter, messages can be delivered to a base station either directly or through relays by other nodes. We first identify a number of unique challenges involved in such systems including message relay and buffer replacement mechanisms and then derive the capacity bound under perfect message relay and buffer replacement mechanisms. We next prove that this problem is NP-hard. We proceed to propose a practical algorithm to approximate the maximal message delivery capacity. Finally, we evaluate the algorithm and examine the sensitivity with delay constraint, buffer size and message relay and replacement schemes.

In this paper, we study the delay-constrained information coverage problem in mobile wireless networks. Motivated by real application needs, our formulation takes advantage of the node mobility for information collection, which takes place when a node moves into the proximity of stationary base stations. To the best of our knowledge, we present the first formulation for the delay-constrained information coverage problem, which targets at optimal base stations placement with the objective of maximizing information collection within a constrained time. We prove that this problem is NP- hard and we develop theoretical analysis to derive its upper and lower performance bounds. We then develop approximation techniques and use simulations to verify their effectiveness.

Dynamic spectrum sharing in cognitive radio networks can enhance flexibility, and as a result the efficiency of spectrum usage. In this paper, we address the problem of spectrum sharing in a cognitive radio network where multiple primary and secondary strategic-users are involved. In this scenario, primary users (PUs) would like to offer part of their spectrum to secondary users (SUs) to make extra revenue. PUs face a trade-off since the more spectrum that is being shared with SUs, the more Quality of Service (QoS) degradation their own service will suffer. SUs access the Internet through an Access Point (AP). They have to decide their best strategies by taking into account service satisfaction and payment lost. The profit of PUs and SUs is directly related to the bandwidth allocation and price charging through the AP. Considering this competitive relationship, we model the scenario as a noncooperative game, and analyze it by exploring the properties of Nash Equilibrium (NE) point. The simulation results support our theoretic analysis.

To solve the scarcity of wireless spectrum, Cognitive Radio (CR) is proposed to let unlicensed wireless users (secondary users) dynamically find and access unused channels without interference to licensed users (primary users). The performance of the CR based Dynamic Spectrum Access (DSA) mechanism can be dramatically improved if the wireless spectrum is predictable, and many works has been done based on this assumption. To understand the predictability of realworld wireless spectrum, we make a large scale empirical study in this paper. The study is based on the spectrum data collected in a metro city in 7 days, ranging from 20MHz to 3GHz. Our study includes the analysis of kth-order Markov universal predictability, the experiment of kth-order Markov on-line predictor, and finally the seeking for specialized predictor for wireless spectrum. We find that 1) it's not efficient to improve prediction by increase Markov order, because on nearly half channels kth-order (k>1) Markov methods make no improvement at all, and for the rest channels, 1st-order Markov method makes the largest improvement and higher orders make little further improvement; 2) We also find that a sliding window method can improve accuracy considerably meanwhile reduce complexity of prediction model significantly.

Accurate spectrum sensing is one of the key technologies to enable dynamic spectrum access in cognitive radio networks. Recently cooperative spectrum sensing is proposed as an efficient way to achieve better sensing accuracy. It makes use of multiple nodes to sense a channel and then analyzes their results together to get more accurate results. Several works have been conducted to study this technology and they mostly focus on the cooperation in individual channels. However, cognitive radio networks usually deal with multiple channels, therefore the coordination among multiple channels needs be studied to fully explore the potential of cooperative sensing. In this paper we focus on the coordination among multiple channels in cooperative spectrum sensing. We at first study the theoretical improvement made by multi-channel coordination, then propose a centralized and a distributed algorithm to implement it. The theoretical analysis proves cooperative sensing can be improved significantly if the coordination among multiple channels is considered. To verify our analysis, we measure licensed channels then conduct simulations basing on collected data. The results show that the proposed algorithms can approximate optimal solution very well.

Energy efficient asynchronous duty cycle MAC protocols are crucial to the success of wireless sensor networks (WSNs). In asynchronous protocols, each node operates its active/sleep schedule independently and enjoys a very low duty cycle when there is no traffic. However, when a node has data to send, it has to keep active and wait until the receiver wakes up due to the lack of schedule knowledge of the receiver. In low duty cycle networks, where nodes wake up infrequently, a sender usually suffers a long period of waiting, which consumes more energy than transmitting data packet itself. In this paper, we propose a new asynchronous MAC protocol, called OCMAC, which decreases the waiting time of sender by exploring opportunistic cooperation among senders. In OC-MAC, neighboring active senders are permitted to exchange data with each other aggressively when waiting for receivers to wake up. After delegating data to another sender, a sender can go to sleep before its receiver wakes up. Though this cooperation works only when there are multiple neighboring active senders, itself almost incurs no additional overhead. Simulation results and measurements on a testbed have shown that OC-MAC helps decrease idle listening, collision and end-to-end delay further.

We study the throughput capacity of mobile wireless ad hoc networks with infrastructure support. Mobility and infrastructure support independently have been shown to be effective ways to improve capacity, but few work has analyzed the impact of their combination. In our work we consider an ad hoc network with n users and k base stations. All base stations are wired to each other with bandwidth c(n). We adopt a general mobility model where users move with arbitrary patterns within a bounded distance around their home-points, and let the area of the network scales as f^2(n). We show that for different parameters, mobility can be divided into strong, weak and trivial regimes. The per-node capacity is Θ(1/f(n))+Θ(min(k^2 c/n, k/n)) under strong mobility, and is Θ(min(k^2 c/n, k/n)) in the two latter cases. We also discuss optimal communication schemes and system parameters in each regime. Our study provides fundamental insight on the understanding and design of wireless ad hoc network.

A critical issue in wireless sensor networks (WSNs) is represented by the network throughput. To meet the throughput requirement, researchers propose multi-channel design in 802.15.4 networks to better utilize the wireless medium and avoid the co-channel interference. However, traditional orthogonal channel design restricts the number of channels and limits the throughput performance. We argue that the orthogonality is not necessary for multi-channel design in WSNs. In this paper, we investigate the feasibility of non-orthogonal channel design. In our experiment, we observe that with nonorthogonal transmission, the effect of interference comes from co-channel and inter-channel is different. More specifically, the inter-channel interference is tolerable with certain channel center frequency distance (CFD). According to that, we propose a novel scheme DCN (Dynamic CCA-threshold for Non-orthogonal transmission) which adjusts the CCA-threshold to enable the concurrent transmissions on adjacent non-orthogonal channels and thus improve the overall network throughput performance. Through comprehensive experiments on our testbed, we verify that our DCN achieves about 38.4% ~ 55.7% throughput improvement in general network configurations comparing to the default ZigBee design.

Cooperative relay networks combined with Orthogonal Frequency Division Multiplexing Access (OFDMA) technology has been widely recognized as a promising candidate for future cellular infrastructure due to the performance enhancement by flexible resource allocation schemes. The majority of the existing schemes aim to optimize single cell performance gain. However, the higher frequency reuse factor and smaller cell size requirement lead to severe inter-cell interference problem. Therefore, the multi-cell resource allocation of subcarrier, time scheduling and power should be jointly considered to alleviate the severe inter-cell interference problem. In this paper, the joint resource allocation problem is formulated. Considering the high complexity of the optimal solution, a two-stage resource allocation scheme is proposed. In the first stage, all of the users in each cell are selected sequentially and the joint subcarrier allocation and scheduling is conducted for the selected users without considering the interference. In the second stage, the optimal power control is performed by geometric programming method. Simulation results show that the proposed the interference-aware resource allocation scheme improves the system capacity compared with existing schemes. Especially, the edge users achieve more benefit.

We are interested in event collection in a 2D region where sensors are deployed to detect and collect interested events. Using traditional multi-hop routing in wireless sensor networks to report events to a sink node or base station, will result in severe imbalanced energy consumption of static sensors. In addition, full connectivity among all the static sensors may not be possible in some cases since generally the sensors are randomly deployed in the target region. In this paper, we exploit a mobile sensor as the sink node to assist the event collection by controlling the movement of the mobile sink to collect static sensor readings. A key observation of our work is that an event has spatial-temporal correlation. Specifically, the same event can be detected by multiple nearby sensors within a period of time. Thus, it is more energy-efficient if the mobile sink can selectively communicate with only a portion of static sensors, while still collecting all the interested events. In this paper, we discuss the event collection problem by leveraging the mobility of the sink node and the spatial-temporal correlation of the event, in favor of maximizing the network lifetime with a guaranteed event collection rate. We first model the problem as sensor selection problem and show that it could be solved in polynomial time, if global knowledge of events is available and there is no velocity constraints on mobile sink. We also analyze the design of a feasible movement route for mobile sink to minimize the velocity requirements for a practical system. An online scheme is then proposed to relax the assumption about global knowledge of events and we prove that the expected event collection rate can be guaranteed in theory. Through comprehensive simulation on real trace data, we demonstrate that the network lifetime can be significantly extended, comparing to some other schemes.

Spectrum sensing is one of the key enabling technologies in Cognitive Radio Networks (CRNs). In CRNs, secondary users (SUs) are allowed to exploit the spectrum opportunities by sensing and accessing the spectrum, which exhibit many critical limitations in practical environments. In this paper, we propose a new sensing service model that uses dedicated wireless spectrum sensor networks (WSSN) for spectrum sensing. The major challenge in WSSN is the design of data fusion, for which the traditional fusion scheme will produce a large amount of errors. We formulate the problem as a boundary detection problem with notable unknown erroneous inputs. To solve the problem, we propose a novel cooperative boundary detection scheme that intelligently incorporates the cooperative spectrum sensing concept and the recent advances in support vector machine (SVM). Cooperative boundary detection consists of two major components, a declaration calibration algorithm and a boundary derivation algorithm. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. A prototype system as well as simulation experiments show that compared with the traditional approaches, cooperative boundary detection can reduce the errors by up to 95% with an average reduction about 85%.

Cooperative communication is a promising technique for future wireless networks, which significantly improves link capacity and reliability by leveraging broadcast nature of wireless medium and exploiting cooperative diversity. However, most of existing works investigate its performance theoretically or by simulation. It has been widely accepted that simulations often fail to faithfully capture many real-world radio signal propagation effects, which can be overcome through developing physical wireless network testbeds. In this work, we build a cooperative testbed based on GNU Radio and Universal Software Radio Peripheral (USRP) platform, which is a promising open-source software-defined radio system. Both single-relay cooperation and multi-relay cooperation can be supported in our testbed. Some key techniques are provided to solve the main challenges during the testbed development: e.g., maximum ratio combine in single-relay transmission and synchronized transmission among multiple relays. Extensive experiments are carried out in the testbed to evaluate performance of various cooperative communication schemes. The results show that cooperative transmission achieves significant performance enhancement in terms of link reliability and end-to-end throughput.

Interference plays an important role in the communication of wireless sensor networks. In this paper, we for the first time propose the concept of tolerable interference and investigate the tolerability of interference in multi-channel sensor networks with empirical measurements. We find that the transmission in sensor networks can tolerate certain level interference from the adjacent channels, which are not orthogonal to the current transmission channel. That is to say, the center frequency distance (CFD) for concurrent transmissions could be smaller than the default setting in sensor networks (i.e., 5 MHz in ZigBee). Concretely, with comprehensive experiments, we make two interesting observations: first, the throughput could be improved by relaxing CCA-threshold with the tolerable neighboring-channel interference; second, most of the corrupted packets caused by tolerable interference only have a small portion of error bits which could be corrected by introducing packet recovery scheme. Based on the above observations, we further have some discussions for better design of WSNs protocols, e.g., the system could enable some adaptive CCA-threshold scheme and packet recovery protocol to improve the overall throughput significantly.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that, coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference", can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free" coordination channel can be built. Based on this idea we propose a DC-MAC to leverage this "free" channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250%.

Intrusion detection is an important problem in sensor networks. Prior works in static sensor environments show that constructing sensor barriers with random sensor deployment can be effective for intrusion detection. In response to the recent surge of interest in mobile sensor applications, this paper studies the intrusion detection problem in a mobile sensor network, where it is believed that mobile sensors can improve barrier coverage. Specifically, we focus on providing k-barrier coverage against moving intruders. This problem becomes particularly challenging given that the trajectories of sensors and intruders need to be captured.

Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either "connected" or "disconnected." These approaches are called connectivity-based topology control. In real environments, however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Toward this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NP-hard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(\vert E\vert ), where E is the link set of the original topology, and the space requirement for individual nodes is O(d), where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 Berkeley Mica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to six times.

Layered streaming in P2P networks has become a hot topic recently. However, the "layered" feature makes the data scheduling quite different from that for nonlayered streaming, and it hasn't been systematically studied yet. In this paper, first, according to the unique characteristics caused by layered coding, we present four objectives that should be addressed by scheduling: throughput, layer delivery ratio, useless packets ratio, and subscription jitter prevention; then a three-stage scheduling approach LayerP2P is designed to request data, where the min-cost flow model, probability decision mechanism, and multiwindow remedy mechanism are used in Free Stage, Decision Stage, and Remedy Stage, respectively, to collaboratively achieve the above objectives. With the basic version of LayerP2P and corresponding experiment results achieved in our previous work, in this paper, more efforts are put on its mechanism details and analysis to its unique features; besides, to further guarantee the performance under sharp bandwidth variation, we propose the enhanced approach by improving the Decision Stage strategy. Extensive experiments by simulation and real network implementation indicate that it outperforms other schemes. LayerP2P has also been deployed in PDEPS Project in China, which is expected to be the first practical layered streaming system for education in P2P networks.

Abstract To prolong the battery lifetime, it is important to continue designing a better energy efficient mechanism for different mobile technologies. Most of the existing works on the IEEE 802.16e sleep mode operation focus on the decision making before a mobile station switching to sleep mode state. The correlation of the decision is mainly on when and how to sleep based on the traffic demands. After the mobile station is switched to sleep mode, the deactivation of it mainly depends on new incoming traffic regardless of the actual amount. Truly, frequent switching can increase the energy cost on the mobile station, which can significantly reduce the battery lifetime. To minimize the switching frequency, we propose a novel approach to resolve this issue by making a heuristic decision during the listening interval. With this aim, we propose a real‐time heuristic algorithm, WAKSLP_DECISION, to accommodate our target. Three main decision criteria are analyzed and designed, namely the probability of buffer overflow, expected delay violation, and battery lifetime expiry, to achieve our goal. We verify the energy consumption performance with simulation experiments to validate our proposed scheme. The result shows that our scheme performs 25–30% better compared with the original standard in terms of energy consumption. We believe this algorithm is practical and implementable without changing the original standard, which can contribute both in the research community and industrial development. Copyright © 2009 John Wiley & Sons, Ltd.

In this article, we study the problem of join routing and scheduling of multi-team data flows multicasting in wireless multi-rate multi-hop networks with the objective of minimizing the time required to complete the transmission transactions. We prove in this paper that the optimal join routing and scheduling problem is NP-hard. Instead of trying to solve the optimization problem, we reformulate the problem into an intuitive form which provides intuitions to achieve optimal solution. Base on these observations, we propose a heuristic algorithm that locally optimally chooses multicast relays and multicast rates. The aim is to locally minimize the schedule time length within two-hop range so that the total transmission time approaches optimal. This algorithm explores multi-team multicast advantages as well as the multi-rate range tradeoffs. Simulations based on practical ISM band channel model and current IEEE802.11 standards' parameters show superior of our proposed algorithm in approaching optimal solution.

Recently a multiple access point (MAP) architecture is proposed for wireless local area networks (WLAN) to exploit the spatial diversity by permitting each user to associate with multiple APs. Benefited from multiple association, the promising cooperative communication technology can be utilized to further enhance the spatial diversity gain. However, cooperative transmission results in low spatial reuse efficiency with traditional physical carrier sensing (PCS) mechanism. That is because multiple cooperative nodes transmit simultaneously, which causes interference to the adjacent nodes and prevents their transmissions. Therefore the PCS range should be decreased aggressively to support more parallel cooperative transmissions. In this paper, a distributed PCS tuning scheme is proposed to enhance the spatial reuse efficiency in cooperative MAP WLAN. A cooperative node selection scheme is jointly designed to avoid the excessive interference caused by cooperative nodes transmission. Simulation results show that the proposed scheme significantly improves the throughput of cooperative MAP WLAN.

Content Distribution is a key application in an infrastructure-based wireless network. Existing works on this area use either pure broadcast transmission or pure unicast transmission. In this paper, we propose a hybrid broadcast-unicast transmission scheme to improve the efficiency of content distribution for wireless networks with multi-rate support. In the proposed scheme, access point broadcast packets to nodes within its transmission range with a deliberately selected broadcast rate. After that, the point-to-point unicast transmissions are exploited for data forwarding. It mitigates the inherit problems that comes from unicast and broadcast transmission respectively, such as severe contention and large amount of redundant re-transmission. Furthermore, cooperative communication is exploited as a physical layer technique to enhance the throughput of the network. A cooperative communication aware routing is designed accordingly, where the contention relationship between wireless links have been taken into consideration. Simulation results show that the proposed scheme outperforms existing schemes dramatically in terms of network throughput and transmission delay.

Existing congestion control protocols have significant limitations in achieving high throughput and reasonable fairness while maintaining fast convergence speed in high bandwidth-delay product networks. In this paper, we propose the general congestion control protocol (GCCP) to address this limitation. GCCP allows for aggressive behavior in large underutilized links to achieve high throughput, but leverages only one ECN bit for network utilization feedback. Once the link is sensed to be highly utilized, the protocol dedicates to fair and rapid bandwidth allocation by requiring congestion window increment is conservative and monotone decreasing with congestion window increasing. The ns2 simulations show that GCCP achieves a pretty good tradeoff between high throughput and reasonable fairness while exhibiting fast convergence speed.

The IEEE 802.16e mobile WiMAX technology aims to provide with an energy efficient communication platform for various mobile applications. In the standard, the sleep mode feature with three Power Saving Classes (PSCs) is designed to compensate for the power saving as the target. Prior work has shown that the proposed Markov Decision Process (MDP) approach can achieve the optimal performance in terms of energy consumption and packet delay through the optimal PSC selection. In this paper, we further consider the design of when to sleep and how to sleep in the system to achieve better tradeoffs between the switching frequency during idle mode and mean power consumption in overall. First, we present our optimal timeout scheme designed based on the previous proposed MDP approach, which can help the system determine when to sleep as well as how to sleep optimally with less switching frequency intuitively. To guarantee the optimality, we show the equivalency of the MDP approach and our optimal timeout scheme that both can achieve the optimal performance in terms of energy consumption level. Also, we demonstrate the performance of our proposed scheme through numerical analysis and validate it with simulation experiments using ns-2 compared with the MDP approach. Finally, we evaluate the impact of Poisson and non-Poisson traffic on our proposed optimal scheme.

We present a novel spectrum access scheme for open spectrum networks. Different from existing works, this work considers the scenario that contending secondary users have heterogeneous channel availability. It is proved to be NP hard to find the optimal spectrum assignment in this scenario. To solve this problem, a novel approximation algorithm is proposed which is based on the maximum weighted matching technique. The performance of the algorithm is evaluated through extensive simulations. Compared with the general optimal spectrum allocation scheme without considering the difference of spectrum availability, the experiments' results demonstrate that the new matching based algorithm improves the network throughput significantly, generally by 20% to 60%. The algorithm's computation complexity is also at a low level of 0(M*n2*m).

Cognitive radio networking is a promising approach to fulfill the future's need for intelligent, high-performance communication and improve the efficiency of overall spectrum utilization. Testbed evaluation of protocols and algorithms is a must for the development of cognitive radio networks. In this paper, we present an integrated testbed framework for cognitive radio networks. The framework includes necessary components for cognitive radio operations: flexible RF front end, software define signal processing, adaptive MAC layer and network layer as well as a cross layer management interface. Such a design eases the cross layer configuration and performance optimization of cognitive protocol stack, while retaining the advantage of modularity. We design a testbed for ad-hoc cognitive radio network.

Cognitive radio has been proposed in recent years to promote spectrum efficiency by exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and traffic demand among secondary users has brought significant challenge for efficient spectrum allocation in cognitive radio networks. Observing that spectrum resource can be better matched to traffic demand of secondary users with the help of relay nodes, in this paper we propose to utilize cooperative relays to assist the transmission and improve spectrum efficiency. Different from traditional cooperative communication within a single channel, in our scheme a relay node may be selected for a link to bridge the link's source and destination using its different common channels with those two nodes. With these new logical links composed of both direct link and relay link, new routing protocols are needed so that end-to-end performance can be better improved. Therefore, we define a new link cost, relay- aware link cost, which considers several aspects including channel availability, channel condition, channel utilization and potential relays. Based on this link cost, a relay-assisted routing (RAR) protocol is proposed which includes routing discovery and local adjustment. Simulation results demonstrate the effectiveness of the proposed routing scheme.

In recent integrated MAC/routing solutions for wireless sensor networks (WSNs), hop-count is exploited to build a coarse-grained logical coordinates to help forward packets towards the direction of sink. This method can retain the merits of geographic routing at the absence of exact location knowledge. However, these solutions may present low energy-efficiency and unacceptable delays in real networks since they seldom consider the impacts of low duty-cycling and link unreliability on routing. Furthermore, geographic advancement of forwarding in hop- count based coordinates is very unreliable and even towards the reverse direction. In this work, average power cost to the sink of each node is considered together with hop-count to build a fine grained logical coordinates, which can help forward packets towards the direction of sink more accurately. Then we propose an energy-efficient integrated MAC/routing (EEMR) protocol for event-driven and time-critical applications based on new logical coordinates. The optimal relay is elected in each hop dynamically, where the objective is to optimize forwarding energy-efficiency on the premise that end-to-end delay is restricted under the predefined upper bound. Analysis and extensive simulations are given to demonstrate the superiority of EEMR by comparing its performance against existing solutions.

This paper discusses a hierarchical multi-radio mesh network, where the normal clients associate with the mesh AP through the access radio and then the mesh APs communicate with each other via the mesh radio. While most of the existing studies focus on the performance issue for the mesh nodes communication with the mesh radio, in this paper, we argue that the channel assignment for the access radio will affect the overall system significantly. In this paper, we try to exploit the information exchange among the mesh nodes to help the access radio channel selection. A new metric, effective channel air time (ECAT), is proposed for channel assignment under this multi-radio mesh network. ECAT can work well when there are traffics in the overlapping channels. A real testbed has been developed to demonstrate the effectiveness of the proposed metric and scheme.

Cooperative communication is a new physical layer technique which improves link capacity by exploiting broadcast nature and spatial diversity of wireless channel. The introduction of cooperative communication in wireless networks changes the traditional definition of link and the contention relationship among links. In this paper, we focus on cooperative communication aware routing protocol design in wireless mesh networks, targeting at maximizing the overall end-to-end throughput of the whole network and meanwhile taking contention relationship among multiple links into consideration. We propose a routing metric called contention-aware cooperative metric (CCM) and prove that CCM has the isotonic property. Therefore, efficient algorithms such as Dijkstra or Bellman-Ford can be used to find CCM-based minimum cost paths. Based on CCM, we propose a routing protocol called Contention-aware Cooperative Routing (CCR) which can be implemented in both link-state and distance-vector routing protocols. Extensive simulations are conducted on ns-2 to evaluate the performance of our novel routing metric and routing protocol. The results show that CCR achieves significant throughput gain compared with hop- count-based routing and ETT-based routing. The end-to-end delay is also dramatically reduced under CCR routing.

Cognitive radio has been proposed in recent years to promote the spectrum utilization by exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and traffic demand in secondary users has brought significant challenge for efficient spectrum allocation in cognitive radio networks. Observing that spectrum resource can be better matched to traffic demand of secondary users with the help of relay node that has rich spectrum resource, in this paper we exploit a new research direction for cognitive radio networks by utilizing cooperative relay to assist the transmission and improve spectrum efficiency. An infrastructure-based secondary network architecture has been proposed to leverage relay-assisted discontiguous OFDM (D-OFDM) for data transmission. In this architecture, relay node will be selected which can bridge the source and the destination using its common channels between those two nodes. With the introduction of cooperative relay, many unique problems should be considered, especially the issue for relay selection and spectrum allocation. We propose a centralized heuristic solution to address the new resource allocation problem. To demonstrate the feasibility and performance of cooperative relay for cognitive radio, a new MAC protocol has been proposed and implemented in a Universal Software Radio Peripheral (USRP)-based testbed. Experimental results show that the throughput of the whole system is greatly increased by exploiting the benefit of cooperative relay.

We are interested in the sensor networks for scientific applications to cover and measure statistics on the sea surface. Due to flows and waves, the sensor nodes may gradually lose their positions; leaving the points of interest uncovered. Manual readjustment is costly and cannot be performed in time. We argue that a network of mobile sensor nodes which can perform self-adjustment is the best candidate to maintain the coverage of the surface area. In our application, we face a unique double mobility coverage problem. That is, there is an uncontrollable mobility, U-Mobility, by the flows which breaks the coverage of the sensor network. Moreover, there is also a controllable mobility, C-Mobility, by the mobile nodes which we can utilize to reinstall the coverage. Our objective is to build an energy efficient scheme for the sensor network coverage issue with this double mobility behavior. A key observation of our scheme is that the motion of the flow is not only a curse but should also be considered as a fortune. The sensor nodes can be pushed by free to some locations that potentially help to improve the overall coverage. With that taken into consideration, more efficient movement decision can be made. To this end, we present a dominating set maintenance scheme to maximally exploit the U-Mobility and balance the energy consumption among all the sensor nodes. We prove that the coverage is guaranteed in our scheme. We further propose a fully distributed protocol that addresses a set of practical issues. Through extensive simulation, we demonstrate that the network lifetime can be significantly extended, compared to a straight forward back-to-original reposition scheme.

Although layered streaming in heterogeneous peer-to-peer networks has drawn great interest in recent years, there's still a lack of systematical studies on its data scheduling issue. In this paper, we propose a new scheduling approach for layered video streaming, called LayerP2P. The key idea and main contributions of LayerP2P come in two-fold: 1) According to the characteristics caused by layered coding, we propose four objectives that should be achieved by data scheduling: high throughput, high layer delivery ratio, low useless packets ratio, and low subscription jitter; 2) We design a 3-stage scheduling mechanism to request absent blocks, where the min-cost flow model, probability decision mechanism and multi-window remedy mechanism are employed in Free Stage, Decision Stage and Remedy Stage, respectively. Each stage has different scheduling objective while collaborates with each other, to achieve the above four objectives. Experimental results indicate that our approach outperforms other schemes in simulation environment. Besides, LayerP2P is implemented in the PDEPS Project in China, which is expected to be the first practical layered streaming system for education in peer-to-peer networks.

Recent research has proven that network coding has great potential to improve network throughput in wireless networks. To fully exploit the performance gain brought by network coding, coding-aware routing has been studied to proactively change route of flows for creating more coding opportunities. However, in today's multi-rate wireless networks, coding may not be a wise decision as the lowest rate has to be used for coded information broadcasting, which causes significant resource waste for the high-rate links. In this paper, we propose the idea of cooperative network coding (CNC) to exploit spatial diversity for improving coding opportunity. We provide a theoretical formulation for calculating the maximal throughput of unicast traffic that can be achieved with CNC in multi-rate wireless networks. CNC-aware routing under both Alice-Bob and X-structure are discussed in this paper. The performance evaluation demonstrates that a CNC-aware route selection scheme that leverages cooperative communication to improve coding opportunity leads to higher end-to-end throughput comparing with the coding-oblivious and traditional coding-aware schemes.

Most of analysis so far for IEEE 802.11(e) WLANs focuses on saturated condition. But it is of practical value to take into account the unsaturation case. In this paper, we extend Bianchi's Markov back-off model to unsaturated condition but analyze it using renewal-reward theorem. In this more general framework, we study the fixed point solution of the system and provide a condition to guarantee both the uniqueness and balance of the fixed point. From the fixed point analysis we find that under unsaturated condition, network parameters should be designed according to the traffic load in order to avoid short-term unfairness or multistability. Then, we study the system throughput. Furthermore, in an asymptotic regime with a large number of nodes, we provide explicit formulas for the collision probability, the aggregate attempt rate and the aggregate throughput. All of our analysis based on unsaturated condition is the generalized analysis which can be applied to saturated condition as well. We provide the validation of our analysis through ns2 simulations.

To prolong the battery lifetime, it is important to continue designing a better energy efficient mechanism for different mobile technologies. Most of the existing works on the IEEE 802.16e sleep mode operation focus on the decision making before a mobile station switching to sleep mode state. The correlation of the decision is mainly on when and how to sleep based on the traffic demands. After the mobile station is switched to sleep mode, the deactivation of it mainly depends on new incoming traffic regardless of the actual amount. Truly, frequent switching can increase the energy cost on the mobile station, which can significantly reduce the battery lifetime. To minimize the switching frequency, we propose a novel approach to resolve this issue by making a heuristic decision during the listening interval. With this aim, we propose a real-time heuristic algorithm, WAKSLP_DECISION, to accommodate our target. Three main decision criteria are analyzed and designed, namely the probability of buffer overflow, expected delay violation, and battery lifetime expiry, to achieve our goal. We verify the energy consumption performance with simulation experiments to validate our proposed scheme. The result shows that our scheme performs 25% to 30% better compared with the original standard in terms of energy consumption. We believe this algorithm is practical and implementable without changing the original standard, which can contribute both in the research community and industrial development.

There is a growing interest in using wireless sensor networks for security monitoring in the underground coal mines. In such applications, the sensor nodes are deployed to detect interested events, e.g., the density of certain gas at some locations is higher than the predefined threshold. These events are then reported to the base station outside. Using conventional multi-hop routing for data reporting, however, will result in imbalance of energy consumption among the sensors. Even worse, the unfriendly communication condition underground makes the multi-hop data transmission challenging, if not impossible. In this paper, we thus propose to leverage tramcars as mobile sinks to assist event collection and delivery. We further observe that the sensor readings have spatial and temporal correlation. More precisely, the same event may be observed by multiple neighboring sensor nodes and/or at different time. Obviously, it can be more energy-efficient if the data are selectively reported. As such, we first provide a general, yet realistic definition on the events. We then transform the event collection problem into a set coverage problem; and our objective is to maximize the system lifetime with the coverage rate of events guaranteed. We show that the problem is NP-hard even when all the events are known in advance. We present an online scheme which leverages the spatial-temporal correlation of the events to balance the communication energy of the static sensor nodes. We prove that the expected event coverage rate can be guaranteed in theory. Through extensive simulation, we demonstrate that our scheme can significantly extend system lifetime, as compared to a stochastic collection scheme.

Dynamic spectrum access has been a subject of extensive research activity in recent years. The increasing volume of literature calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper we present a detailed spectrum measurement study, with data collected in the 20MHz to 3GHz spectrum band and at four locations concurrently in South China. We examine the first and second order statistics of the collected data, including channel occupancy/vacancy statistics, channel utilization within each individual wireless service, and the temporal, spectral, and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2-dimensional frequent pattern mining algorithm that can accurately predict channel availability based on past observations.

Spectrum is a critical yet scarce resource and it has been shown that dynamic spectrum access can significantly improve spectrum utilization. To achieve this, it is important to incentivize the primary license holders to open up their under-utilized spectrum for sharing. In this paper we present a secondary spectrum market where a primary license holder can sell access to its unused or under-used spectrum resources in the form of certain fine-grained spectrum-space-time unit. Secondary wireless service providers can purchase such contracts to deploy new service, enhance their existing service, or deploy ad hoc service to meet flash crowds demand. Within the context of this market, we investigate how to use auction mechanisms to allocate and price spectrum resources so that the primary license holder's revenue is maximized. We begin by classifying a number of alternative auction formats in terms of spectrum demand. We then study a specific auction format where secondary wireless service providers have demands for fixed locations (cells). We propose an optimal auction based on the concept of virtual valuation. Assuming the knowledge of valuation distributions, the optimal auction uses the Vickrey-Clarke-Groves (VCG) mechanism to maximize the expected revenue while enforcing truthfulness. To reduce the computational complexity, we further design a truthful suboptimal auction with polynomial time complexity. It uses a monotone allocation and critical value payment to enforce truthfulness. Simulation results show that this suboptimal auction can generate stable expected revenue.

With the development of cognitive radio technologies, dynamic spectrum access becomes a promising approach to increase the efficiency of spectrum utilization and solve spectrum scarcity problem. Under dynamic spectrum access, unlicensed wireless users (secondary users) can dynamically access the licensed bands from legacy spectrum holders (primary users) on an opportunistic basis. While most primary users in existing works assume secondary transmissions as negative interference and don't actively involve them into the primary transmission, in this paper, motivated by the idea of cooperative communication, we propose a cooperative cognitive radio framework, where primary users, aware of the existence of secondary users, may select some of them to be the cooperative relay, and in return lease portion of the channel access time to them for their own data transmission. Secondary users cooperating with primary transmissions have the right to decide their payment made for primary user in order to achieve a proportional access time to the wireless media. Both primary and secondary users target at maximizing their utilities in terms of their transmission rate and revenue/payment. This model is formulated as a Stackelberg game and a unique Nash Equilibrium point is achieved in analytical format. Based on the analysis we discuss the condition under which cooperation will increase the performance of the whole system. Both analytical result and numerical result show that the cooperative cognitive radio framework is a promising framework under which the utility of both primary and secondary system are maximized.

Delay tolerant networks (DTNs) use a store-carry-forward communication model relying on the mobility of nodes because a persistent end-to-end path from source to destination generally does not exist. Epidemic routing is a typical protocol in which data can be replicated along multiple opportunistic paths. With the advent of network coding, it is intuitive that data can not only be replicated, but also coded, when the transmission opportunity arises. In this paper we have implemented the epidemic routing with network coding based on the qualnet platform to validate this theory. What's more, to obtain better performance we introduced adaptive scheduling mechanism. We analytically show that with network coding, the performances of the network including the delivery delay, throughput and the delivery rate have been improved, reflecting many realistic scenarios. And through the simulation result, the correctness of our analysis is confirmed. To sum up, our research will generate a significant impact on DTN, as one focus of future network.

A medium access control (MAC) protocol to address the so-called unreachability problem in mobile ad hoc networks is proposed. Stations maintain double hop neighborhood graphs and exchange designated eMAC tables to share their knowledge about their neighborhood topology. An adaptive table broadcasting technique to facilitate topology information dissemination in mobile ad hoc networks is also proposed. Performance of the proposed schemes is evaluated and compared with earlier schemes through simulations. Our results show performance enhancement due to better handling of unreachability, possible heterogeneous power distributions among contending stations, and mobility issues.

Reputation based and credit-exchange based approaches have been studied extensively to enforce cooperation among non-cooperative nodes in wireless ad hoc networks. Most of the existing solutions are fundamentally context-based ones, which need to accurately identify selfish behaviors, securely maintain the context, and appropriately punish the selfish nodes. These requirements are extremely difficult to satisfy if not impossible. From a completely new angle, this paper proposes a context-free protocol, COFFEE, to enforce cooperation among selfish nodes, which has the ability to transmit a packet over the path successfully without the dependency on the information of other packets' transmission. Considering that every node in the network is rational, during the packet forwarding stage, if the intermediate nodes can not clearly tell whether the packet is destined to them or not, they can not simply drop the packet. Thus, in our proposed COFFEE protocol, through introducing several techniques, for any packet received by any node, the node thinks the packet could be destined to it and forwards the packet to find out the answer. Detailed analysis and performance evaluation have been conducted to demonstrate the effectiveness of the proposed protocol.

In the paper, we design a navigation system based on sensor network to guide a robot to walk out of event region. The navigation system does not require any exit or locations.

Conventional linear multicast can be constructed on any acyclic network by increasing the order of the finite field to a sufficiently large amount over which the multicast is defined. In this paper, we first discuss the reciprocal theorem of the conventional linear multicast and design a linear multicast on any give acyclic network with constant finite field by extending the multicast dimension and relaxing the constraint on the information storage. In particular, we propose the binary linear multicast network coding and the linear multicast with binary coefficients. With the proposed method, the computation complexity for network coding at the intermediate nodes can be significantly reduced; therefore cheap network nodes can be deployed in a large scale due to their low cost for wireless communications. In addition, some applications of the proposed binary linear multicast network coding in wireless communication networks are illustrated and validated.

The 14 papers in this special issue are divided into four categories: theoretical frameworks and schemes; integration of media processing and network protocols; heterogeneity; and quality evaluation.

Peer-to-Peer (P2P) networks have emerged as one of the most promising approaches to improve the scalability of Video-on-Demand (VoD) service over Internet. However, despite a number of architectures and streaming protocols have been proposed in past years, there is few work to study the practical performance of P2P-based VoD service especially in consideration of real user behavior which actually has significant impact on system scalability. Therefore, in this paper, we first characterize the user behavior by analyzing a large amount of real traces from a popular VoD system supported by the biggest television station in China, cctv.com. Then we ex-amine the practical scalability of P2P-based VoD service through extensive trace-driven simula-tion under a general system framework. The results show that P2P networks scale well in provid-ing VoD service under real user behavior by obtaining a considerable good cache hit ratio. Moreover, it is observed that adopting hard cache at client side help achieves better system scal-ability than that with soft cache. We also identify the impact of various aspects of user behavior upon system scalability through detailed simulation. We believe that our study will shine insight-ful light on the understanding of practical scalability of P2P-based VoD service and be helpful to future system design and optimization.

During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-to-peer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e. gossip-style overlay construction), and exchanges streaming data with the neighbors (i.e. data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints.

Ultra-wideband (UWB) communication is a promising enabling technology for future broadband wireless services. A simple, scalable, distributed, efficient medium access control (MAC) protocol is of critical importance to utilize the large bandwidth UWB channels and enable numerous new applications and services cost-effectively. In this paper, by investigating the characteristics of UWB communications, we propose a Distributed, EXclusive region (DEX) based MAC protocol. The proposed DEX protocol capitalizes on the spatial multiplexing gain of UWB networks by reserving exclusive regions (ER) surrounding the sender and receiver for data and acknowledgment (ACK) transmissions, so that users can efficiently and fairly share network resources in a distributed and asynchronous manner. We further quantify the network performance bounds and derive the optimal ER size to maximize the expected network transport throughput for a dense, multi-hop UWB network. Extensive simulation results demonstrate the efficiency and effectiveness of the DEX protocol. This work explores how to effectively utilize the wireless spatial capacity of distributed, multi-hop wireless networks by optimizing protocol parameters, instead of depending on more complicated control messages.

Taking advantage of the independent fading channel conditions among multiple wireless users, opportunistic transmissions schedule the user with the instantaneously best condition and thus increase the spectrum utilization efficiency of wireless networks. So far, most proposed opportunistic scheduling policies for ad hoc networks exploit local multiuser diversity, i.e., each transmitter selects its best receiver independently. However, due to co-channel interference, the decisions of neighboring transmitters are highly correlated. Furthermore, the neighboring links without a common sender also experience independent channel fading. Taking the contention relationship and the channel diversity among links into account, we extend the concept of multi-user diversity to a more generalized one, by which a set of senders cooperatively schedule the instantaneously and globally best out-going links, thus the spatial diversity of the channel variation can be further exploited. In this paper, we formulate the opportunistic scheduling problem with fairness requirements into an optimization problem and present its optimal solution, i.e., the optimal scheduling policy. We also propose GOS, a distributed Graph theory based and Opportunistic Scheduling algorithm, which modifies IEEE 802.11 protocol to implement the optimal scheduling policy. Theoretical analysis and simulation results both verify that our implementation achieves higher network throughput and provides better fairness support than the existing algorithms.

In this paper a Markov chain based analytical model is proposed to evaluate the slotted CSMA/CA algorithm specified in the MAC layer of IEEE 802.15.4 standard. The analytical model consists of two two-dimensional Markov chains, used to model the state transition of an 802.15.4 device, during the periods of a transmission and between two consecutive frame transmissions, respectively. By introducing the two Markov chains a small number of Markov states are required and the scalability of the analytical model is improved. The analytical model is used to investigate the impact of the CSMA/CA parameters, the number of contending devices, and the data frame size on the network performance in terms of throughput and energy efficiency. It is shown by simulations that the proposed analytical model can accurately predict the performance of slotted CSMA/CA algorithm for uplink, downlink and bi-direction traffic, with both acknowledgement and non-acknowledgement modes.

In this paper we propose a cooperative-aware medium access control (MAC) protocol in the newly emerged multiple access point (MAP) WLAN system, where each user can associate with multiple APs. Leveraging the multiple association feature, the spectrum efficient virtual-antenna array (VA) cooperative strategy can be utilized. However, the APs selected for VA transmission cannot serve packet transmission individually, which leads to spatial reuse inefficiency. To balance the tradeoff between spatial reuse efficiency and cooperative gain, an interference model that describes interference among cooperative transmissions should be first established. In this paper, a virtual link model is proposed, where each virtual link is composed of one end user, one combination of VA APs and one data rate to represent one cooperative transmission. Based on this model, a multi-cell virtual link scheduling problem is formulated to achieve optimal system performance. Combined with local clique searching procedure and physical carrier sensing adaptation scheme, the dual decomposition of the scheduling problem can distributively achieve the near optimal performance. Finally a VA-based cooperative MAC (V-MAC) protocol is proposed to implement the cooperative scheduling scheme. Simulation result shows V-MAC significantly improves the system throughput meanwhile guarantees the system fairness.

With the increasing development of IEEE 802.11 based Wireless Local Area Network (WLAN) devices, large-scale multi-cell WLANs with a high density of users and access points (APs) have emerged widely in various hotspots. Providing resilient data transmission has been a primary challenge for scaling the WLANs because the high density of users and APs results in too many collisions. In this paper, we analyze and point out the defect of the single association mechanism defined in IEEE 802.11 on transmission reliability from a network perspective. Then, we propose a "multi-AP" architecture with which a MAC layer device called an AP Controller (AC) is employed to enable each user to associate and cooperate with multiple APs. In this way, the users can benefit from the diversity effect of multipaths with independent collisions and transmission errors. This paper concentrates on the theoretical analysis of performance comparison between the proposed "Multi-AP" architecture and that in IEEE 802.11. Extensive simulation results show that the proposed "multi-AP" architecture can obtain much better performance in terms of the throughput per user and the total throughput, and the performance gain is position dependent. Moreover, the unfairness issue in traditional WLANs due to capture effect can be alleviated properly in the "multi-AP" framework.

Mobile sensor has enabled many new applications in wireless sensor networks where coverage is one of the most important problems to support those applications. Previous research works of coverage issue mainly focus on the static sensor network, which can not handle the significant challenge brought by the node mobility. In this paper, we consider the coverage problem in mobile sensor networks under a very general framework, in which all the sensors are moving all the time with any specified movement model. We propose a scheme that can provide a Real-time Coverage-Map According to Probability (RC- MAP) of the mobile sensor networks. By introducing probability into coverage, RC-MAP provides three different types of coverage map in real-time, including probability to be covered, expected number of sensors to cover, and expected waiting time before being covered. Extensive simulation results demonstrate the high accuracy and effectiveness of the proposed scheme.

Significant technology progress has been witnessed in the research area of dynamic spectrum access. However, the success of dynamic spectrum access will not be possible without the evolution of an spectrum and service market which is both stable and efficient. In this paper, we investigate the dynamic access spectrum from the economic point of view. We study three-layer spectrum market with spectrum holder, wireless service provider and end users. In a duopoly situation, two wireless service providers participate in bandwidth competition to purchase spectrum and price competition to attract end users, with the aim of maximizing their own profit. We formulate the wireless service providers' competition as a two-stage game. Under general assumptions about the pricing and demand functions, a unique equilibrium is identified as the outcome of the game, which shows the stability of the market. We further evaluate the market efficiency in a special case of symmetric wireless service providers and affine pricing and demand functions. The result shows the efficiency of the equilibrium is of reasonable level even with non-cooperative wireless service providers.

To improve the performance of spectrum sensing, cooperation among Cognitive Radios (CRs) has been proposed recently as an effective solution. Most existing works require either time synchronization or extra infrastructure support, which are not always practical. This paper proposes an asynchronous spectrum sensing framework which is of high accuracy, short response time and ease to implement. Within such a framework, each node uses our proposed Sliding-Window algorithm to analyze the spectrum status with the sensing results received from its neighbors. This algorithm maintains a minimal and sufficient number of sensing results using Sliding-Window method, and applies Likelihood Ratio Test (LRT) on them to make a decision. This algorithm is evaluated both in theory and by simulations. The results proved that our proposed cooperative sensing solution can show good performance in various situations.

Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either "connected" or "disconnected". These approaches are called connectivity-based topology control. In real environments however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Towards this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NPhard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(jEj) where E is the link set of the original topology, and the space requirement for individual nodes is O(d) where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 BerkeleyMica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to 6 times.

In a network supporting cooperative communication, the sender of a transmission is no longer a single node, which causes the concept of a traditional link to be reinvestigated. Thus, the routing scheme basing on the link concept should also be reconsidered to ";truly"; exploit the potential performance gain introduced by cooperative communication. In this paper, we investigate the joint problem of routing selection in network layer and contention avoidance among multiple links in MAC layer for multi-hop wireless networks in a cooperative communication aware network. To the best of our knowledge, it is the first work to investigate the problem of cooperative communication aware routing in multi-source multi-destination multi-hop wireless networks. Several important concepts, including virtual node, virtual link and virtual link based contention graph are introduced. Basing on those concepts, an optimal cooperative routing is achieved and a distributed routing scheme is proposed after some practical approximations. The simulation results show that our scheme reduces the total transmission power comparing with non-cooperative routing and greatly increases the network throughput comparing with single flow cooperative routings.

In wireless sensor networks (WSNs), QoS topology control achieves energy-efficiency by turning off redundant nodes and links, while still satisfying the given QoS requirement. However, existing topology control algorithms assume that links are either connected or disconnected. Recent experiments have shown that, besides the connected and disconnected region, a large percentage of links reside in the transitional region with fluctuating link qualities. In this paper, we propose both centralized and distributed solutions for QoS topology control, where we employ the opportunistic transmission to catch the best transmission opportunities on transitional links. Our simulations demonstrate that opportunistic transmission based approach can significantly improve energy-efficiency in QoS topology control with low communication overhead. A unique contribution of this paper is to consider link quality and apply opportunistic communication in topology control for WSNs.

Dynamic spectrum access can significantly improve the spectrum utilization. For wireless service providers, the emergence of dynamic spectrum access brings new opportunities and challenges. The flexible spectrum acquisition gives a particular provider the chance to easily adapt its system capacity to fit end users' demand. However, the competition among several providers for both spectrum and end users complicates the situation. In this paper, we propose a general three-layer spectrum market model for the future dynamic spectrum access system, in which the interaction among spectrum holder, wireless service providers and end users are considered. We study a duopoly situation, where two wireless service providers participate in bandwidth competition in spectrum purchasing and price competition to attract end users, with the aim of maximizing their own profit. We believe we are the first one to explicitly study the relation of these two competitions in dynamic spectrum market. We formulate the wireless service providers' competition as a non-cooperative two-stage game. We first analyze the static game when full information is available for providers. Under general assumptions about the price and demand functions, a unique pure Nash equilibrium is identified as the outcome of the game, which shows the stability of the market. We further evaluate the market efficiency of the equilibrium in a symmetric case, and show that the gap with the social optimal is bounded within a small constant ratio. When the market information is limited, we provide myopically optimal adjustment algorithms for the providers. With such strategies, short term price updating converges to the Nash equilibrium of the given subgame, while long term bandwidth updating converges to a point close to the Nash equilibrium of the full game.

In ad hoc networks, wireless nodes rely on each other to transmit data over multi-hops by forwarding packets. A selfish node may decide not to forward packets for other nodes to save its own resource but still use the network to send and receive data. Such a selfish behavior can degrade network perf

Fast proliferation of IEEE 802.11 wireless devices has led to the emergence of High-Density (HD) Wireless Local Area Networks (WLANs), where it is challenging to improve the throughput because each device has to share channel with all the other devices within its carrier sensing range. Although the existing adaptive Physical Carrier Sensing (PCS) techniques can improve the throughput, they result in high frame loss rate. In this paper, we investigate a Multi-AP (MAP) architecture, in which each user can associate with multiple APs according to the network topology and traffic distribution, for adaptive PCS based HD-WLANs. One of important features of the MAP architecture is that it can obtain Multi-AP diversity in both uplink and downlink. In uplink (from users to APs) the frame loss rate can be decreased by combining the reception of all associated APs, and in downlink the throughput can be improved significantly by dynamically selecting one of associated APs for transmissions according to the channel fading and traffic distribution. We first study the uplink and downlink performance of the MAP theoretically, and then propose an AP association algorithm for deciding which APs to associate with, an AP selection algorithm for dynamically selecting an AP for downlink transmissions, and an ACK management solution for avoiding ACK collisions. We build a testbed based on Intel StarEast platform to make real experiments for performance evaluation. In a typical experiment scenario, compared to the scheme with the adaptive PCS only, up to 30% throughput gain can be observed in uplink, and nearly 100% throughput gain can be found in downlink.

Radio spectrum resource is of fundamental importance for wireless communication. Recent reports show that most available spectrum has been allocated. While some of the spectrum bands (e.g., unlicensed band, GSM band) have seen increasingly crowded usage, most of the other spectrum resources are underutilized. This drives the emergence of open spectrum and dynamic spectrum access concepts, which allow unlicensed users equipped with cognitive radios to opportunistically access the spectrum not used by primary users. Cognitive radio has many advanced features, such as agilely sensing the existence of primary users and utilizing multiple spectrum bands simultaneously. However, in practice such capabilities are constrained by hardware cost. In this paper, we discuss how to conduct efficient spectrum management in ad hoc cognitive radio networks while taking the hardware constraints (e.g., single radio, partial spectrum sensing and spectrum aggregation limit) into consideration. A hardware-constrained cognitive MAC, HCMAC, is proposed to conduct efficient spectrum sensing and spectrum access decision. We identify the issue of optimal spectrum sensing decision for a single secondary transmission pair, and formulate it as an optimal stopping problem. A decentralized MAC protocol is then proposed for the ad hoc cognitive radio networks. Simulation results are presented to demonstrate the effectiveness of our proposed protocol.

In this paper, we study the theoretical problem of the end-to-end rate assignment for multi-hop wireless networks. Specifically, we consider the problem of joint congestion control, random access and power control design with multi-hop transmissions and interference-limited link rates. In order to address both the end-to-end throughput maximization and energy efficiency, we formulate this problem into a cross-layer design problem under a realistic interference-based communication model, which captures the attainable link capacity in practice. There are primarily three challenges in this design: 1) how to formulate the cross-layer design; 2) how to solve the non- convex and non-separable problem efficiently; more importantly 3) under a reasonably complexity, how to design a distributed algorithm that can realize this formulation while maintaining the architectural modularity among different layers. First, we propose a novel method that can convert a non- convex and non-separable programming into an equivalent convex programming problem. The problem is solved by a dual decomposition technique. We show that the resulting algorithm can be practically realized. We then design a distributed algorithm that jointly considers random access and power control to adapt for the transport layer congestion status. Simulation results confirm that the proposed algorithm can achieve close to the global optimum within reasonable convergence times.

Due to such features as low cost, ease of deployment, increased coverage, and enhanced capacity, multihop wireless networks such as ad hoc networks, mesh networks, and sensor networks that form the network in a self-organized manner without relying on fixed infrastructure is touted as the new frontier of wireless networking. Providing efficient quality of service (QoS) support is essential for such networks, as they need to deliver real-time services like video, audio, and voice over IP besides the traditional data service. Various solutions have been proposed to provide soft QoS over multihop wireless networks from different layers in the network protocol stack. However, the layered concept was primarily created for wired networks, and multihop wireless networks oppose strict layered design because of their dynamic nature, infrastructureless architecture, and time-varying unstable links and topology. The concept of cross-layer design is based on architecture where different layers can exchange information in order to improve the overall network performance. Promising results achieved by cross-layer optimizations initiated significant research activity in this area. This paper aims to review the present study on the cross-layer paradigm for QoS support in multihop wireless networks. Several examples of evolutionary and revolutionary cross-layer approaches are presented in detail. Realizing the new trends for wireless networking, such as cooperative communication and networking, opportunistic transmission, real system performance evaluation, etc., several open issues related to cross-layer design for QoS support over multihop wireless networks are also discussed in the paper.

To effectively address the explosive growth of multimedia applications over the Internet, a large-scale media streaming system has to fully take into account the issues of security, quality of service (QoS), scalability, and heterogeneity. However, current streaming solutions do not address all these challenges simultaneously. To address this limitation, this paper proposes a secure and high-performance streaming system called TrustStream, which combines the best features of scalable coding, content distribution network (CDN) and peer-to-peer (P2P) networks to achieve unprecedented security, scalability, heterogeneity, and certain QoS simultaneously under a unified architecture. In this architecture, raw video is encoded into two layers, namely, the base layer, which contains the most critical media content and is transmitted through a CDN-featured single-source multi-receiver (S-M) P2P network to guarantee a minimal level of quality, and the enhancement layer, which is transmitted in a pure multisource multi-receiver (M-M) P2P framework to achieve maximum scalability and bandwidth utilization. Heterogeneity is therefore addressed by delivering only the layers that a receiver is able to manage. Security is provided by combining our key distribution mechanism and key-embedding scheme under our proposed S-M P2P topology. We have implemented TrustStream system over the Internet. Deployed by ChinaCache, the largest CDN provider in China, TrustStream has broadcasted several popular live video programs over the Internet. The experimental results demonstrate the advantages and effectiveness of our architecture and system.

Wireless sensor networks have been widely used for surveillance in harsh environments. In many such applications, the environmental data are continuously sensed, and data collection by a server is only performed occasionally. Hence, the sensor nodes have to temporarily store the data, and provide easy and on-hand access for the most updated data when the server approaches. Given the expensive server-to-sensor communications, the large amount of sensors and the limited storage space at each tiny sensor, continuous data collection becomes a challenging problem. In this article, we present partial network coding (PNC) as a generic tool for these applications. PNC generalizes the existing network coding (NC) paradigm, an elegant solution for ubiquitous data distribution and collection. Yet PNC allows efficient storage replacement for continuous data, which is a deficiency of the conventional NC. We prove that the performance of PNC is quite close to NC, except for a sub-linear overhead on storage and communications. We then address a set of practical concerns toward PNC-based continuous data collection in sensor networks. Its feasibility and superiority are further demonstrated through simulation results.

Wireless sensor networks have been widely used in many surveillance applications. Due to the importance of sensor nodes in such applications, certain level of protection need to be provided to them. We study the self protection problem for static wireless sensor networks in this paper. Self protection problem focuses on using sensor nodes to provide protection to themselves instead of the target objects or certain target area, so that the sensor nodes can resist the attacks targeting on them directly. A wireless sensor network is p-self-protected, if at any moment, for any wireless sensor (active or non-active), there are at least p active sensors that can monitor it. The problem finding minimum p-self-protection is NP-complete, and no efficient self protection algorithms have been proposed. In this paper, we provide efficient centralized and distributed algorithms with constant approximation ratio for minimum p-self-protection problem in sensor networks with either homogeneous or heterogeneous sensing radius. In addition, we design efficient distributed algorithms to not only achieve p-self-protection but also maintain the connectivity of all active sensors. Our simulation confirms the performances of proposed algorithms.

In this letter, a network-coded cooperation scheme with dynamic coding mechanism (DC-NCC) is proposed. In DC-NCC, the relay dynamically adapts forming the network-coded data based on the observed instantaneous source-to-relay channel quality, and then forwards the network-coded data towards corresponding destinations. Under the assumption (denoted as A) that each destination can reliably overhear the data from other sources, the diversity-multiplexing tradeoff of DC-NCC is proved to outperform that of conventional cooperation (CC), This verifies that DC-NCC outperforms CC in bandwidth efficiency. Moreover, DC-NCC offers reduced system outage probability and single-pair outage probability compared with CC, and achieves the same full diversity order as CC at high signal-to-noise ratio (SNR). Numerical results also show that with dynamic coding, DC-NCC avoids unnecessary error propagation which can be caused by coding those erroneous data into network-coded data. Finally the performance of DC-NCC is discussed in case the assumptionAis removed. For a wireless network composed ofNsource-destination (s-d) pairs and a single relay node, although there is a certain outage probability increase for eachs-dpair, DC-NCC still achieves a diversity-multiplexing tradeoff superior to CC.

In Wireless Local Area Networks (WLANs), traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals completely, brings too many exposed terminals and degrades the throughput seriously. Some existing work has proven that an aggressive PCS, which turns up the PCS threshold to allow the existence of hidden terminals, can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to compute the optimal PCS threshold. To address this issue, in this paper we develop an analytical model, which can be used to compute the optimal PCS threshold and investigate the impact of the aggressive PCS on the Quality-of-Service (QoS) in terms of the packet loss rate. Then, we propose a QoS-aware aggressive PCS tuning algorithm, with which users can adapt the PCS threshold to the varying network conditions and the QoS requirement. Extensive simulation results show that the proposed algorithm obtains significant throughput gain compared to the traditional PCS and bounds the packet loss rate below the QoS requirement. The unfairness issue brought by the aggressive PCS is also discussed and evaluated with experimental results at the end of the paper.

Open spectrum systems allow unlicensed secondary users equipped with cognitive radio to opportunistically access the spectrum underutilized by primary users. Cognitive radio has many advanced features, such as agilely sensing the signal of primary users and utilizing multiple spectrum bands simultaneously, etc. However, the capability of practical cognitive radios is constrained by hardware cost, resulting in partial spectrum sensing and spectrum aggregation limit. In this paper, we take such constraints into consideration and investigate efficient spectrum management in ad hoc cognitive networks with single cognitive radio. A hardware-constrained cognitive MAC, HC-MAC, is proposed to conduct accurate spectrum sensing and spectrum access decision. We identify the optimal spectrum sensing decision for a single secondary transmission pair, and formulate it as an optimal stopping problem. A decentralized MAC protocol is then proposed for the ad hoc cognitive network. Simulation results are given to demonstrate the effectiveness of our proposed protocol.

Many sensor network applications exploit the mobility of sensor nodes and the location-awareness plays an important role in these applications. However, it is too expensive to equip special hardware in each sensor node for localization, thus a distributed range-free localization scheme in mobile sensor network is highly desired. Several such localization techniques have been developed but few of them consider leveraging the feature of node mobility. Leveraging node mobility, in this paper, both static constrain set by transmission range and velocity constrain set by node movement are introduced. Based on these two types of constrain, we propose a range-free mobile inequality localization (MIL) algorithm, which uses ring inequalities to restrict and estimate the possible location in numerical method. Our approach is distributed, quickly re-localizable and works well when all sensors are moving uncontrollably. We analyze the theoretical bound for the accuracy of the position estimation and the comprehensive simulations demonstrate that our algorithm performs much better than the existing works.

Wireless sensor networks have been widely used in many surveillance applications. Due to the importance of sensor nodes in such applications, certain level of protection needs to be provided to them. We study the self protection problem for static wireless sensor networks in this paper. Self protection problem focuses on using sensor nodes to provide protection to themselves instead of the target objects or certain target area, so that the sensor nodes can resist the attacks targeting on them directly. A wireless sensor network is p-self-protected, if for any wireless sensor there are at least p active sensors that can monitor it. The problem finding minimum p-self-protection is NP-complete and no efficient self protection algorithms have been proposed. In this paper, we provide efficient centralized and distributed algorithms with constant approximation ratio for minimum p-self-protection problem. In addition, we design efficient distributed algorithms to not only achieve p-self-protection but also maintain the connectivity of all active sensors. Our simulation confirms the performances of proposed algorithms.

In many sensor network applications, manual or well-controlled node deployment is not practical. Random deployments, e.g., from the air, often result in unevenly distributed nodes and hence insufficient coverage. In this paper, we present a novel mobility-assisted network for field coverage, and suggest that, by introducing only a small set of mobile sensors, the field coverage can be remarkably improved. The main challenges for this mobility-assisted framework are determining the necessary coverage contribution of the mobile sensors and achieving such contribution. We develop an optimal strategy for separating the contribution from static sensors and mobile sensors. We then proposed a random walk based mobility model for the mobile sensors to achieve their necessary contribution. We demonstrate through experiment that a small set of mobile sensors can effectively solve the unbalance of the sensor distribution and significantly improve the system lifetime and coverage quality.

Aggressive physical carrier sensing (PCS), which improves the spatial reuse efficiency by shrinking the PCS range, is a promising technique to scale high density wireless local area networks (WLANs). Although the aggressive PCS can improve the throughput in MAC layer due to enabling more simultaneous transmissions, it results in serious frame loss rate caused by hidden terminal problems. TCP congestion control is loss sensitive, and thus high frame loss rate degrades TCP throughput seriously. In this paper, we propose an analytical model to investigate the TCP performance with aggressive PCS, and based on the proposed model the TCP throughput is expressed as a function with respect to the PCS threshold. Numerical results show that MAC throughput efficient PCS starves the TCP throughput, and thus we should not adjust the PCS threshold to maximize the MAC capacity in TCP based applications. In addition, extensive experimental results based on StarEast testbed are given to validate the analysis.

In this paper, we propose a novel protocol called VC-MAC that utilizes the concept of cooperative communication tailored for vehicular networks, especially for gateway downloading scenarios. VC-MAC leverages the broadcast nature of the wireless medium to maximize the system throughput. The spatial and user diversity are exploited by the concurrent cooperative relaying to overcome the unreliability of the wireless channel in vehicular networks. We theoretically analyze the selection of optimal relay set using weighted independent set (WIS) model, and then design a back-off mechanism to select the concurrent relays in a distributed manner. Extensive simulations in ns-2 are carried out to demonstrate that compared with existing strategies, VC-MAC effectively enhances cooperative information downloading and significantly increases the system throughput.

We study the link layer scheduling problem in wireless ad hoc networks. In such a network, the communication links compete for the scarce and time-varying wireless channels. Recently, opportunistic scheduling that exploits variation of channel conditions at each link has drawn great attention to improve system performance. Taking advantage of the multiuser diversity and being aware of the potential contention among neighboring transmissions, we formulate the opportunistic scheduling problems with QoS requirements and present the optimal scheduling policies for both single- and multi- hop ad hoc networks. We also proposed COS, a distributed Cooperative and Opportunistic Scheduling algorithm, which realizes the optimal scheduling policies by introducing the cooperation among neighboring transmitters. Simulation results indicate that our implementation achieves higher network throughput and provides better QoS support than existing solutions.

Limited spectrum resources, inefficient spectrum usage and increasing wireless communication necessitates a paradigm shift from the current fixed spectrum management policy to a more flexible one. With the Federal Communications Commission's (FCC) spectrum policy reform, secondary spectrum sharing has become a viable and promising option. In this paper, we study power control for spectrum sharing among secondary users with interference temperature limit (ITL) constraints at measurement points. Each secondary user will adjust its own transmission power to make sure the overall interference at the measurement points does not exceed the required ITL. Under the assumption that each secondary user is selfish and rational while there is only limited coordination between primary users and secondary users, we study the distributed power control scheme for secondary users. In our proposed system model, secondary users generating the highest interference at a measurement point will back off their transmissions if the ITL is exceeded. A non-cooperative power control game among secondary users is proposed to maximize each user's utility. We identify the Nash equilibrium of the proposed game and analyze its property. Simulations are conducted to demonstrate that the proposed solution can achieve a satisfactory performance in terms of the total transmitting rate of all the secondary users.

Network-coded cooperation is a new communication paradigm that exploits spatial diversity by pooling distributed "communication" (relay) and also the distributed "computation" resources (coding) of different nodes in a network so as to increase wireless networks system performance. In this paper, we develop a selective network-coded cooperation (SNCC) scheme suitable for multiple unicast transmission pairs in a single-cell wireless network. The relay node performs network coding on received information from multiple sources before forwarding the network-coded data towards the corresponding destinations, which can decode their desired data from the network-coded data and the data they overhear from the other sources. The performance analysis and evaluation in terms of the single-pair outage probability and diversity-multiplexing tradeoff show that under SNCC any transmission pair achieves a full diversity order of M+1 (M is the number of available relays) while holding a better diversity-multiplexing tradeoff performance than existing cooperative cooperation schemes. Moreover, due to the mechanism that the relay adapts the data on which to perform network coding according to the observed source-to-relay channel quality, SNCC smartly avoids error propagation due to incorporating erroneous data into network-coded data, which is verified by numerical results of the single-pair outage probability.

Cooperative communication is a powerful technology combating fading in wireless medium. In contrast to conventional cooperation (CC) which allows the relay to simply process and forward what it has heard, network-coded cooperation (NCC) endows network coding capability with the relay, i.e., allows the relay to first encode the data it has received from different sources and then forward the network-coded data to corresponding destinations. In this paper we systematically investigate the performance of NCC in terms of diversity-multiplexing tradeoff as well as exact system outage behavior. Under the assumption (denoted as A) that each destination can reliably overhear the data from other sources, we prove the diversity-multiplexing tradeoff of NCC outperforms that of CC. Moreover, we derive the close form of the system outage probability of NCC as a function of signal-to-noise ratio (SNR). The analytical and numerical results show that by requiring less bandwidth cost, NCC offers a similar or even reduced system outage probability compared with CC, and achieves the same full diversity order as CC at high SNR. Finally we discuss the performance of NCC in case the assumption A is removed. For a wireless network composed of N s-d pairs and a single relay, although there is a certain system outage probability increase for NCC, it still provides the same full diversity order of 2 as CC at high SNR, and has a diversity-multiplexing tradeoff superior to CC.

It has been a challenging problem to support VoIP-type delay sensitive applications in an 802.11 wireless LAN, because the standard handoff procedure implemented in many current 802.11 products occurs a delay deem unacceptable to VoIP users. To reduce this delay, we have developed a fast handoff scheme called Proactive Scan. It employs two new techniques. The first is to decouple the time-consuming channel scan from the actual handoff, and to eliminate channel scan delay by doing scan early and interleaving it with ongoing traffic in a non-intrusive way. The second technique is a smart trigger that takes into account both uplink and downlink quality and explicitly addresses the link asymmetry which has yet not been touched in previous work. Through implementation and experimentation study, we have shown that Proactive Scan does provide fast handoff and satisfactory performance to VoIP applications. Further, it is a software-only client-only solution that any mobile device can use in any existing 802.11 networks.

Traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals in wireless networks, causes too many exposed terminals and deteriorates the throughput per user seriously. Some existing work has proven that an aggressive PCS can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to optimize the PCS according to the network conditions. In this paper, we develop an analytical model to study the behaviors of a user in the aggressive PCS scenario, with which the optimal PCS threshold can be derived. Then, to avoid the complicated computation, we present a heuristic algorithm, in which the parameters required for PCS tuning are estimated by the carrier sensing mechanism employed in IEEE 802.11. Simulation results show that the proposed heuristic algorithm can make the PCS threshold approach to the theoretical optimal one. Moreover, the proposed heuristic algorithm can obtain significant throughput gain compared to the traditional PCS threshold setting solutions.

Providing field coverage is a key issue in many sensor network applications. For a field with unevenly distributed static sensors, a quality coverage with acceptable network lifetime is often difficult to achieve. We propose a hybrid network that consists of both static and mobile sensors, and we suggest that it can be a cost-effective solution for held coverage. The main challenges of designing such a hybrid network are, first, determining necessary coverage contributions from each type of sensors; and second, scheduling the sensors to achieve the desired coverage contributions, which includes activation scheduling for static sensors and movement scheduling for mobile sensors. In this paper, we offer an analytical study on the above problems, and the results also lead to a practical system design. Specifically, we present an optimal algorithm for calculating the contributions from different types of sensors, which fully exploits the potentials of the mobile sensors and maximizes the network lifetime. We then present a random walk model for the mobile sensors. The model is distributed with very low control overhead. Its parameters can be fine-tuned to match the moving capability of different mobile sensors and the demands from a broad spectrum of applications. A node collaboration scheme is then introduced to further enhance the system performance. We demonstrate through analysis and simulation that, in our hybrid design, a small set of mobile sensors can effectively address the uneven distribution of the static sensors and significantly improve the coverage quality.

The recent researches in wireless networks prompt the opportunistic transmission that exploiting channel fluctuations to improve the overall system performance. In wireless ad hoc networks, nodes may have packets destined to multiple neighboring nodes. We consider an opportunistic scheduling that takes advantage of time-varying channel among different receivers. Maximizing over system throughput and satisfying QoS requirements for transmission flows are two important objectives that need to be considered. In this paper, we formulate the opportunistic scheduling problem as an optimization problem which maximizes the overall network performance while satisfying QoS constraints of individual flows. We also proposed COS, a distributed cooperative and opportunistic scheduling algorithm, which modifies IEEE 802.11 protocol to implement the optimal scheduling policy by introducing the cooperation among neighboring transmitters. Simulation results indicate that our implementation achieves higher network throughput and provides better QoS support than existing work.

Broadband wireless access technology is developed very fast in recent years. The IEEE 802.16 and 802.11a are two broadband wireless access system standards for metropolitan and local areas respectively. The IEEE 802.11a works in the license-exempt (LE) band, and the IEEE 802.16 is designed such that it can operate in the same LE spectrum too. Although the two systems work in different environments, interference can exist under some scenario. In this paper, the coexistence issues of the two types of networks are investigated and a simple and accurate way for bit-error-ratio (BER) calculation is given. The analysis is also verified by Monte Carlo simulations. Furthermore, in our work, we also show how our research production is used to configure the coexisting IEEE 802.16 and 802.11a systems to mitigate the interference.

This paper proposed an opportunistic network-coded cooperation (ONCC) scheme suitable for multiple unicast transmission pairs in a single-cell wireless network. In contrast to existing work related to network-coded cooperation where the relay always performs network-coding-and-forwarding to help those transmitting terminals, ONCC is opportunistic because the relay decides whether or not to help do cooperation for a source-destination s-d pair based upon limited feedback from the destination. It does not assist a transmission pair unless the feedback indicates failure of the direct transmission between the source and the destination. For a wireless system composed of two s-d pairs and a relay, systematic performance analysis of ONCC was presented in terms of diversity-multiplexing tradeoff and system outage probability. ONCC is proved to be able to achieve an optimal diversity-multiplexing tradeoff which corresponds to the transmit diversity bound in the high-SNR (signal-to-noise ratio) regime. This means that ONCC makes more efficient use of the degrees of freedom of the channel than those protocols where an s-d pair is always be assisted by cooperative communication. Moreover, it shows that under a fixed system energy constraint, ONCC provides a reduced system outage probability while maintaining an increased expected spectral efficiency at the same time compared with incremental relaying protocol, which is similar to ONCC in "opportunistic" property but different from ONCC in that it does not exploit network coding technology.

Space communication protocol standards-transport protocol (SCPS-TP) is a set of TCP extensions aimed at reliable transfer of information between space mission end systems. Some works have been done on throughput performance evaluation of SCPS-TP over LEO- and GEO-satellite channels. In this paper, we present an experimental performance evaluation of SCPS-TP over a simulated cislunar communication link. We intend to see how effective both the window-based and the rate-based transmission control mechanisms of the SCPS-TP cope with a long cislunar link delay, especially when hybridized with a high bit-error-rate (BER), and which option has throughput advantage over the other. The experimental results reveal that the protocols have statistically significant performance difference over an error-prone link with a BER around 10-5at which pure rate control shows superior performance over other three protocols. SCPS-Vegas performs poorly over a long-delayed, error-prone cislunar channel. Quantitatively, the throughput advantage of SCPS-pure rate control is around 3000 bytes/sec over all other three protocols over both symmetrical and asymmetrical channels at BER=10-5. We conclude that pure rate control is very suitable for a long-delayed, error-prone cislunar communication links.

The consultative committee for space data systems (CCSDS) file delivery protocol (CFDP) operates in a store-and-forward model to ensure the efficient transfer of information in a wide variety of mission configurations, from relatively low earth-orbiting spacecrafts to complex arrangements of inter-planetary space links. The performance of CFDP in the deferred NAK mode has been evaluated in a theoretical manner. In this paper, we present an experimental performance evaluation of CFDP in the deferred NAK mode over direct point to point low earth orbit (LEO)-satellite links simulated using a test-bed. We compare CFDP/TCP with the commonly used TCP and space communication protocol standards (SCPS) protocol stacks to see which one is more effective over a LEO-satellite link. The evaluation results show that CFDP outperform other two protocols over lossy and asymmetric channels. We found that CFDP is more tolerant of heavy noise and asymmetric channel rate. The superior performance of CFDP over lossy links is attributed to that data are stored pending transmission opportunity and portions of file can be made available to the user as soon as they arrive and its ability to minimize link traffic because of aggregated selective negative acknowledgements.

Traditional physical carrier sensing (PCS) threshold setting, which aims at eliminating hidden terminals completely in wireless networks, brings too many exposed terminals and degrades the throughput per user seriously. Some existing work has proven that an aggressive PCS threshold can improve the throughput by balancing the tradeoff between the existence of hidden terminals and exposed terminals. However, the aggressive PCS results in high packet loss, which degrades the quality-of-service (QoS) seriously. To address this issue, in this paper we develop a QoS aware aggressive PCS threshold tuning algorithm, in which the PCS threshold is tuned dynamically according to the varying network conditions. One important feature of the proposed algorithm is that it can bound the packet loss in the region defined by QoS requirement. Extensive simulation results show that the proposed algorithm can bound the packet loss in the QoS requirement and at the same time obtain remarkable throughput gain compared to the traditional PCS threshold setting solutions.

We are interested in providing a media-on-demand streaming service to a large population of clients using a peer-to-peer approach. Since the demands of different clients are asynchronous and the contents of clients' buffers are continuously changing, finding partners with expected data and collaborating with them for future content delivery are very important and challenging problems. In this paper, we propose a generic buffer-assisted search (BAS) scheme to improve partner search efficiency by reducing the size of index overlay. We have also developed a novel scheduling algorithm based on deadline-aware network coding (DNC) to fully exploit network resources by dynamically adjusting the coding window size. Extensive simulation results demonstrate that BAS can provide a faster response time with less control cost than the existing search methods, and DNC improves the network capacity utilization and provides high streaming quality under different network conditions.

The 19 papers in this special issue focus on cross-layer optimized wireless multimedia communications. The papers are organized into four sections: quality of service support for wireless networks; system architecture for multimedia over wireless networks; resource allocation in wireless multimedia communications, and multimedia coding and scheduling issues in wireless networks.

One of the distinctive features in a wireless ad hoc network is lack of any central controller or single point of authority, in which each node/link then makes its own decisions independently. Therefore, fully cooperative behaviors, such as cooperation for increasing system capacity, mitigating interference for each other, or honestly revealing private information, might not be directly applied. It has been shown that power control is an efficient approach to achieve quality of service (QoS) requirement in ad hoc networks. However, the existing work has largely relied on cooperation among different nodes/links or a pricing mechanism that often needs a third-party involvement. In this paper, we aim to design a non-cooperative power control algorithm without pricing mechanism for ad hoc networks. We view the interaction among the users' decision for power level as a repeated game. With the theory of stochastic fictitious play (SFP), we propose a reinforcement learning algorithm to schedule each user's power level. There are three distinctive features in our proposed scheme. First, the user's decision at each stage is self-incentive with myopic best response correspondence. Second, the dynamics arising from our proposed algorithm eventually converges to pure Nash equilibrium (NE). Third, our scheme does not need any information exchange or to observe the opponents' private information. Therefore, this proposed algorithm can safely run in a fully selfish environment without any additional pricing and secure mechanism. Simulation study demonstrates the effectiveness of our proposed scheme.

Most of the real deployed peer-to-peer streaming systems adopt pull-based streaming protocol. In this paper, we demonstrate that, besides simplicity and robustness, with proper parameter settings, when the server bandwidth is above several times of the raw streaming rate, which is reasonable for practical live streaming system, simple pull-based P2P streaming protocol is nearly optimal in terms of peer upload capacity utilization and system throughput even without intelligent scheduling and bandwidth measurement. We also indicate that whether this near optimality can be achieved depends on the parameters in pull-based protocol, server bandwidth and group size. Then we present our mathematical analysis to gain deeper insight in this characteristic of pull-based streaming protocol. On the other hand, the optimality of pull-based protocol comes from a cost -tradeoff between control overhead and delay, that is, the protocol has either large control overhead or large delay. To break the tradeoff, we propose a pull-push hybrid protocol. The basic idea is to consider pull-based protocol as a highly efficient bandwidth-aware multicast routing protocol and push down packets along the trees formed by pull-based protocol. Both simulation and real-world experiment show that this protocol is not only even more effective in throughput than pull-based protocol but also has far lower delay and much smaller overhead. And to achieve near optimality in peer capacity utilization without churn, the server bandwidth needed can be further relaxed. Furthermore, the proposed protocol is fully implemented in our deployed GridMedia system and has the record to support over 220,000 users simultaneously online.

In this paper, we present the challenges in supporting multimedia, in particular, VoIP services over multihop wireless networks using commercial IEEE 802.11 MAC DCF hardware, and propose a novel software solution, called Layer 2.5 SoftMAC. Our proposed SoftMAC resides between the IEEE 802.11 MAC layer and the IP layer to coordinate the real-time (RT) multimedia and best-effort (BE) data packet transmission among neighboring nodes in a multihop wireless network. To effectively ensure acceptable VoIP services, channel busy time and collision rate need to be well controlled below appropriate levels. Targeted at this, our SoftMAC architecture employs three key mechanisms: 1) distributed admission control for regulating the load of RT traffic, 2) rate control for minimizing the impact of BT traffic on RT one, and 3) nonpreemptive priority queuing for providing high priority service to VoIP traffic. To evaluate the efficacy of these mechanisms, extensive simulations are conducted using the network simulator NS2. We also implement our proposed SoftMAC as a Windows network driver interlace specification (NDIS) driver and build a multihop wireless network testbed with 32 wireless nodes equipped with IEEE 802.11 a/b/g combo cards. Our evaluation and testing results demonstrate the effectiveness of our proposed software solution. Our proposed collaborative SoftMAC framework can also provide good support for A/V streaming in home networks where the network consists of hybrid WLAN (wireless LAN) and Ethernet

Routing in wireless communication systems such as ad hoc networks remains a challenging problem given the limited wireless bandwidth, users' mobility, and potentially large scale. Recently, a thrust of research has addressed these problems-the on- demand routing, geographical routing, and virtual coordinates. In this paper, we focus on geographical routing that has been shown to achieve good scalability without flooding; however, this usually requires the availability of location information and can suffer from poor routing performance and severe dead end problems, especially in sparse networks. Specifically, we propose a new Hop ID routing scheme, which is a virtual coordinate-based routing protocol and does not require any location information. This achieves excellent routing performance comparable with that obtained by the shortest path routing schemes. In addition, we design efficient algorithms for setting up the system and adapt to the node mobility quickly and can effectively route out of dead ends. Extensive analysis and simulation show that the Hop ID-based routing achieves efficient routing for mobile ad hoc networks with various density, irregular topologies, and obstacles.

Wireless sensor networks have recently been suggested for many surveillance applications, such as object monitoring, path protection, or area coverage. Since the sensors themselves are important and critical objects in the network, a natural question is whether they need certain level of protection, so as to resist the attacks targeting on them directly. If this is necessary, then who should provide this protection, and how it can be done? We refer to the above problem as self-protection , as we believe the sensors themselves are the best (and often the only) candidates to provide such protection. In this article, we for the first time present a formal study on the self-protection problems in wireless sensor networks. We show that, if we simply focus on enhancing the quality of field or object covering, the sensors might not necessarily be self-protected, which in turn makes the system extremely vulnerable. We then investigate different forms of self-protections, and show that the problems are generally NP-complete. We develop efficient approximation algorithms for centrally controlled sensors. We further extend the algorithms to fully distributed implementation, and introduce a smart sleep-scheduling algorithm that minimizes the energy consumption.

Sensor networks have emerged as a promising technology with various applications, where power efficiency is one of the critical requirements. The recent IEEE 802.15.4 standard offers a promising platform for wireless sensor networks. Since each node can act as a coordinator or a device in the IEEE 802.15.4 standard, 802.15.4-based sensor networks have various possible network topologies. To reduce power consumption, in this paper, we try to construct network topologies with a small number of coordinators while still maintaining network connectivity. By reducing the number of coordinators, the average duty cycle is reduced and the battery life is prolonged. Three topology control algorithms are proposed in this paper. Self-pruning (SP) is the simplest one with O(1) running time and provides the shortest path to the sink node. Ordinal pruning (OP) can significantly improve SP in terms of power saving with O(n) running time. Layered pruning (LP) is a trade off between the first two pruning algorithms with O(radicn) running time and has a slightly higher power consumption than OP. Furthermore, all three algorithms are independent of the physical radio propagation characteristics. Extensive simulations have been performed to verify the effectiveness of the proposed topology control schemes

The IEEE 802.16 protocol for wireless broadband access (wirelessMAN) has been standardized recently. In order to improve network coverage and scalability, mesh mode is supported in 802.16. In the mesh mode, all nodes are organized in an ad hoc fashion and use a pseudo-random function to compete for their transmission opportunities based on the scheduling information in the two-hop neighborhood. In this paper, we develop a stochastic model for the distributed mesh mode scheduler. With this model, we analyze the scheduler performance under various conditions, and the analytical results match very well with the ns-2 simulation results. The analytical model developed in this paper is instrumental in optimizing the IEEE 802.16 mesh network performance. To the best of our knowledge, this work is the first one theoretically investigating the IEEE 802.16 mesh mode scheduling performance

QoS provisioning in multi-hop IEEE 802.11 networks is very challenging due to the interference nature of wireless medium and the contention-based behavior among neighboring nodes. In such networks, one of the key questions for QoS support is: given a specific topology and traffic condition, how much bandwidth can be utilized along a path in the network without violating QoS demand of existing traffic? Considering that in general QoS-sensitive traffic has the well-controlled sending rate, one key observation is that the network unsaturated condition should be considered. Another observation is that, not only the interaction between the new traffic and the existing ones that can be sensed (by the new one), but also the interaction between the new traffic and the traffic that is hidden but can have influence upon the new one should be studied. Based upon the above observations, we propose an analytical model for multi-hop IEEE 802.11 networks to calculate how much bandwidth can be utilized along a path without violating the QoS requirements of existing traffic. A notion, "free channel time", which is the time allowed for a wireless link to transmit data, is introduced to analyze the path capacity. Simulation results demonstrate that our proposed analytical model can accurately predict the path capacity under various network conditions without breaking QoS demands of all existing traffic

Abstract There is much research activity in the emerging area of delay/disruption tolerant networks (DTN) in the past few years. DTNs pose many new challenges, and protocols designed for wired Internet or wireless mobilead hocnetworks do not work properly in DTNs. Routing is one of the key components in DTNs and the message ferry (MF) approach has been drawn a lot of attention to support routing in DTNs. Besides the underlying routing protocols, issues in transport layer are also very important and need to be addressed for DTNs. Recently, many new routing protocols, including unicast using the MF approach, inter‐region routing and multicast, have appeared in the literature. There are also a few works addressing issues in the transport layer. This paper provides an overview on recent developments including message ferrying, multicast support, inter‐region routing, and transport layer issues. It also points out some open research issues and intends to motivate new research and development in this area. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we investigate the coexistence issue of two type of ultra-wideband (UWB) systems under IEEE 802.15.3a, i.e. Direct-Sequence UWB and MultiBand-Orthogonal Frequency Division Multiplexing UWB. The mutual interference of such coexistence scenarios is analyzed by physical layer Monte Carlo simulations. We observe severe and asymmetric performance degradation due to the mutual interference between the two systems. Therefore, we propose the goodput-oriented utility-based transmit power control (GUTPC) algorithm to improve the performance. The feasible condition and the convergence property of GUTPC are investigated, and the choice of the coefficients is discussed for fairness and efficiency. Numerical results demonstrate that GUTPC improves the goodput of the coexisting systems effectively and fairly.

This paper presents an automated software tool SQUARE (software quality and architecture modelling environment). It is designed and implemented to support the analysis of software quality from software architectural designs. The tool is based on a model-based method and follows a structured process to systematically derive quality models from software architectural designs by adapting and applying the principles of system hazard analysis. Through identification of potential quality hazards and their consequences, the quality related properties of the components and connectors and the causal relationships between them are derived and then translated into a quality model represented in a graphical notation. The tool enables automated analysis of the quality models in the graphical notation to recognize a number of types of software quality features including quality sensitive components, quality risks and quality trade-off points, etc. A case study with a real e-commerce system is also reported

Anycast routing is very useful for many applications such as resource discovery in delay tolerant networks (DTNs). In this paper, based on a new DTN model, we first analyze the any-cast semantics for DTNs. Then we present a novel metric named EMDDA (expected multi-destination delay for anycast) and a corresponding routing algorithm for anycast routing in DTNs. Extensive simulation results show that the proposed EMDDA routing scheme can effectively improve the efficiency of anycast routing in DTNs. It outperforms another algorithm, minimum expected delay (MED) algorithm, by 11.3% on average in term of routing delays and by 19.2% in term of average max queue length.

Group communication is becoming increasingly important with the decreasing cost of broadband access and the growing number of Internet users, which requires efficient multi-source multicast support. This paper targets at addressing two fundamental questions related to application-layer multi- source multicast: how many sources can be simultaneously served and how in reality can such a service be effectively provided. Considering the high bandwidth requirement and potential huge control overhead, in this paper we construct a single distribution tree shared by all the sources. Theoretically, we prove that the optimal single shared tree solution can serve at least m -1 sources, where m is the maximum number of sources that can be supported in the system with any number of trees. Practically, we propose a distributed protocol for tree construction and tree refinement so as to approach the theoretical optimal. The simulation results manifest that our proposed heuristic protocol significantly increases the number of sources that can be served in multi-source sessions and the result approaches to the optimal solution.

The mandatory access mode in IEEE 802.11 MAC is distributed coordination function (DCF), which provides both basic access and RTS/CTS(request-to-send/clear-to-send) access. Previous modeling work for DCF only provides analysis when all the nodes in WLAN lie in the carrier sense range of each other. However, in real deployment of WLAN, all the stations are in the communication range of access point (AP) but not necessarily with respect to each other, which results in hidden terminals(HT) and degrades the performance. This paper presents an accurate analytical model for DCF with hidden terminals through Markov chain for throughput in saturated situation. The analytical model is suitable for both basic and RTS/CTS access mechanisms, and is evaluated by extensive simulation results.

The Internet has witnessed a rapid growth in deployment of data-driven overlay network (DON) based streaming applications during recent years. In these applications, each node independently selects some other nodes as its neighbors (i.e. overlay construction), and exchanges streaming data with these neighbors (i.e. data scheduling). This scheme improves the robustness of the system. However, most of the work in the literature focused on the construction problem, and very few addressed its scheduling problem which is also very important for the overall performance. In this paper, we analytically study the scheduling problem in DON and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to maximize the system throughput. Experimental results indicate that our algorithms outperform other schemes and the throughput gain is up to 80%.

Overlay multicast is becoming increasingly popular for multimedia delivery over Internet. The available bandwidths between sender and different receivers are generally heterogeneous. In this paper, we propose a scheme to improve the throughput of a media delivery session with heterogeneous receivers by organizing the receivers into layered data distribution meshes and sending substreams to each mesh using layered coding. Our solution utilizes alternative paths and network coding in each mesh. We first formulate the problem into a mathematical programming, whose optimal solution requires global information. We therefore present a distributed heuristic algorithm. The heuristic progressively organizes the receivers into layered meshes. Each receiver can subscribe to a proper number of meshes to maximize its throughput by fully utilizing its available bandwidth. The benefits of organizing the topology into layered mesh and using network coding are demonstrated through extensive simulations. Numerical results indicate that the average throughput of a media delivery session is significantly improved.

Transferring voice traffic over multihop wireless mesh network (WMN) based on IEEE 802.11 is a challenging job. One main reason is that many small VoIP packets introduce significant overhead, and therefore greatly limits the capacity of VoIP support in WMN. To alleviate this inefficiency, in this paper, we argue for aggregation at routing layer, termed as path aggregation. With path aggregation, flows with similar directions choose routes that share as many as common links. This way, small VoIP packets from different flows can be effectively multiplexed into large packets over these common links and thus increases the utilization of wireless channel. We introduce an important performance metric, Channel Time Cost (CTC), to reflex the channel time used to deliver a voice flow. We formulate the path aggregation as an optimization problem that minimizes the sum of CTC of all flows over the network. We further propose a greedy distributed heuristic algorithm to yield approximate solution. We conduct extensive packet-level simulations and the results confirm that our path aggregation algorithm can effectively improve the VoIP capacity and increase the total throughput of WMN where mixed VoIP traffic and other Best Effort traffic co-exist.

This paper considers the problem of how to maximize throughput in multi-radio multi-channel wireless mesh networks. With mathematical model based on radio and radioto-radio link, we introduce a scheduling graph and show that the feasibility problem of time fraction vector is equal to the problem of whether the scheduling graph is [M]-colorable, where M is the number of slots in one period. We use this equivalence property to derive a sufficient condition of feasibility, and then, using this sufficient condition, we mathematically formulate the joint routing and channel assignment problem as a linear programming problem. Finally, we use vertex coloring to get a feasible schedule and lift the resulting flows. We prove that the optimality gap is above a constant factor. The numeric results demonstrate the effectiveness of our proposed algorithm.

With the advance of wireless technology, wireless devices are capable of adjusting transmit power and physical (PHY) layer data-rate. In this paper, we investigate the problem of how to adjust the power level and the PHY rate in order to maximize the network throughput in wireless ad hoc networks. Solving this problem can help compute the network capacity of ad hoc networks, which has drawn a lot of attention recently. In our study, we find that there exist intertwined relationships among maximum network throughput, power, and PHY rate. These intertwined relationships make computing the maximum network throughput a difficult problem. To get around the coupled relationships among power, PHY rate and network throughput, we take a simulation-based optimization approach, i.e., use a recursive randomized algorithm to find the solution. We study the convergence and complexity of our algorithm. Our results show that our algorithm always converges and the computation complexity is polynomial. The simulations also show that our algorithm can iteratively improve the network throughput, given an initial feasible power and rate setting.

The upcoming standard IEEE 802.11e will provide Quality of Service (QoS) support in Wireless Local Area Network (WLAN). The contention based channel access method called Enhanced Distributed Channel Access (EDCA) is considered as the mandatory mode for Medium Access Control (MAC) in IEEE 802.11e. This paper presents an accurate throughput analytical model for EDCA in saturated situation. The analytical model is suitable for both basic access and RTS/CTS access mechanisms. It considers the features in EDCA such as different Arbitration Inter-frame Space (AIFS) and contention window for different Access Category (AC), and virtual collision for different priority queue in the same station; and can be easily extended to other features such as Transmission Opportunity (TXOP), etc. The analytical model is evaluated by extensive simulation results, and it provides deep understanding of the effect by different parameter setting to the throughput.

Multi-band OFDM Alliance (MBOA) Ultra-wide band (UWB) is a candidate of IEEE 802.15.3a, the standard for high speed Wireless Personal Area Networks (WPAN). The Media Access Control (MAC) protocol of MBOA consists of distributed reservation period (DRP) and Prioritized contention access (PCA), where DRP provides flexible distributed slot reservation for Quality of Service (QoS) support. This paper analyzes the delay performance of DRP channel access for MBOA UWB MAC. In DRP, the reserved periodical slot pattern in each super-frame for station can be arbitrary and not evenly distributed. To address the difference in delay performance introduced by reservation pattern, a bi-dimensional Markov chain model is proposed, where one dimension is for queue size distribution and the other is for allocated slots. The accuracy of the model is verified by simulations and insights has been given for slot reservation in MBOA.

Wireless sensor networks have recently been suggested for many surveillance applications such as object monitoring, path protection, or area coverage. Since the sensors themselves are important and critical objects in the network, a natural question is whether they need certain level of protection, so as to resist the attacks targeting on them directly. If this is necessary, then who should provide this protection, and how it can be achieved? We refer to the above problem as self-protection, as we believe the sensors themselves are the best (and often the only) candidate to provide such protection. In this papel; we for the jirst time present a formal study on the selfprotection problem in wireless sensor networks. We show that, if we simply focus on the quality ofjield or object covering, the sensors might not necessarily be self-protected, which in turn makes the system vulnerable. We then investigate dzreerent forms of self-pmtections, and show that the problems are generally NP-complete. We develop eficient approximation algorithms for centrally-controlled sensors. We then extend the algorithms to filly distributed implementation, and introduce a smart sleep-scheduling algorithm that minimize the energy consumption.

Providing video-on-demand streaming service to a large population of clients using peer-to-peer approach is drawing great interest recently. Since clients' demands are asynchronous and the buffered contents are continuously changing, how to find partners with expected data and collaborate with each other for future content delivery are very important and challenging. In this paper, we propose a generic buffer-assisted search (BAS) scheme to improve partner search efficiency. Extensive simulation results demonstrate that BAS can provide faster response time with less control cost than the existing search methods

Current peer-to-peer (P2P) streaming systems often assume that hosts are cooperative. However, this may not be true in the open environment of the internet. In this paper, we discuss how to detect malicious hosts (e.g., with attacking actions and abnormal behavior) based on their history performance. In our system, each host monitors the performance of its neighbor(s) and reports this to a server. Based on the reports, the server computes host reputation with hosts of low reputation being malicious. A problem is that hosts may lie by submitting forged reports to the server. We hence formulate the reputation computing problem in the presence of lying hosts as a minimization problem and solve it by the traditional Levenberg-Marquardt algorithm. Simulation results show that our scheme can efficiently detect malicious hosts with high accuracy

Compared with file-sharing and distributed hash table (DHT) network, P2P video streaming is more vulnerable to denial of service (DoS) attacks because of its high bandwidth demand and stringent time requirement. This paper studies the design of DoS resilient streaming networks using credit systems. We propose a novel framework-ripple-stream-to improve DoS resilience of P2P streaming. Ripple-stream leverages existing credit systems to introduce credit constraints in overlay construction such that malicious nodes are pushed to the fringe of overlays. Combining credit constraints with overlay optimization techniques, ripple-stream can achieve both DoS resilience and overlay efficiency

Abstract—Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection’s throughput by adopting more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely decrease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based approaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delaybased and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scalable delay-based component into the standard TCP Reno congestion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP. Index Terms—TCP performance, delay-based congestion control, high speed network I.

This paper proposes to leverage multi-source multi-path diversity to design a video streaming system for supporting concurrent video-on-demand (VoD) services over wireless mesh networks (WMNs). By integrating a wireless interference model into consideration, we have a more realistic network model to capture the characteristics of wireless networks. Based on that, we mathematically formulate the route selection problem for the proposed streaming system using rate/interference-distortion optimization framework, and rely on a genetic algorithm to solve it heuristically. Simulation results show that the proposed system has better performance than systems using single-source, single-path and systems that do not consider interference.

Wireless sensor networks have been widely used for surveillance in harsh environments. In many such applications, the environmental data are continuously sensed, and data collection by a server is only performed occasionally. Hence, the sensor nodes have to temporarily store the data, and provide easy and on-hand access for the most updated data when the server approaches. Given the expensive server-to-sensor communications, the large amount of sensors and the limited storage space at each tiny sensor, continuous data collection becomes a challenging problem. In this paper, we present partial network coding (PNC) as a generic tool for the above applications. PNC generalizes the existing network coding (NC) paradigm, an elegant solution for ubiquitous data distribution and collection. Yet, PNC enables efficient storage replacement for continuous data, which is a major deficiency of the conventional NC. We prove that the performance of PNC is quite close to NC, except for a sub-linear overhead on storage and communications. We then address a set of practical concerns toward PNC-based continuous data collection in sensor networks. Its feasibility and superiority are further demonstrated through simulation results

we propose an analytical model for multi-hop IEEE 802.11 networks to calculate how much bandwidth can be utilized along a path without violating the QoS requirements of existing rate-controlled traffic flows. A notion, "free channel time", which is the time allowed for a wireless link to transmit data, is introduced to analyze the path capacity. To achieve the goal, the proposed model effectively characterizes the unsaturated traffic condition. It could also depict the interaction between the newly injected traffic and the hidden traffic that could have influence upon the new traffic. Simulation results demonstrate that our proposed analytical model can accurately predict the path capacity under various network conditions without breaking QoS demands of all existing traffic.

With the increasing development of IEEE 802.11 based Wireless Local Area Network (WLAN) devices, large-scale WLANs with high dense deployment of user terminals and access points (APs) have emerged widely in various hotspots. Enhancing transmission reliability has been a primary challenge for scaling the WLANs because high dense deployment of user terminals and APs results in too many collisions. In this paper, we investigate the defects of single association mechanism defined in IEEE 802.11 on transmission reliability from network perspective. Then, we propose a multi-AP architecture, with which an AP Controller (AC) is employed to enable each user terminal to associate and cooperate with multiple APs. In this way, the user terminals can benefit from the diversity effect of multi-paths with independent collisions and transmission errors. This paper concentrates on the performance comparison between the proposed multi-AP architecture and that in IEEE 802.11 standard. Extensive simulation results show that the proposed mechanism can obtain much better performance in terms of the throughput per user and the total throughput, and the performance gain is position dependent. Moreover, the unfairness issue in traditional WLANs due to capture effect can be alleviated properly in the multi-AP framework. © 2006 ACM.

In this paper we study the problem of using the rate adaptation technique to achieve energy efficiency in an IEEE 802.11-based multi-hop network. Specifically, we formulate it as an optimization problem, i.e., minimizing the total transmission power over transmission data rates, subject to the traffic requirements of all the nodes in a multi-hop network. Interestingly, we can show that this problem is actually a well-known multiple-choice knapsack problem, which is proven to be an NP-hard problem. So, instead of finding an optimal solution, which is NP-hard, we seek a sub-optimal solution. Our key technique to attack this problem is distributed cooperative rate adaptation. Here, we promote node cooperation due to our observation that the inequality in non-cooperative channel contention among nodes caused by hidden terminal phenomenon in a multi-hop network tends to result in energy inefficiency. Under this design philosophy, we propose a distributed cooperative rate adaptation (CRA) scheme and prove that it converges. Simulation results show that our CRA scheme can reduce the power consumption up to 86% as compared to the existing (non-cooperative) algorithm.

Real time communication (RTC) has critical quality of service (QoS) requirements, which is much more challenging in wireless multi-hop networks. Traditional measurement-based routing schemes often ignore the interference from the coming RTC traffic itself (i.e. self-traffic), so they can not get an accurate quality estimation of the path to serve the coming RTC traffic. In this paper, we propose a novel prediction-based routing metric, PPTT (Path Predicted Transmission Time), to estimate end-to-end delay of RTC traffics. PPTT is traffic-aware by taking explicit consideration of both self-traffic and neighboring traffics interfering with the RTC flow, and thus offers an accurate prediction of transmission delay. By selecting route with minimal PPTT, the quality of service for the coming RTC flow will be improved, in terms of end-to-end delay and goodput. To evaluate the performance, we implement PPTT scheme and study its performance in a wireless multi-hop test bed consisting of 32 nodes equipped with IEEE 802.11 a/b/g combo cards, and we also conduct extensive simulations with different random topologies in network simulator NS2 for a more comprehensive comparison. Experiment results show that this routing metric outperforms other non prediction-based routing metric such as ETX (Expected Transmission Count) and WCETT (Weighted Cumulative Expected Transmission Time) in terms of delay and goodput in wireless multi-hop networks.

This paper addresses the throughput optimization problem in multihop ultra-wideband (UWB) networks by jointly considering network topology formation and routing. Given a spatial distribution of UWB devices and traffic requirement, we want to form piconets and select paths to maximize the network throughput. Although there have been several works considering the problem of selecting paths to achieve the optimal throughput in multihop wireless networks, to the best of our knowledge, none of them takes the topology formation into the consideration. In this paper, we use Boolean matrices to model role assignment in UWB networks and formulate the throughput optimization problem as a nonlinear programming (NLP) problem. Since the throughput optimization problem is NP-hard, we give an upper bound of the optimal throughput by relaxing some constraints and using pseudo-Boolean optimization to linearize the NLP. We prove that the solution of the upper bound is at most three times of the optimal throughput. Based on the topology formed by solving the upper bound, we formulate a lower bound of the optimal throughput as a linear programming problem and use column generation to solve the lower bound. Numerical results show that the lower bound is very close to the upper bound. Simulation results demonstrate the effectiveness of the scheme.

In this paper, we first identify several challenges in designing a joint channel assignment and routing (JCAR) protocol in heterogeneous multiradio multichannel multihop wireless networks (M3WNs) using commercial hardware [e.g., IEEE 802.11 Network Interface Card (NIC)]. We then propose a novel software solution, called Layer 2.5 JCAR, which resides between the MAC layer and routing layer. JCAR jointly coordinates the channel selection on each wireless interface and the route selection among interfaces based on the traffic information measured and exchanged among the two-hop neighbors. Since interference is one of the major factors that constrain the performance in a M3WN, in this paper, we introduce an important channel cost metric (CCM) which actually reflects the interference cost and is defined as the sum of expected transmission time weighted by the channel utilization over all interfering channels (for each node). In CCM, both the interference and the diverse channel characteristics are taken into account. An expression for CCM is derived in terms of equivalent fraction of air time by explicitly taking the radio heterogeneity into consideration. Using CCM as one of the key performance measures, we propose a distributed algorithm (heuristic) that produces near-optimal JCAR solution. To evaluate the efficacy of our heuristics, we conduct extensive simulations using the network simulator NS2. To demonstrate implementation feasibility, we conducted various experiments for the proposed distributed JCAR algorithm on a multihop wireless network testbed with nine wireless nodes, each is equipped with single/multiple 802.11a/g cards. Both experimental and simulation results demonstrate the effectiveness and implementation easiness of our proposed software solution

Packet loss is inevitable in video multicast. In this paper, we propose and study an effective feedback-free loss recovery scheme for layered video which combines forward error correction (FEC) and stream replication. In our scheme, the server multicasts the video in parallel with FEC packets and a number of replicated delayed (ReD) version of the stream. Receivers autonomously and dynamically join the FEC and ReD streams to repair their losses. On the server side, we analyze and optimize the number of replicated streams and FEC packets to meet a certain residual loss requirement (i.e., error after correction). On the receiver side, we analyze the optimal combination of FEC and ReD packets to minimize its loss. We also present a fast yet accurate approximation algorithm for receiver to make such decision. We show that FEC combined with merely one or two replicated streams can effectively reduce the residual error rate (by as much as 50%) as compared with pure FEC or replication alone. Both subjective and objective video measures confirm that our recovery scheme achieves much better visual quality.

We consider media streaming using application-level multicast (ALM) where packet loss has to be recovered via retransmission in a timely manner. Since packets may be lost due to congestion, node failures, and join and leave dynamics, traditional "vertical" recovery approach where upstream nodes retransmit the lost packets is no longer effective. We therefore propose lateral error recovery (LER). In LER, hosts are divided into a number of planes, each of which forms an independent ALM tree. Since error correlation across planes is low, a node effectively recovers its error by "laterally" requesting retransmission from nearby nodes in other planes. We present analysis on the complexity and recovery delay on LER. Using Internet-like topologies, we show via simulations that LER is an effective error recovery mechanism. It achieves low overhead in terms of delivery delay (i.e., relative delay penalty) and physical link stress. As compared with traditional recovery schemes, LER attains much lower residual loss rate (i.e., loss rate after retransmission) under a certain deadline constraint. The performance can be substantially improved in the presence of some reliable proxies.

Recent advances in information theory show that the throughput of a multicast session can be improved using network coding. In overlay networks, the available bandwidth between sender and different receivers are different. In this paper, we propose a solution to improve the throughput of an overlay multicast session with heterogeneous receivers by organizing the receivers into layered data distribution meshes and sending substreams to each mesh using layered coding. Our solutions utilize alternative paths and network coding in each mesh. We first formulate the problem into a mathematical programming, whose optimal solution requires global information. We therefore present a distributed heuristic algorithm. The heuristic progressively organizes the receivers into layered meshes. Each receiver can subscribe to a proper number of meshes to maximize its throughput by fully utilizing its available bandwidth. The benefits of organizing the topology into layered mesh and using network coding are demonstrated through extensive simulations. Numerical results indicate that the average throughput of a multicast session is significantly improved (up to 50% to 60%) with only slightly higher delay and network resource consumption