Sequential recommenders aim to enhance prediction accuracy by leveraging user interaction sequences, with transformer-based models showing particularly strong performance. Among them, cold-start sequential recommenders are particularly challenging because these models typically require extensive historical data to perform optimally. Some works attempt to address this issue by enhancing the adaptive ability of the sequence recommenders with meta-learning approaches. However, they are unsuitable for enhancing the popular Transformer-based sequence recommenders: MAML-based models cannot adapt the large number of parameters of Transformers, while transition-based and metric-based meta-learning models rely on unique architectures that are incompatible with Transformer-based frameworks. Also, they usually lack mechanisms to recognize and cater to multiple interests within short interaction sequences. To address these limitations, we propose MESA, a meta-modulation plugin module specifically designed to enhance the cold-start recommendation of Transformer-based sequential recommender systems. (1) We design a meta-modulation method to directly modulate the parameters in Transformer-based sequence encoders, thus enabling the model to adapt more effectively to new users in cold-start scenarios. (2) Additionally, MESA integrates the Mixture of Experts (MoE) mechanism, which refines sequence representations by utilizing multiple experts, each focusing on different aspects of user interests. This structure enhances the personalization of the recommendation by effectively handling diverse user interests within the sequences. Experiments demonstrate the effectiveness of MESA in cold-start scenarios. Our codes are available here : https://github.com/Mushroom-cat/MESA .

Graph Contrastive Learning (GCL) has recently garnered significant attention for enhancing recommender systems. Most existing GCL-based methods perturb the raw data graph to generate views, performing contrastive learning across these views to learn generalizable representations. However, most of these methods rely on data- or model-based augmentation techniques that may disrupt interest consistency. In this paper, we propose a novel interest-aware augmentation approach based on diffusion models to address this issue. Specifically, we leverage a conditional diffusion model to generate interest-consistent views by conditioning on node interaction information, ensuring that the generated views align with the interests of the nodes. Based on this augmentation method, we introduce DiffCL, a graph contrastive learning framework for recommendation. Furthermore, we propose an easy-to-hard generation strategy. By progressively adjusting the starting point of the reverse denoising process, this strategy further enhances effective contrastive learning. We evaluate DiffCL on three public real-world datasets, and results indicate that our method outperforms state-of-the-art techniques, demonstrating its effectiveness.

Aggregating noisy labels from mobile crowdsourcing (MCS) to recover true labels is a fundamental yet challenging problem, especially due to the sparsity and unreliability of crowd-contributed data. While most prior work addresses only single-label scenarios, real-world MCS applications often require robust solutions for both single-label and multi-label tasks, where each instance may be associated with multiple categories. In this paper, we propose ATHENA, a novel approach that leverages self-supervision signals inherent in MCS data for effective label aggregation. Firstly, we propose a graph transformer model that can learn from the MCS topology and features. Then, we propose self-supervision signals inherently included in the dataset to help aggregate the labels. To address the unique challenges of multi-label aggregation, we further extend our approach toATHENA+, introducing a label message passing (LMP) module that explicitly models correlations and dependencies among labels. We conducted extensive experiments on multiple single-label and multi-label classification datasets, comparing the proposed models with state-of-the-art methods. Our results demonstrate that ATHENA and ATHENA+ are highly effective in aggregating labels and obtain much better performance than existing methods.

Headphones have become ubiquitous in daily work and communication, leading users to assume a sense of privacy and security during confidential conversations while overlooking the potential risk of eavesdropping. In this paper, we present mmEar, an end-to-end eavesdropping system that demonstrates the feasibility of compromising headphones using a commercial off-the-shelf (COTS) mmWave radar. Unlike previous approaches that rely on relatively strong vibrations, mmEar targets extremely faint, low-SNR speech-induced vibrations on headphone surfaces. To address this challenge, we introduce a Faint Vibration Emphasis (FVE) technique that amplifies phase variations on the IQ plane, followed by a deep denoising network for enhanced signal quality. Furthermore, we design a diffusion-based generative model within a pretrain–finetune framework, leveraging large-scale synthetic data to significantly improve generalization and robustness across diverse scenarios. Extensive experiments on multiple headphone and earphone models validate the practicality and effectiveness of the proposed attack, revealing that most tested devices can be compromised to recover intelligible speech.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical for many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developedMagPrint++, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting.MagPrint++has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation both on COTS mobile phones and a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users,MagPrint++achieves$94.3\%$accuracy in classifying users from these traces, which represents a$10.9\%$improvement over the state-of-the-art classification method.

Face authentication has become an increasingly attractive and prevalent technology in human-computer interaction. Recent works have explored radio frequency (RF) signals for illumination-robust and privacy-preserving face authentication. However, these approaches require all users to undergo compulsory prior registration and cannot effectively handle unregistered users, which restricts their practical applicability in real-world scenarios. In this paper, we present ammWave-basedmulti-user face authentication system,m3FacePass, which performs zero-shot face authentication in complex multi-user scenarios using a commercial off-the-shelf (COTS) mmWave radar. First, m3FacePass collects mmWave signals and reconstructs spatial mappings by generating 3D heatmaps. Based on the 3D heatmaps,m3FacePassdetects and separates faces in multi-user scenarios through template matching. Then, unique facial features are extracted and transformed to a hypersphere manifold, which reserves feature space for unregistered users. By introducing dummy classifiers in the authentication model,m3FacePassoptimizes decision boundaries between registered and unregistered users. Furthermore,m3FacePassenhances its generalization ability for zero-shot authentication by incorporating pseudo unknowns during model training. Extensive experiments in real-world environments demonstrate thatm3FacePassachieves an authentication accuracy of 96.6% for registered users and 91.1% for unregistered users in multi-user scenarios.

Face authentication has become an increasingly attractive and prevalent technology in human-computer interaction. Recent works have explored radio frequency (RF) signals for illumination-robust and privacy-preserving face authentication. However, these approaches require all users to undergo compulsory prior registration and cannot effectively handle unregistered users, which restricts their practical applicability in real-world scenarios. In this paper, we present a mmWave-based multiuser face authentication system,$m^{3}$FacePass, which performs zero-shot face authentication in complex multi-user scenarios using a commercial off-the-shelf (COTS) mm Wave radar. First,$m^{3}$FacePass collects mmWave signals and reconstructs spatial mappings by generating 3D heatmaps. Based on the 3D heatmaps,$m^{3}$FacePass detects and separates faces in multi-user scenarios through template matching. Then, unique facial features are extracted and transformed to a hypersphere manifold, which reserves feature space for unregistered users. By introducing dummy classifiers in the authentication model,$\boldsymbol{m}^{\mathbf{3}}$FacePass optimizes decision boundaries between registered and unregistered users. Furthermore,$\boldsymbol{m}^{\mathbf{3}}$FacePass enhances its generalization ability for zero-shot authentication by incorporating pseudo unknowns during model training. Extensive experiments in realworld environments demonstrate that$\boldsymbol{m}^{3}$FacePass achieves an authentication accuracy of 96.6% for registered users and 91.1% for unregistered users in multi-user scenarios.

Blockchain enhances the robustness and user engagement of Federated Learning (FL) systems but fails to meet the throughput and real-time requirements for model transmission. While sharding architectures improve system throughput, the latency introduced by mainchain model transmission remains a performance bottleneck, compromising the QoS of FL systems. In this paper, we propose a Mainchain-Free Sharding architecture, MFSChain, featuring an adaptive sharding mechanism based on hierarchical clustering. This mechanism improves shard model performance by eliminating the need for mainchain aggregation (i.e., shard-level global models). We also introduce the Federated Learning State Tree (FLS-Tree) for client management and state migration without a mainchain, alongside a lightweight storage scheme, LiFLS-Tree. Through theoretical analysis and extensive simulations, we demonstrate that MFSChain outperforms traditional blockchain and sharding architectures. Specifically, MFSChain reduces client waiting time by 17% and 24%, increases average model accuracy by 2.5% to 10% compared to traditional global models, and boosts throughput by$464 \times$while reducing transaction processing latency by 99%.

Social recommendation systems leverage both user-user (u-u) social relations and user-item (u-i) collaborative interactions to improve recommendation quality. Despite their effectiveness, existing models often struggle with task-irrelevant information and misalignment between social and collaborative signals and the downstream recommendation task, leading to suboptimal performance. To address these limitations, we propose a novel framework for Effective and Consistent Information Extraction for Social Recommendation (ECSR). Our approach focuses on two key modules: (1) a task-irrelevant information discarding module that filters out noisy signals from both social relations and user-item interactions, and (2) a task-relevant information alignment module that captures both shared and view-specific task-relevant information, ensuring alignment with the recommendation objective. By integrating them into a unified form, our method extracts minimal and sufficient statistics, which significantly enhance the model's ability to predict user preferences. We validate ECSR on three real-world social recommendation datasets, demonstrating that it consistently outperforms state-of-the-art baselines.

Bluetooth-based location services have experienced significant growth over the past decades. RSSI-based techniques using beacons only provide meters-level accuracy. Angular-based approaches rely on customized antenna arrays, introducing high costs and limited usability. In 2020, Bluetooth Special Interest Group (Bluetooth SIG) released version 5.1, integrating Angle of Arrival (AoA) estimation to enable direction finding capabilities, which has the potential to improve localization across various fields, including logistics and industry. However, more than 4.1 billion devices (68% of the total) still do not support the direction finding feature. To address this issue and ensure backward compatibility, we proposed Bridge, a solution that leverages an additional trigger node (referred to as Trigger) to make the direction finding feature compatible with all Bluetooth devices without requiring modifications to existing hardware or firmware. The Trigger mimics communication behaviors with both locators and targets simultaneously by sending a nesting packet. Subsequently, processes and algorithms are delicately designed to estimate AoA. Bridge also supports large-scale deployment through dynamic packet flow switching, enabling it to handle concurrent targets and manage handover with a consistent operation pattern. We implemented and evaluated Bridge in real-world scenarios. The system achieved an average localization error of 33.4cm while extending the direction-finding feature to 10 target devices of different Bluetooth versions, indicating the effectiveness of Bridge.

With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link reliability, as evidenced by the measurement results based on our real-world setup. In this paper, we propose a blind coherent combining design named B2LoRa to boost LoRa transmission performance. The intuition behind B2LoRa is to leverage the repeated broadcasting mechanism inherent in satellite-IoT systems to achieve coherent combining under the low-power and low-cost constraints, where each re-transmission at different times is regarded as the same packet transmitted from different antenna elements within an antenna array. Then, the problem is translated into aligning these packets at a fine granularity despite the time, frequency, and phase offsets between packets in the case of frequent packet loss. To overcome this challenge, we present three designs — joint packet sniffing, frequency shift alignment, and phase drift mitigation to deal with ultra-low SNRs and Doppler shifts featured in satellite-IoT systems, respectively. Finally, experiment results based on our real-world deployments demonstrate the high efficiency of B2LoRa.

Bluetooth Low Energy (BLE) direction finding, a feature introduced in BLE version 5.1, enables precise localization through Angle of Arrival (AoA) estimation. However, this advancement introduces new risk to BLE direction finding based localization system. Specifically, the AoA estimation based on phase sampling of constant-tone-extension (CTE) is susceptible to the signal injection attack. This paper presents SaDiF, a feasible spoofing attack mechanism to mislead the locators into mistaking the positioning result as a continuous path. By eavesdropping on BLE packets and injecting attack signals containing pre-designed disturbing phase shift, SaDiF subtly alters the AoA estimation without detection, thus interfere the localization results. Moreover, SaDiF address the challenges posed by hardware imperfections by proposing an injection timing optimization to improve attack robustness. Extensive experiments demonstrates the effectiveness of SaDiF in successfully attacking multiple BLE targets in real-time scenarios. In conclusion, our findings reveal critical security risks in BLE direction finding feature and provide insights into strengthening its defenses.

Designing effective negative sampling strategies is crucial for training Multi-Modal Recommendation (MMRec) models, as it helps address the issues of sparse user-item interactions and facilitates the learning of high-dimensional modality features. However, most existing methods randomly sample non-interacted items as negative ones, which frequently result in easy negatives. They limit the model's ability to accurately capture user preferences. In this paper, we propose to Generate Difficulty-aware Negative Samples via conditional diffusion for MMRec (denoted as GDNSM). Leveraging the rich semantic and contextual information from multi-modal features, our method generates hard negative samples with varying difficulty levels, tailored to user preferences. They force the model to learn finer-grained distinctions between positive and negative samples, enhancing its ability to inferring user preferences. To avoid unstable training, we design a dynamic difficulty scheduling mechanism that schedules the negative samples from easy to hard for model training, ensuring both stability and effectiveness. Extensive experiments on three real-world datasets demonstrate that the effectiveness of our models.

The integration of social information into recommender systems (RSs) has gained significant popularity for enhancing recommendation performance and user experience. However, this practice introduces substantial privacy risks, particularly concerning the leakage of sensitive social relationships. While prior research has primarily focused on user-level and interaction-level privacy risks, the social relation-level privacy risks remain largely unexplored. To fill this gap, we investigate social privacy risks through membership inference attacks (MIA) and propose a Social relation-level MIA (SMIA) framework. Two key challenges arise: (1) the adversary can only access the recommended item IDs, which provide indirect and limited information about social relationships, and (2) extracting socially relevant preferences from recommendation results is inherently difficult. To tackle the first challenge, we leverage shadow models to transform sparse item IDs into dense features, enabling adversaries to effectively utilize recommendation outputs. For the second challenge, SMIA employs a dual-branch learning approach that disentangles social and behavioral preferences. Therefore, we can extract socially relevant signals from the disentangled preferences.Extensive experiments on real-world datasets demonstrate that both social and general RSs are highly vulnerable to such attacks, highlighting the urgent need for robust privacy protection mechanisms. To defend against these attacks, we introduce a Socially Adversarial Learning (SAL) defense mechanism that selectively obscures sensitive social information in user representations during training, effectively reducing privacy leakage. We further evaluate the effectiveness of our defense and discuss future directions for developing privacy-preserving mechanisms in social RSs.

Physical layer authentication (PLA), which leverages device-specific physical layer features to achieve information-theoretic security with low complexity, offers a hardware-rooted security solution for next-generation Internet of Things (IoT) networks. While existing PLA approaches primarily rely on passive extraction of inherent hardware-induced radio frequency features, they remain vulnerable to adversarial spoofing that replicates legitimate radio frequency fingerprints (RFF). To address this critical vulnerability, we propose an active PLA framework that employs a challenge–response protocol to embed session-specific perturbation into each transmission. Upon receiving a nonce from the receiver, the legitimate transmitter generates a hash value and embeds a corresponding in-phase and quadrature (IQ) imbalance-based perturbation into the baseband signal. This design conceals inherent hardware-specific RFF and injects dynamic, unpredictable fingerprints that vary across sessions and are resilient to forgery. At the receiver, authentication is performed using a learning-based method that combines a CNN-based feature extractor with a lightweight logistic regression classifier trained on augmented samples. Extensive simulations demonstrate that the proposed framework achieves high authentication accuracy under both static and dynamic channel conditions, while effectively resisting advanced spoofing attacks, including GAN-based impersonation. These results confirm the robustness, generalization capability, and applicability of the proposed scheme for secure IoT communications.

With the rapid developments of Internet of Thing (IoT) technologies, side-channel sound sensing has become a key research area. Unlike traditional sound-sensing methods that rely on direct acoustic signal recording, side-channel sound-sensing methods utilize various indirect emissions from electronic devices, such as mechanical vibrations, electromagnetic leakage, etc., to infer sound content, making it preferred in many application scenarios. Recently, many works related to side-channel sound sensing have been proposed and drawn much attention from not only academia but also industry. In this article, we present a comprehensive survey of various side-channel sound-sensing methods. Specifically, we first review the commonly sensed object-state characteristics in side-channel sound-sensing methods. Then, we introduce the widely exploited hardware infrastructure, and signal processing techniques, which are the basis of side-channel sound sensing. After that, we provide a categorization introduction of emerging side-channel sound-sensing methods, and review the common application scenarios. Finally, we provide a thoughtful analysis of the countermeasures, challenges, and future directions in this field. With this survey, we aim to summarize the latest research results in the field of side-channel sound sensing and provide insights and inspiration for future research works.

As one of the most common ways for user authentication, Personal Identification Number (PIN), due to its simplicity and convenience, has suffered from plenty of side-channel attacks, which pose a severe threat to people’s privacy and property. The success of existing attacks is usually built upon the premise of no occlusion between the attacker and the victim’s hand gesture, but it increases the difficulty of launching the attack and the possibility of exposure. To overcome such limitation, we propose ArmSpy++, an improved video-assisted PIN inference attack built upon our previous research, ArmSpy. Specifically, ArmSpy++ employs new modules to leverage more features like the keystroke-induced elbow bending, wrist speed variation, and the spatial relationship between different arm joints, to correctly detect Keystrokes. ArmSpy++ delves into the perspective relationship and natural typing habits to ensure a high success rate of PIN inference. We also re-designed the inferred PIN pattern coordination mechanism to accurately deduce the PINs. By using a pre-trained HigherHRNet model for posture estimation ArmSpy++ eliminates the necessity of additional training. The extensive experiments demonstrate that ArmSpy++ can achieve over 83.1% average accuracy with 3 attempts and even 92.5% for some victims, indicating the severity of the threat posed by ArmSpy++.

As one of the most essential accessories, headsets have been widely used in common online conversations. The metal coil vibration patterns of headset speakers/microphones have been proven to be highly correlated with the speaker-produced/microphone-received sound. This paper presents an online conversation eavesdropping system,RFSpy, which uses only one RFID tag attached on a headset to alternately sense metal coil vibrations of headset speaker and microphone for eavesdropping on speaker-produced and microphone-received sound. In some accessible scenarios, assuming attackers secretly attach a small, battery-free RFID tag under one ear cushion of an eavesdropped user’s headset without being noticed. Meanwhile, RFID readers are camouflaged as decorations placed in/out of rooms to transmit and receive RF signals. When the eavesdropped user talks with other users online through the headset,RFSpyfirst activates the RFID tag to capture the metal coil vibration patterns of headset speaker and microphone upon RF signals. Then,RFSpyreconstructs sound spectrograms from the RF signal-based vibration patterns for not only trained words but also untrained (i.e., out-of-vocabulary) words utilizing designed SSR network. Finally,RFSpyconverts the sound spectrograms to conversation content through sound recognition API. Extensive experiments demonstrate thatRFSpycan eavesdrop on online conversations with out-of-vocabulary words effectively.

The technology of facial expression reconstruction has paved the way for various face-centric applications such as virtual reality (VR) modeling, human-computer interaction, and affective computing. Existing vision-based solutions present challenges in privacy leakage and poor lighting conditions. In this paper, we introduce a nonintrusive facial expression reconstruction system,mm3DFace, which uses a millimeter wave (mmWave) radar to reconstruct facial expressions in a privacy-preserving and passive manner.mm3DFacefirst captures and pre-processes mmWave signals reflected by a human face, and extracts intricate facial geometric features using a ConvNeXt model integrated with triple loss embedding. Subsequently,mm3DFacederives pose-invariant facial representations utilizing region-divided affine transformation, and further generates individual facial shapes with 68 facial landmarks. Then, dynamic facial expressions with 3D facial avatars are reconstructed to exhibit realistic facial expressions. Finally,mm3DFaceenables micro-expression recognition with mmWave signals, which ensures the capability of describing tiny facial changes. Through extensive real-world experiments involving 15 participants,mm3DFaceachieves a normalized mean error of 3.94%, a mean absolute error of 2.30 mm, and a 3D-mean absolute error of 4.10 mm in tracking 68 facial landmarks, which demonstrates the efficacy and practicality ofmm3DFacein real-world 3D facial reconstruction scenarios.

The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios.

The two-factor authentication (2FA) has been increasingly used with the popularity of mobile devices. Currently, many existing 2FA schemes extract the devices’ acoustic fingerprints as the second factor. Nevertheless, they mainly consider deriving fingerprints from the raw acoustic waveforms for authentication, which are susceptible to the fingerprint variations caused by the environmental noise or the varying distance between devices. To address these vulnerabilities, we propose a robust system utilizing the distortions of modulated signals, which are incurred by the acoustic elements of mobile devices, as the proof for 2FA. Specifically, our system first designs a channel delay estimation scheme to accurately estimate the propagation delay from the speaker to the microphone by deriving the phase change of the received sinusoidal signal. To perform a robust authentication, we design a new acoustic fingerprinting scheme to remove the impacts of the varying distance and environmental noise from the demodulated PSK signals for fingerprint extraction. Moreover, our device authentication component designs a transfer learning-based scheme to capture the subtle differences in devices’ fingerprints for accurate device authentication. To the best of our knowledge, this is the first 2FA system that could extract acoustic fingerprints in modulation domain and can effectively withstand the impacts of channel distortions. We also confirm the accuracy and security of our system through extensive user experiments.

User authentication is evolving with expanded applications and innovative techniques. New authentication approaches utilize RF signals to sense specific human characteristics, offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generate environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Next, fine-grained body features are extracted from these environment-independent and posture-independent human silhouette images using a metric learning model. To eliminate potential security threats that arise from unauthorized users, OpenAuth synthesizes data placeholders for enhancing unauthorized user identification. Finally, a k-NN-based authentication model is constructed to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4% and false acceptance rate (FAR) of 1.8% in open-world scenarios.

User cold-start recommendation presents a significant challenge for recommender systems, affecting their overall effectiveness. Meta-learning-based methods have been introduced to address this issue. These methods treat the user cold-start recommendation problem as a few-shot learning task, where each user represents a unique task. The objective is to acquire shared initialization parameters that can be effectively applied across all cold-start users. Subsequently, these shared parameters are fine-tuned into personalized parameters using individual interaction data. Recent studies argue that shared parameters are unsuitable for all users with an implicit grouping distribution of user preference. Therefore, they propose adaptive-initialization-based methods, which first differentiate tasks based on user preferences and then generate task-adaptive initialization parameters using task representations. However, both the meta-learning and adaptive-initialization-based manners ignore discovering the adaptive capability of update strategies in the process of transferring initialization parameters to personalized parameters. Instead, they rely on task-shared optimization strategies, leading the model to fall into an overfitting or underfitting situation. In response to this, we propose a dual-adaptive update strategies-enhanced meta-optimization framework (DAUS) for user cold-start recommendation. First, we integrate dual-adaptive update strategies to enhance the adaptive capability of transferring initialization parameters. This involves incorporating both task-adaptive optimization hyperparameters and objectives. Second, we design a multifaceted task encoder , which can provide diverse task information to differentiate between tasks, including explicit task features (task relevance, training signals) and other implicit task information. Extensive experiments based on three real-world datasets demonstrate that our DAUS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/DAUS .

The need for an adaptive congestion control (CC) service is crucial due to the heterogeneity of systems and the diversity of applications. Traditional CC methods often fail to adaptively balance throughput and delay, struggling to meet the varied demands of different network applications. In this work, we introduceAuto, a novel CC service that employs Multi-Objective Reinforcement Learning (MORL) to transcend these limitations. Unlike conventional approaches,Autooptimizes policies within a single model to cater to all potential preferences for balancing throughput and delay, making it ideal for diverse and heterogeneous network environments. To enhance operational transparency, we developed an interpretation algorithm that translates MORL into a human- readable decision tree, essential for service computing where clarity and interpretability are crucial. Furthermore,Autoallows users to explicitly set flow priorities and target sending rates, meeting varied application demands. Our extensive evaluations show thatAutonot only consistently outperforms existing CC methods in diverse network conditions but also exhibits robustness to stochastic packet loss and rapid network changes. These capabilities establishAutoas a pioneering solution for next-generation congestion control in networking services.

Hand pose estimation is a key support for a variety of interactive applications including user interface control, sign language understanding, virtual reality modeling, etc. Existing approaches mainly exploit wearable devices such as gloves or bracelets to estimate hand poses, which may introduce high deploying costs and intrusive user experience. Others rely on vision technologies whereas they could face complicated illuminations and privacy leakage. In this paper, we present a millimeter wave (mmWave) signal-based 3D hand pose estimation system, mmHand, which utilizes a mmWave radar to generate 3D hand skeletons and reconstruct 3D hand meshes. mmHand first leverages mmWave signals to sense a hand and pre-process the signals. Then, mmHand extracts spatial and temporal features using a designed attention-based hourglass network (mmSpaceNet) and Long Short-Term Memory (LSTM), respectively. Based on the extracted features, mmHand further regresses hand joints in 3D space to generate 3D hand skeletons. Finally, 3D hand meshes that continuously describe hand poses with detailed surfaces are reconstructed through a hand Model with Articulated and Non-rigid defOrmations (MANO). Extensive experiments demonstrate that mmHand can accurately generate 3D hand skeletons with 18.3mm mean per joint position error and 95.1 % of correct key points, which indicates the effectiveness of mmHand on hand pose estimation.

User authentication is evolving with expanded application scenarios and innovative techniques. New authentication approaches utilize RF signals to sense specific human behaviors and characteristics, such as faces, specific gestures, etc., offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generates environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Based on the environment-independent and posture-independent human silhouette images, OpenAuth further extracts fine-grained body features through a metric learning model for user authentication. To eliminate potential security threats that arise from frequent accesses by unauthorized users, OpenAuth synthesizes data placeholders for enhancing the applicability of unauthorized user identification. Finally, a k-NN-based authentication model is constructed based on the extracted body features to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4 % and false acceptance rate (FAR) of 1.8% in open-world scenarios.

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques.

This paper introduces a novel multi-directional speaker, named MuDiS, which utilizes a parametric array to generate highly focused sound beams in multiple directions. The system capitalizes on air nonlinearity to reproduce sound from ultrasounds, successfully overcoming challenges inherent in traditional parametric arrays, such as transducer size and wavefront shape. It supports three important features simultaneously: independent beams, wide-angle digital steering, and unintended leakage suppression. To address these challenges, we designed a specialized cell structure that connects ultrasonic transducers, redirecting an approximately omnidirectional wavefront with optimal interspacing. An optimization-based algorithm is developed to minimize unintended leakages, and a nonlinear distortion reduction scheme is proposed to enhance sound quality. The paper showcases a prototype demonstrating the system's capabilities as a multidirectional speaker with a wide sound projection angle. Experimental results validate the effectiveness of our approach. The proposed multi-beam projection system rivals the performance of commercially available single-beam projection directional speakers, and improved steering angle and sound fidelity compared to multi-beamforming performance using traditional parametric arrays.

Eavesdropping on human sound is one of the most common but harmful ways to threaten personal privacy. As one of the most essential accessories, headsets have been widely used in common online conversations, such as online calls, video meetings, etc. The metal coil vibration patterns of headset speakers/microphones have been proven to be highly correlated with the speaker-produced/microphone-received sound content. This paper presents an online conversation eavesdropping system, RFSpy, which uses only one RFID tag attached on a headset to alternately sense the metal coil vibrations of headset speaker and microphone for eavesdropping on speaker-produced and microphone-received sound. In some accessible scenarios, such as meeting rooms, offices, etc., assuming attackers secretly attach a small, battery-free RFID tag under one ear cushion of an eavesdropped user's headset without being noticed. Meanwhile, RFID readers are camouflaged as decorations placed in/out of rooms to transmit and receive RF signals. When the eavesdropped user talks with other users online by using the headset, RFSpy first activates the RFID tag attached on the headset to capture the metal coil vibration patterns of headset speaker and microphone upon RF signals. Then, RFSpy reconstructs sound spectrograms from the RF signal-based vibration patterns for not only trained words but also untrained (i.e., out-of-vocabulary) words by utilizing a designed Sound Spectrogram Reconstruction (SSR) network. Finally, RFSpy converts the sound spectrograms to conversation content through a sound recognition API. Extensive experiments in real environments demonstrate that RFSpy can eavesdrop on online conversations with out-of-vocabulary (OOV) words effectively.

Graph-based social recommendation improves the prediction accuracy of recommendation by leveraging high-order neighboring information contained in social relations. However, most of them ignore the problem that social relations can be noisy for recommendation. Several studies attempt to tackle this problem by performing social graph denoising, but they suffer from 1) adaptability issues for other graph-based social recommendation models and 2) insufficiency issues for user social representation learning. To address the limitations, we propose a model-agnostic graph denoising module (denoted as MADM) which works as a plug-and-play module to provide refined social structure for base models. Meanwhile, to propel user social representations to be minimal and sufficient for recommendation, MADM further employs mutual information maximization (MIM) between user social representations and the interaction graph and realizes two ways of MIM: contrastive learning and forward predictive learning. We provide theoretical insights and guarantees from the perspectives of Information Theory and Multi-view Learning to explain its rationality. Extensive experiments on three real-world datasets demonstrate the effectiveness of MADM.

As the metaverse continues to expand, it becomes increasingly critical to have human-centric networks that are both efficient and high-performing to optimize the user experience. Network modeling plays a fundamental role in optimizing and allocating resources efficiently, and configuring networks to satisfy the demands of diverse applications and users. Recently, traditional queuing theory-based approaches to network modeling have given way to machine learning-based methods. These methods rely on vast amounts of data for building precise models. Although high-precision simulators are ubiquitous, data collection is still an expensive and time-consuming process, resulting in a data bottleneck. In this paper, we propose a weakly supervised learning approach to modeling networks for human-centric networking in the metaverse. Specifically, we identify that queuing theory-based labels can be used to design the supervision signal at a very low cost. Therefore, we propose an approach that combines the inaccurate network modeling obtained from queuing theory-based approaches with an efficient and precise network model through only a small amount of simulation data. To make it a reality, we propose a novel neural network model that combines the powerful graph neural network and transformers. Additionally, we propose several additional supervision signals and a training algorithm to build a better network model. Experimental results demonstrate that our approach reduces the burden of data collection while achieving prediction accuracy comparable to results from large amounts of expensive simulation data. Furthermore, our approach exhibits superior generalization ability.

As one of the most representative applications built on deep learning, audio systems, including keyword spotting, automatic speech recognition, and speaker identification, have recently been demonstrated to be vulnerable to adversarial examples, which have already raised general concerns in both academia and industry. Existing attacks follow the same adversarial example generation paradigm from computer vision, i.e., overlaying the optimized additive perturbations on original voices. However, due to the additive perturbations’ nature on human audibility, balancing the stealthiness and attack capability remains a challenging problem. In this paper, we rethink the stealthiness of audio adversarial examples and turn to introduce another kind of audio distortion, i.e., reverberation, as a new perturbation format for stealthy adversarial example generation. Such convolutional adversarial perturbations are crafted as real-world impulse responses and behave as a natural reverberation for deceiving humans. Based on this idea, we propose AdvReverb to construct, optimize, and deliver phoneme-level convolutional adversarial perturbations on both speech and music carriers with a well-designed objective. Experimental results demonstrate that AdvReverb could realize high attack success rates over 95% on three audio-domain tasks while achieving superior perceptual quality and keeping stealthy from human perception in over-the-air and over-the-line delivery scenarios.

Sequential recommendation is a critical part of the flourishing online applications by suggesting appealing items on users’ next interactions, where global dependencies among items have proven to be indispensable for enhancing the quality of item representations toward a better understanding of user dynamic preferences. Existing methods rely on pre-defined graphs with shallow Graph Neural Networks to capture such necessary dependencies due to the constraint of the over-smoothing problem. However, this graph representation learning paradigm makes them difficult to satisfy the original expectation because of noisy graph structures and the limited ability of shallow architectures for modeling high-order relations. In this paper, we propose a novel Graph Diffusion Representation-enhanced Attention Network for sequential recommendation, which explores the construction of deeper networks by utilizing graph diffusion on adaptive graph structures for generating expressive item representations. Specifically, we design an adaptive graph generation strategy via leveraging similarity learning between item embeddings, automatically optimizing the input graph topology under the guidance of downstream recommendation tasks. Afterward, we propose a novel graph diffusion paradigm with robustness to over-smoothing, which enriches the learned item representations with sufficient global dependencies for attention-based sequential modeling. Moreover, extensive experiments demonstrate the effectiveness of our approach over state-of-the-art baselines.

Desirable information processing in space-terrestrial integrated vehicle networks (STINs) handles data distributed in different satellites while transmitting, where efficient modeling time-varying resources is critical. Existing works are not applicable to STINs, however, because they lack the joint consideration of different movement patterns and fluctuating loads. In this paper, we propose theTime-Varying Resource Graph (TVRG)to model dynamic resources in STINs, by leveraging the advantages of software-defined networking in flexible resource management. Firstly, we propose theSTIN mobility modelto uniformly model different movement patterns in STINs. Then, we propose alayered Resource Modeling and Abstraction (RMA)approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we formulate theTVRG-based Processing on the Way (TPoW)problem for data flows with processing requirements and multiple sources. We propose aMulti-level Processing on the Way (MPoW)approach with a bounded approximation ratio, realizing adaptive matching of resources and demands of processing and transmission. To evaluate the RMA approach, we propose aTVRG-based Routing (TR)algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that our RMA-TR and MPoW outperform most related schemes by decreasing nearly 40% bandwidth consumption with the shortest end-to-end delay.

Gait-based user authentication schemes have been widely explored because of their ability of non-invasive sensing and avoid replay attacks. However, existing gait-based user authentication methods are environment-dependent. In this paper, we present an environment-independent gait-based user authentication system,RFPass, which can identify different individuals leveraging RFID signals. Specifically, we find that Doppler shift of RF signals can describe environment-independent gait features for different individuals. InRFPass, when a user walks through theRFPasssystem, RF signals are first collected by a deployed RFID tag array. Then,RFPassremoves environmental interference from the collected RF signals through a proposed Multipath Direction of arrival (DoA) Signal Select (MDSS) algorithm, and further constructs the environment-independent gait profile. Afterwards, environment-independent gait features are extracted from the constructed gait profile by a proposed CNN-RNN model. Based on the extracted gait features, a hierarchical classifier is trained for user authentication and spoofer detection. Extensive experiments in different real environments demonstrate thatRFPasscan achieve environment-independent gait-based user authentication.

Physical-layer key generation has emerged as a promising avenue for establishing secret keys using reciprocal channel measurements between wireless devices. However, channel reciprocity may suffer degradation from ambient noise and cause mismatched secret bits, while existing methods mitigating this issue may yet face limitations in key efficiency. The root cause behind such limitations is the heavy reliance on channel measurements, which can be naturally susceptible to channel non-reciprocity attributed to environmental factors. Instead of direct key extraction from channel measurements, we seek to share a pre-defined key and utilize channel measurements as a bearer to facilitate key transmission. We propose an accurate and efficient key generation method (KeyCome) to ensure secure key sharing by encapsulating it with channel state information (CSI) obfuscation matrices through circulant convolution. To this end, we develop a reliable key derivation through a quadratic programming method with matrix equilibration, ensuring stable and rapid solutions. Notably, the transmitter can control the key beforehand for enhanced communication efficiency and combine it with an error correction mechanism for accurate key derivation. Furthermore, a lightweight reconciliation scheme is designed to minimize mismatched bits caused by occasional non-reciprocity. Comprehensive experiments demonstrate KeyCome's high accuracy and efficiency in key generation.

The two-factor authentication ($2$FA) has drawn increasingly attention as the mobile devices become more prevalent. For example, the user's possession of the enrolled phone could be used by the$2$FA system as the second proof to protect his/her online accounts. Existing$2$FA solutions mainly require some form of user-device interaction, which may severely affect user experience and creates extra burdens to users. In this work, we propose a secure$2$FA system utilizing the proximity of a user's enrolled phone and the login device as the second proof without requiring the user's interactions. The basic idea of our$2$FA system is to derive location signatures based on acoustic beep signals emitted alternately by both devices and sensing the echoes with microphones, and compare the extracted signatures for proximity detection. Moreover, to further enhance the security of our system, we also design a device authentication scheme which derives the acoustic fingerprint between the login device and enrolled phone to verify the identity of two devices. Given the received beep signal, our system designs a period selection scheme to identify two sound segments accurately: the chirp period is the sound segment propagating directly from the speaker to the microphone whereas the echo period is the sound segment reflected back by surrounding objects. To achieve an accurate proximity detection, we develop a new energy loss compensation extraction scheme by utilizing the extracted chirp periods to estimate the intrinsic differences of energy loss between microphones of the enrolled phone and the login device. Our proximity detection component then conducts the similarity comparison between the identified two echo periods after the energy loss compensation to effectively determine whether the enrolled phone and the login device are in proximity for$2$FA. Moreover, to provide higher security, our device fingerprint-assisted proximity detection further utilizes the overall energy loss between the login device and enrolled phone as their hardware fingerprint to authenticate the identity of two devices. Our experimental results show that our system is accurate in providing$2$FA and robust to both man-in-the-middle (MiM) and co-located attacks across different scenarios and device models.

The indoor location awareness has drawn increasing attention as the mobile apps are used extensively in our daily lives. Existing indoor localization solutions either require a pre-installed infrastructure or can only achieve room-level accuracy, which could not provide a function-location service for mobile devices. In this work, we propose a new active sensing system that enables smartphones to identify some pre-defined indoor locations robustly without requiring any additional sensors or pre-installed infrastructure. The main idea behind our system is to utilize the acoustic signatures, which are derived from the mobile device by emitting a beep signal and selecting its echoes created by dominant reflectors, as the robust fingerprint for location identification. Given the microphone samplings, our system designs a correlation based technique to accurately detect the beginning points of echoes from the received beep signal. To achieve a robust location identification, we develop a new echo selection scheme to select echoes created by dominant reflectors by exploiting the relationships between propagation delays of different orders of echoes. To deal with the variable number of selected echoes, our location identification component then derives histograms from selected echoes and uses the one-against-all SVM classifiers to determine the current location. Our experimental results show that our proposed system is accurate and robust for location identification under various real-world scenarios.

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Moreover, to thwart the possible co-located attacks, our proximity detection component further makes the enrolled phone to generate an active random vibration signal by its built-in motor, and then matches the signal received by the microphone of login device with the signal received by the accelerometer of enrolled phone to verify the proximity of two devices. Our experimental results show that our proposed system is accurate and robust to various attacks across different scenarios and device models.

Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, current CDR methods, including those considering time information, do not jointly model the general and current interests within and across domains, which is pivotal for accurately predicting users’ future interactions. In this article, we propose a Contrastive learning-enhanced Multi-View interest learning model (CMVCDR) for cross-domain sequential recommendation. Specifically, we design a static view and a sequential view to model uses’ general interests and current interests, respectively. We divide a user’s general interest representation into a domain-invariant part and a domain-specific part. A cross-domain contrastive learning objective is introduced to impose constraints for optimizing these representations. In the sequential view, we first devise an attention mechanism guided by users’ domain-invariant interest representations to distill cross-domain knowledge pertaining to domain-invariant factors while reducing noise from irrelevant factors. We further design a domain-specific interest-guided temporal information aggregation mechanism to generate users’ current interest representations. Extensive experiments demonstrate the effectiveness of our proposed model compared with state-of-the-art methods.

Recent years have witnessed enormous research efforts on WiFi sensing to enable intelligent services of Internet of Things. However, due to the omni-directional broadcasting manner of WiFi signals, the activity semantic underlying the signals can be leaked to adversaries for surveillance, as demonstrated by our previous work. In this paper, we further extend the attack capability ofActListenerto impersonation attack, which could eavesdrop on users’ behavioral uniqueness imperceptibly using a WiFi infrastructure in any location of user sensing area. In particular,ActListener detects each human activityand converts the eavesdropped signals to that by legitimate devices based on our proposed signal propagation models. To extract noise-resilient individual behavioral uniqueness from converted CSI of WiFi signals, we further add user identification models into the substitute model set for training the signal pattern calibration generative model. Experimental results demonstrate thatActListenercould achieve over 80% accuracy in activity semantics retrieval and impersonation by using the converted signals.

As the most important incremental component in the advent of the 6G era, Low-Earth-Orbit (LEO) satellite networks are becoming increasingly instrumental, and their integration with Software-Defined Networking (SDN) is progressively recognized as a potent strategy for evolving toward truly service-centric networks, where networks are flexiblely reconstructed based on the service demands. Within such networks, the SDN controllers are responsible for network management by making service-aware resource orchestration. Hence, the placement and assignment of controllers emerge as one of the most critical aspects of the network management service, which becomes particularly challenging when confronted with the unique complexities posed by LEO satellite networks, characterized by their highly dynamic topology and unpredictable load fluctuations. In this paper, for the first time, we tackle the issue of controller placement and assignment with a focus on delivering network management services. Firstly, we formulate theadaptive controller placement and assignmentproblem. Then, we propose thecontrol relation graph (CRG)to capture the control overhead. Next, we present theCRG-based controller placement and assignmentalgorithm and thesliding window based traffic prediction method. Thelookahead-based improvementalgorithm is designed to further decrease management costs. Finally, we conduct a series of theoretical analyses including time complexities. Extensive emulation results demonstrate that our algorithms outperform related schemes in terms of response time and load balancing.

User cold-start recommendation is one of the most challenging problems that limit the effectiveness of recommender systems. Meta-learning-based methods are introduced to address this problem by learning initialization parameters for cold-start tasks. Recent studies attempt to enhance the initialization methods. They first represent each task by the cold-start user and interacted items. Then they distinguish tasks based on the task relevance to learn adaptive initialization. However, this manner is based on the assumption that user preferences can be reflected by the interacted items saliently, which is not always true in reality. In addition, we argue that previous approaches suffer from their adaptive framework (e.g., adaptive initialization), which reduces the adaptability in the process of transferring meta-knowledge to personalized RSs. In response to the issues, we propose a task-difficulty-aware meta-learning with adaptive update strategies (TDAS) for user cold-start recommendation. First, we design a task difficulty encoder, which can represent user preference salience, task relevance, and other task characteristics by modeling task difficulty information. Second, we adopt a novel framework with task-adaptive local update strategies by optimizing the initialization parameters with task-adaptive per-step and per-layer hyperparameters. Extensive experiments based on three real-world datasets demonstrate that our TDAS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/TDAS.

Textual reviews have been widely utilized in recommender systems because text reviews contain rich user preference information. Recent studies have increasingly incorporated textual reviews into user-item graphs as auxiliary information to learn node representations, notably enhancing recommendation performance. However, most existing review-based recommendations could not effectively exploit correlations between ratings and reviews, and suffer from noisy interactions which are further amplified during neighborhood aggregation. To address such limitations, we propose a new graph contrastive learning model for review-based recommendations in this paper. We construct a user-item graph view using both ratings and reviews. In addition, we design two graph views with ratings and reviews, respectively. Through contrastive learning based on these three views, our model manages to generate rich supervision signals for both user and item nodes. Our approach effectively explores intrinsic correlations between heterogeneous rating and review data, which enhance the robustness against interaction noises. A comprehensive experimental study on five benchmark datasets demonstrates that our model outperforms state-of-the-art methods.

Low-power wide-area networks (LPWAN) evolve rapidly with advanced communication primitives (e.g., coding, modulation) being continuously invented. This rapid iteration on LPWAN, however, forms a communication barrier between legacy wireless sensor nodes deployed years ago (e.g., ZigBee-based sensor node) with their latest competitor running a different communication protocol (e.g., LoRa-based IoT node): they work on the same frequency band but share different MAC- and PHY-layer regulations and thus cannot talk to each other directly. To break this barrier, we propose LigBee, a cross-technology communication (CTC) solution that enables symbol-level communication from the latest LPWAN LoRa node to legacy ZIGBEE node. We have implemented LigBee on both software-defined radios and commercial-off-the-shelf (COTS) LoRa and ZigBee nodes, and demonstrated that LigBee builds a reliable CTC link from LoRa node to ZigBee node on both platforms. Our experimental results show that i) LigBee achieves a bit error rate (BER) in the order of 10−3with 70 ∼ 80% frame reception ratio (FRR), ii) the range of LigBee link is over 300m, which is 6 ∼ 7.5× the typical range of legacy ZigBee and state-of-the-art solution, and iii) the throughput of LigBee link is maintained on the order of kbps, which is close to the LoRa’s throughput.

Motivated by many potential applications that could be enabled by acoustic motion tracking, in this paper we systematically examine the factors that limit the accuracy of acoustic tracking in practical scenarios. We identify three main challenges: (i) high mobility, (ii) low SNR, and (iii) hardware frequency response. We further show that the last two issues may exacerbate the performance issue under high mobility. We develop effective approaches to address the issues. In particular, to address high mobility, we tackle phase wrap-around using the derivative of the phase; we further estimate the Doppler shift under diverse scenarios and compensate the Doppler in channel impulse response (CIR). To address low SNR, we use a novel approach to estimate the phase shift between consecutive time intervals to effectively support time-domain beamforming and increase SNR. To tackle the uneven frequency response, we show that it is important to estimate and compensate the phase as well as the amplitude of the frequency response. Our extensive evaluation shows that each of our techniques is effective and putting them together significantly enhances the accuracy of acoustic motion tracking in general scenarios.

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods.

Recent years have witnessed the emerging market of 3D facial reconstruction that supports numerous face-driven scenarios including modeling in virtual reality (VR), human-computer interaction, and affective computing applications. Current mainstream approaches rely on vision for 3D facial reconstruction, which may encounter privacy concerns and suffer from obstruction scenes and bad lighting conditions. In this paper, we present a nonintrusive 3D facial reconstruction system, mm3DFace, which leverages a millimeter wave (mmWave) radar to reconstruct 3D human faces that continuously express facial expressions in a privacy-preserving and passive manner. Based on the pre-processed mmWave signals, mm3DFace first extracts facial geometric features that capture subtle changes in facial expressions through a ConvNeXt model with triple loss embedding. Then, mm3DFace derives distance and orientation-robust facial shapes with 68 facial landmarks using region-divided affine transformation. mm3DFace next reconstructs facial expressions through a designed regional amplification method and finally generates 3D facial avatars that continuously express facial expressions. Extensive experiments involving 15 participants in real-world environments show that mm3DFace can accurately track 68 facial landmarks with 3.94% normalized mean error, 2.30mm mean absolute error, and 4.10mm 3D-mean absolute error, which is effective and practical in real-world 3D facial reconstruction.

Over the past years, smart ecology has attracted much attention, especially for smart home applications. As a key component, monitoring appliances performs significant impact. However, appliances under monitoring usually contain smart modules such as WiFi or Bluetooth, which are limited to traditional appliances. Existing approaches such as distributed sensing, energy disaggregation, and infrastructure-mediated sensing, require the installation of external hardware or have a limited sensing range. In this study, we developed AUDIOSENSE to leverage the acoustic signal generated by the power supply to monitor electrical appliances throughout the house remotely from a single point. In realizing AUDIOSENSE, we proposed an optimized Variation Mode Decomposition scheme to extract the frequency components, as well as a data augmentation scheme to improve generalizability and enable multi-label classification. In experiments, AUDIOSENSE achieved mAP values of 99.3% in multi-label classification.

Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined POI graphs to capture such indispensable correlations for modeling user preferences, assuming that the appropriate geographical dependencies among POIs could be pre-determined. However, the pre-defined graph structures are always far from the optimal graph topology due to noise and adaptability issues, which may decrease the expressivity of learned POI representations as well as the credibility of modeling user preferences. In this paper, we propose a novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. In particular, we develop an adaptive POI graph matrix and learn it via similarity learning with POI embeddings, automatically capturing the underlying geographical dependencies for representation learning. Afterward, we incorporate the learned representations of POIs and personalized spatial-temporal information with an extension to the self-attention mechanism for capturing dynamic user preferences. Extensive experiments conducted on two real-world datasets validate the superior performance of our proposed method over state-of-the-art baselines.

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model.

Recent advances in traffic signal control have witnessed the success of reinforcement learning. However, most of these approaches have focused on vehicle traffic and lack consideration for pedestrians. This can be attributed in part to the fact that the existing underlying technologies are not yet practical to deploy in real-world environments. Vision technologies, for example, can easily be obscured from view in reality. The direction of movement and position of pedestrians is difficult to estimate accurately. The emergence of 6G localization and tracking services offer new opportunities. With this base service, we intend to improve the efficiency, safety, and scalability of multi-intersection traffic signal control with mixed traffic flows. This problem is challenging for its coordination, scalability, and access of new traffic. To solve these challenges, we propose PV-TSC, a distributed reinforcement learning motivated traffic signal control with pedestrian access. We analyze different behaviors of pedestrian traffic, and integrate pedestrian traffic with the proven traffic signal control scheme for vehicle traffic. Finally, we conduct simulation experiments to illustrate the superiority of PV-TSC against classic methods, and further analyze the effectiveness of PV-TSC design by exploring its variants.

Graph Convolutional Networks (GCNs) have been widely used for collaborative filtering, due to their effectiveness in exploiting high-order collaborative signals. However, two issues have not been well addressed by existing studies. First, usually only one kind of information is utilized, i.e., user preference in user-item graphs or item dependency in item-item graphs. Second, they usually adopt static graphs, which cannot retain the temporal evolution of the information. These can limit the recommendation quality. To address these limitations, we propose to mine three kinds of information (user preference, item dependency, and user behavior similarity) and their temporal evolution by constructing multiple discrete dynamic heterogeneous graphs (i.e., a user-item dynamic graph, an item-item dynamic graph, and a user-subseq dynamic graph) from interaction data. A novel network (PDGCN) is proposed to learn the representations of users and items in these dynamic graphs. Moreover, we designed a structural neighbor aggregation module with novel pooling and convolution operations to aggregate the features of structural neighbors. We also design a temporal neighbor aggregation module based on self-attention mechanism to aggregate the features of temporal neighbors. We conduct extensive experiments on four real-world datasets. The results indicate that our approach outperforms several competing methods in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Dynamic graphs are also shown to be effective in improving recommendation performance.

User cold-start scenarios pose great challenges to recommendation systems in accurately capturing user preferences with sparse interaction records. Besides incorporating auxiliary information to enrich user/item representations, recent studies under the schema of meta-learning focus on quickly adapting personalized recommendation models based on cold-start users’ scarce interactions. The majority of meta-learning based recommendation methods follow a bi-level optimization paradigm and learn globally shared initialization across all cold-start recommendation tasks. In addition, to further facilitate the ability of fast adaptation, existing methods have made efforts to tailor task-specific prior knowledge by identifying the individual characteristics of each task. However, we argue that multi-view commonalities between existing users and cold-start users are also essential for precisely distinguishing new tasks, but not comprehensively modeled in previous studies. In this article, we propose a multifaceted relation-aware meta-learning approach namely MeCM for user cold-start recommendation, which enhances task-adaptive initialization customization by extracting multiple views of task relevance. We design a dual customization framework consisting of two successive phases including cluster-level customization and task-level customization. Specifically, MeCM first extracts multifaceted semantic relations between tasks and refines task commonalities into task clusters maintained with memory networks (MNs). Globally learned fast weights corresponding to task clusters are queried to perform cluster-level customization. Then task-level customization is triggered based on contextual information of the target task via interaction-wise encoding. Extensive experiments on real-world datasets demonstrate the superior performance of our model over state-of-the-art meta-learning-based recommendation methods.

Many remote sensing applications, such as forest fire monitoring, need to send a large volume of data to the ground with low delay. Therefore, the cooperation transmission, which relies on cooperation among satellites to achieve continuous transmission, emerges as an indispensable technique. Most existing work cannot minimize the delay through dynamic cooperation transmission. In this paper, we investigate how to minimize the delay in remote sensing satellite networks based on cooperation transmission, where cooperation hotspots refer to the satellites with ground-satellite links to the Earth Stations (ESs). First, we propose the cooperation capability model to quantify capabilities of cooperation hotspots. Then, we formulate the satellite cooperation transmission problem and prove its NP-hardness. To solve the problem, we propose the delay-minimized cooperation transmission scheme. Both CCT and DCT algorithms adapt well to the dynamic topology and time-varying available resources. Finally, we formally analyze the approximation ratios and the time complexities of both algorithms. We also prove that the DCT always setups loop-free paths. NS2-based simulation results demonstrate that our schemes have good scalability, and both CCT and DCT algorithms reduce the end-to-end delay on average by more than 21.77%, and significantly improve throughput, packet loss rate and flow completion time.

The controller provisioning, which adjusts the number, locations, and members of satellite controllers adaptive to the dynamic network load and topology, fundamentally impacts the performance of software-defined satellite networks (SDSNs). An ideal provisioning strategy should achieve a low total control overhead throughout the entire satellite operation period, which is extremely challenging since the network loadcan only be predicted in a short time scale. Existing methods can hardly achieve this goal for they greedily configure controllers in each time slot, where switches have to frequently migrate from one controller to another. In this paper, we focus on achievingglobally optimized strategieswith onlycurrent network load information. We first propose a comprehensive control overhead model and formulate theControllerProvisioningProblem (CPP)in SDSNs as a non-convex integer programming problem. To solve the problem, we propose an approximate algorithm named AROA by introducing a regularization framework and based on randomized rounding. We theoretically derive its competitive ratio. To produce strategies in time for future large satellite constellations, we further propose a more efficient heuristic algorithm HROA. Evaluations on our built simulation system show that our proposed methods significantly outperform related schemes in control overhead, latency, and scalability.

Near-field communication (NFC) technology emerges as a vital role with appealing benefits for users to improve mobile device’s functionality. Although today’s most smartphones and smartwatches come with NFC support, other mobile devices (e.g., PC and laptops) and IoT devices that don’t equip with dedicated radio modules cannot take advantage of wide-scale NFC capability. We design and developMagneComm+, an NFC-like implementation scheme without dedicated hardware and propose a novel near-field communication protocol that is applicable to almost all mobile devices and IoT devices. The key idea is to utilize the electromagnetic induction (EMI) signal emitted from the computing devices (e.g., CPUs) and captured by magnetometers on mobile devices for communication. We tackle challenges indata encoding/decoding,preamble detection,retransmission and error correction,multi-transmitter, andfull-duplexschemes, to efficiently generate and reliably receive EMI signal with the hardware available on devices. We prototypeMagneComm+on both between laptops and smartphones, as well as between two laptops with an external magnetometer. Extensive evaluation results show that ourMagneComm+supports around$10~cm$10cmcommunication distance with average110 bps(bit per second) data rate on the normal-speed mode, and maximum17.28 kbpson the full-speed mode.

Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Since the early 2010s, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey article, we first proposed a two-level taxonomy of cross-domain recommendation that classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.

User authentication nowadays has become an important support for not only security guarantees but also emerging novel applications. Although WiFi signal-based user authentication has achieved initial success, it works in single-user scenarios while multi-user authentication remains a challenging task. In this paper, we present MultiAuth, a multi-user authentication system that can authenticate multiple users with a single pair of commodity WiFi devices. The basic idea is to profile multipath components of WiFi signals, and leverage the multipath components to characterize each user individually for multi-user authentication. MultiAuth first profiles multipath components of WiFi signals through a proposed MUltipath Time-of-Arrival estimation algorithm (MUTA). Then, after matching corresponding multipath components to each user in complex multi-user scenarios, MultiAuth constructs individual CSI based on the multipath components to characterize each user individually. An AoA-based approach is exploited to further separate individual CSI constructed by the users with same ToA. To identify users through their activities, MultiAuth extracts user behavior profiles based on the individual CSI, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that MultiAuth is effective in multi-user authentication with 86.2% average accuracy and 9.5% average false accept rate.

Quick response (QR) codes have been widely used in mobile applications, especially mobile payments, such as Alipay, WeChat, PayPal, etc due to their convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported and attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose ScreenID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In ScreenID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types, and manufacturers and thus improve the security of QR codes.

A urine bag is a common medical appliance widely used in clinical treatment for patients with urinary frequency, urgency, or incontinence problems. The real-time monitoring and alarming of the liquid volume in the bag are necessary to avoid the infection caused by urine reflux. Current monitoring relies on manual observations of doctors and medical attendants, but it has problems with timeliness, especially in surgery or at night. In this paper, we propose Wi-Uro, a real-time, low-cost, and accurate urine bag monitoring and alarming system with commodity Wi-Fi devices. To solve the random deformation of urine bags possibly due to human postures, it integrates Wi-Fi Channel State Information (CSI) with tension sensor readings to infer liquid volume in bags. Once the volume is exceeding the threshold, Wi-Uro system will prompt the alarm information immediately. Evaluations based on real urine bags show a satisfactory estimation accuracy (over 95%), which means that Wi-Uro can be applied in routine patient care.

It is unpractical to conduct full-size data collection in ubiquitous IoT data systems due to the energy constraints of IoT sensors and large system scales. Although sparse sensing technologies have been proposed to infer missing data based on partial sampled data, they usually focus on data inference while neglecting the sampling process, restraining the inference efficiency. In addition, their inferring methods highly depend on data linearity correlations, which become less effective when data are not linearly correlated. In this paper, we propose, Compact IOT Data CollEction, namely CODE, to conduct precise data matrix sampling and efficient inference. Particularly, CODE integrates two major components, i.e., cluster-based matrix sampling and Generative Adversarial Networks (GAN)-based matrix inference, to reduce the data collection cost and guarantee the data benefits, respectively. In the sampling component, a cluster-based sampling approach is devised, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. For the inference component, a GAN-based model is developed to estimate the full matrix, which consists of a generator network that learns to generate a fake matrix, and a discriminator network that learns to discriminate the fake matrix from the real one. A reference implementation of CODE is conducted under three operational large-scale IoT systems, and extensive data-driven experiment results are provided to demonstrate its efficiency and robustness.

PIN inference attack leveraging keystroke-induced side-channel information poses a substantial threat to the security of people’s privacy and properties. Among various PIN inference attacks, video-assisted method provide more intuitive and robust side-channel information to infer PINs. But it usually requires there is no visual occlusion between the attacker and the victims or their hand gestures, making the attackers either easy to expose themselves or inapplicable to the scenarios such as ATM or POS terminals. In this paper, we present a novel and practical video-assisted PIN inference system, ArmSpy, which infers victim’s PIN by observing from behind the victims in a stealthy way. Specifically, ArmSpy explores the subtle keystroke-induced arm posture changes, including elbow bending angle changes and the spatial relationship between different arm joints, to infer the PIN entries. We develop the keystroke inference mechanism to detect the keystroke events and pinpoint the keystroke positions, and then accurately infer the PINs with the proposed inferred PIN coordination mechanism. Extensive experimental results demonstrate that ArmSpy can achieve over 67% average accuracy on inferring the PIN with 3 attempts and even over 80% for some victims, indicating the severity of the threat posed by ArmSpy.

With the development of smart indoor environments, user authentication becomes an essential mechanism to support various secure accesses. Although recent studies have shown initial success on authenticating users with human activities or gestures using WiFi, they rely on predefined body gestures and perform poorly when meeting undefined body gestures. This work aims to enable WiFi-based user authentication with undefined body gestures rather than only predefined body gestures, i.e., realizing a gesture-independent user authentication. In this paper, we first explore physiological characteristics underlying body gestures, and find that statistical distributions under WiFi signals induced by body gestures can exhibit invariant individual uniqueness unrelated to specific body gestures. Inspired by this observation, we propose a user authentication system, which utilizes WiFi signals to identify individuals in a gesture-independent manner. Specifically, we design an adversarial learning-based model, which suppresses specific gesture characteristics, and extracts invariant individual uniqueness unrelated to specific body gestures, to authenticate users in a gesture-independent manner. Extensive experiments in indoor environments show that the proposed system is feasible and effective in gesture-independent user authentication.

Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from various source, which is ideal for spatial MIMO. Hence, OCC can be applied to several emerging application scenarios, such as vehicle and drone communications. However, distance is a major bottleneck for OCC system, because the increase in distance makes it difficult for the camera to distinguish adjacent LEDs, which we call LED spatial mixing.In this paper, we propose a novel hierarchical coding scheme named as OnionCode to support dynamic range of channel capacity in one-to-many OCC scenario. OnionCode adopts a multi-priority receiving scheme, i.e., the receivers can dynamically discard the low-priority bit streams according to the measured channel capacity. OnionCode achieves this based on a key insight that, the luminance level of a mix-LED is distinguishable. We prototype a LED-based OCC system to evaluate the efficacy of OnionCode and the results show that OnionCode achieves a higher coding efficiency and overall throughput compared with the existing hierarchical coding.

The continuously increasing time spent on car trips in recent years brings growing attention to the physical and mental health of drivers on roads. As one of the key vital signs, the heartbeat is a critical indicator of drivers' health states. Most existing studies on heartbeat monitoring either require sensor attachment or could only provide sketchy heart rates. Moreover, most approaches require the subject to remain stationary or a quiet measuring environment, which is hard to apply to dynamic driving environments. In this paper, we propose a contactless cardiac cycle monitoring system, mmECG, which leverages Commercial-Off-The-Shelf mmWave radar to estimate the fine-grained heart movements of drivers in moving vehicles. By exploring the principle of mmWave signal-based sensing, we first perform studies in static environments and find the fine-grained heart movements, represented as stages of atria and ventricles in repetitive cardiac cycles, can be captured by the FMCW-based mmWave radar as phase changes in signals. Whereas in driving environments, such phase changes are caused and influenced by not only the heartbeat of drivers but also driving operations and vehicle dynamics. To further extract the minute heart movements of drivers and eliminate other influences in phase changes, we construct a movement mixture model to represent the phase changes caused by different movements, and further design a hierarchy variational mode decomposition (VMD) approach to extract and estimate the essential heart movement in mmWave signals. Finally, based on the extracted phase changes, mmECG reconstructs the cardiac cycle by estimating fine-grained movements of atria and ventricles leveraging a template-based optimization method. Experimental results involving 25 drivers in real driving scenarios demonstrate that mmECG can accurately estimate not only heart rates but also cardiac cycles of drivers in real driving environments.

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma while enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility for human participants and not adaptive to informed attacks. In this poster, we propose a non-intrusive and adaptive speaker de-identification scheme to balance the privacy and utility of voice services. We generate adversarial examples to conceal user identity from exposure by Automatic Speaker Identification (ASI). By learning a compact distribution with a conditional variational auto-encoder, our system enables on-demand target sampling and diverse identity transformation. We also introduce the acoustic masking effect to construct inaudible perturbations, thus preserving the speech content and perceptual quality. Experiments on 50 speakers show our system could achieve 98.2% successful de-identification on 4 mainstream ASIs with an objective perceptual quality of 4.38 and a subjective mean opinion score of 4.56.

Nowadays, the market of 3D human posture tracking has extended to a broad range of application scenarios. As current mainstream solutions, vision-based posture tracking systems suffer from privacy leakage concerns and depend on lighting conditions. Towards more privacy-preserving and robust tracking manner, recent works have exploited commodity radio frequency signals to realize 3D human posture tracking. However, these studies cannot handle the case where multiple users are in the same space. In this paper, we present a mmWave-based multi-user 3D posture tracking system, m3Track, which leverages a single commercial off-the-shelf (COTS) mmWave radar to track multiple users' postures simultaneously as they move, walk, or sit. Based on the sensing signals from a mmWave radar in multi-user scenarios, m3Track first separates all the users on mmWave signals. Then, m3Track extracts shape and motion features of each user, and reconstructs 3D human posture for each user through a designed deep learning model. Furthermore. m3Track maps the reconstructed 3D postures of all users into 3D space, and tracks users' positions through a coordinate-corrected tracking method, realizing practical multi-user 3D posture tracking with a COTS mmWave radar. Experiments conducted in real-world multi-user scenarios validate the accuracy and robustness of m3Track on multi-user 3D posture tracking.

Gait-based user authentication schemes have been widely explored because of their ability of non-invasive sensing and avoid replay attacks. However, existing gait-based user au-thentication methods are environment-dependent. In this paper, we present an environment-independent gait-based user authen-tication system, RFPass, which can identify different individuals leveraging RFID signals. Specifically, we find that Doppler shift of RF signals can describe environment-independent gait features for different individuals. In RFPass, when a user walks through the RFPass system, RF signals are first collected by a deployed RFID tag array. Then, RFPass removes environmental interfer-ence from the collected RF signals through a proposed Multipath Direction of arrival (DoA) Signal Select (MDSS) algorithm. Next, we construct an environment-independent gait profile to describe the user's walking movements. Afterward, environment-independent gait features are extracted by a proposed CNN-RNN model. Based on the extracted gait features, a trained model is constructed for user authentication and spoofer detection. Extensive experiments in different real environments demonstrate that RFPass can achieve environment-independent gait-based user authentication.

The integration of deep learning on Speaker Recognition (SR) advances its development and wide deployment, but also introduces the emerging threat of adversarial examples. However, only a few existing studies investigate its practical threat in physical domain, which either evaluate its feasibility only by directly replaying generated adversarial examples, or explore the partial channel interference for robustness improvement. In this paper, we propose a physical adversarial example attack, PhyTalker, which could generate and inject perturbations on voices in a live-streaming manner on attacking various SR models in different physical channels. Compared with the typical adversarial example for digital attacks, PhyTalker generates a subphoneme-level perturbation dictionary to decouple the perturbation optimization and injection. Moreover, we introduce the channel augmentation to compensate both device and environmental distortions, as well as model ensemble to improve the perturbation transferability. Finally, PhyTalker recognizes and localizes the latest recorded phoneme to determine the corresponding perturbations for real-time broadcasting. Extensive experiments are conducted with a large-scale corpus in real physical scenarios, and results show that PhyTalker achieves an overall Attack Success Rate (ASR) of 85.5% in attacking mainstream SR systems and Mel Cepstral Distortion (MCD) of 2.45dB in human audibility.

Traditional one-time user authentication is vulnerable to attacks when an adversary can obtain unauthorized privileges after a user’s initial login. Continuous user authentication (CA) has recently shown its great potential by enabling seamless user authentication with few users’ participation. We devise a low-cost system that can exploit users’ pulsatile signals from photoplethysmography (PPG) sensors in commodity wearable devices to perform CA. Our system requires zero user effort and applies to practical scenarios that have nonclinical PPG measurements with human motion artifacts (MAs). We explore the uniqueness of the human cardiac system and develop adaptive MA filtering methods to mitigate the impacts of transient and continuous activities from daily life. Furthermore, we identify general fiducial features and develop an adaptive classifier that can authenticate users continuously based on their cardiac characteristics with little additional training effort. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks. We show that our MA mitigation approaches can improve the CA accuracy by around 39% under both transient and continuous daily activity scenarios.

Researchers have proposed many novel methods to detect abnormal taxi trajectories. However, most of the existing methods usually adopt a counting-based strategy, which may cause high false positives due to imprecisely identifying diverse trajectories as anomalies and therefore, they need the support of large-scale historical trajectories to work properly. To improve detection precision and efficiency, in this article, we propose STR, an online abnormal taxi trajectory detection method based on spatio-temporal relations. The basic principle behind STR is that given the displacement from the source point to a testing point, if the driving time and driving distance are not within the normal ranges, the point is identified as anomalous. To learn the two normal ranges for driving time and driving distance, STR defines two spatio-temporal models which characterize the relationship between displacement and driving distance/driving time. To improve detection efficiency, STR reduces the number of models that need to be learned by making full use of the similarity of transportation modes in different time periods and neighboring areas. The effectiveness and performance of STR are evaluated on real-world taxi trajectories. The experiment results show that compared with counting-based methods, STR achieves greater precision by reducing false positives. Furthermore, STR is more efficient than its counterparts and is suitable for online detection.

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints that encode heterogeneous behaviors continuously. In this article, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of Long-Short Term Memory (LSTM) and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.

Given the increasing amount of time people spent on driving, the physical and mental health of drivers is essential to road safety. Breathing patterns are critical indicators of the wellbeing of drivers on the road. Existing studies on breathing monitoring require active user participation of wearing special sensors or relatively quiet environments during sleep, which are hardly applicable to noisy driving environments. In this work, we propose a fine-grained breathing monitoring system,BreathListener, which leverages audio devices on smartphones to estimate the fine-grained breathing waveform in driving environments. By investigating the data collected from real driving environments, we find that energy spectrum density (ESD) of acoustic signals can be utilized to capture breathing procedures in driving environments. To extract breathing pattern in ESD signals,BreathListenereliminates interference from driving environments in ESD signals utilizing background subtraction and variational mode decomposition (VMD). After that, the extracted breathing pattern is transformed into Hilbert spectrum, and we further design a deep learning architecture based on generative adversarial network (GAN) to generate fine-grained breathing waveform from the Hilbert spectrum of extracted breathing patterns in ESD signals. Experiments with ten drivers in real driving environments show thatBreathListenercan accurately capture breathing patterns of drivers in driving environments.

Existing Space-Terrestrial Integrated Network (STIN) applications collect all data from multiple satellites and terrestrial nodes to the specific analyze center on the earth for processing, which wastes lots of network resources. To save these resources, we propose a novelprocessing-while-transmittingpattern in the SDN-based STIN architecture. Through a logically centralized control plane, it cooperatively processes a complex task on appropriate nodes during data transmission. Here, the key point is to jointly determine the transmission path and place subtasks adaptive to data distributions, heterogeneous link costs, task characteristics, the dynamic topology, and network resources. In this paper, we firstly formulate theTransmission-cost-minimized joint Routing and Tasks placement Problem (TRTP)in time-varying STINs. We prove it is NP-hard and has no Polynomial-Time Approximation Scheme (PTAS). To solve the problem, we propose theJoint Routing and Task Placement (JRTP)algorithm. It first converts the time-varying STIN to a stable graph to cope with the network dynamics, according to the topology and resources during task processing. Then, it jointly decides the routing and task placement through atask-topology graph model, which converts the TRTP problem on the stable graph to the classic shortest path problem. We prove that the performance of JRTP is bounded in cases when transmission resources are sufficient and further improve it through the idea of reinforcement. The experimental results show that our processing pattern can significantly decrease the transmission cost and delay, and our algorithms outperform most related ones.

Recent years have witnessed the rapid development of automatic speech recognition (ASR) systems, providing a practical voice-user interface for widely deployed smart devices. With the ever-growing deployment of such an interface, several voice-based attack schemes have been proposed towards current ASR systems to exploit certain vulnerabilities. Posing one of the more serious threats,hidden voice attack uses the human-machine perception gap to generate obfuscated/hidden voice commands that are unintelligible to human listeners but can be interpreted as commands by machines. However, due to the nature of hidden voice commands (i.e., normal and obfuscated samples exhibit a significant difference in their acoustic features), recent studies show that they can be easily detected and defended by a pre-trained classifier, thereby making it less threatening. In this paper, we validate that such a defense strategy can be circumvented with a more advanced type of hidden voice attack calledHVAC. Our proposed HVAC attack can easily bypass the existing learning-based defense classifiers while preserving all the essential characteristics of hidden voice attacks (i.e., unintelligible to humans and recognizable to machines). Specifically, we find that all classifier-based defenses build on top of classification models that are trained with acoustic features extracted from the entire audio of normal and obfuscated samples. However, only speech parts (i.e., human voice parts) of these samples contain the useful linguistic information needed for machine transcription. We thus propose a fusion-based method to combine the normal sample and corresponding obfuscated sample as a hybrid HVAC command, which can effectively cheat the defense classifiers. Moreover, to make the command more unintelligible to humans, we tune the speed and pitch of the sample and make it even more distorted in the time domain while ensuring it can still be recognized by machines. Extensive physical over-the-air experiments demonstrate the robustness and generalizability of our HVAC attack under different realistic attack scenarios. Results show that our HVAC commands can achieve an average 94.1% success rate of bypassing machine-learning-based defense approaches under various realistic settings.

Non-parallel text style transfer aims to rephrase a sentence with another style while reserving its content relying on non-parallel data. Most existing methods can be divided into two groups: 1) separating content and style of the input text and 2) directly modeling the style transfer process. To the best of our knowledge, all these existing works lack fine-grained supervisory signals during training, which leads to difficulty in achieving a good balance between content preservation and style satisfaction. However, the study on supervisors which could provide fine-grained supervisory signals for training transfer models has received relatively less attention. Thus, we propose a self-attentional discriminator and a training strategy for training an attentional transfer model by leveraging the fine-grained supervisory signals from the proposed discriminator. Specifically, our discriminator provides token-wise style/content weights by performing self-attention between the sentence vector and the token embeddings, which forms a shortcut in back-propagation leading to more accurate gradients. The style/content weights also pose a better content alignment constraint and improve the interpretability of the training procedure by identifying stylized tokens. The training of our transfer model is end-to-end via Gumbel-Softmax with the pre-trained discriminator. Experiments on two datasets with automatic and human evaluations as well as theoretical and empirical analysis demonstrate the effectiveness of our method.1

Sequential recommendation which aims to predict a user's next interaction based on his/her previous behaviors, has attracted great attention. Recent studies mainly employ deep recurrent neural networks or self-attention networks to capture dynamic user preferences. However, existing methods merely focus on modeling users' clear interests in interacted items. We argue that for an interaction, the user may also have ambiguous interests in items that are semantically related to the interacted one. For comprehensively capturing user preferences, it is beneficial to discover potential interests from historical interactions at a broader itemset level. Therefore, in this paper, we propose a knowledge graph enhanced sequential recommendation model namely KGIE, which focuses on enhancing user interest modeling with knowledge-enriched itemsets by incorporating the knowledge graph. Specifically, in addition to item-level interest modeling with interacted items, we further construct knowledge-enriched itemsets that are extracted via high-order knowledge associations with the interacted items. For capturing personalized itemset-level interests, we design an attentive aggregation unit to combine item embeddings considering both inherent and contextual personalization signals. Furthermore, to balance the contributions of both two levels of interest modeling, we adaptively learn high-level preference representations with a gating fusion unit. Extensive experiments on three real-world datasets demonstrate the superior performance beyond state-of-the-art methods and recommendation interpretability of our model.

The booming of mobile technologies and Internet of Things (IoTs) have facilitated the explosion of wireless devices and brought convenience to people's daily lives. Coming with the explosive growth of wireless devices, incompatibility of heterogeneous wireless technologies hindered the growing demands for everything connected. And spectrum sharing among heterogeneous wireless technologies has led to severe Cross-Technology Interference (CTI), which is a vital obstacle for network reliability and spectrum utilization. Researches in recent years have shown that Cross-Technology Communication (CTC) turns out to be a promising solution with broad perspective for the coexistence of heterogeneous wireless technologies. However, due to the physical layer incompatibility of WiFi and Bluetooth Low Energy (BLE), the researches about CTC between these two most wildly used wireless technologies are limited by now. In this paper, we propose WiBWi, a payload encoding-based bidirectional CTC scheme between BLE and WiFi, which can achieve near-optimal throughput and powerful robustness. For uplink, i.e., BLE to WiFi communication, WiBWi leverages a novel extended WiFi preamble detection rule and probabilistic inference based encode mapping to achieve fast and reliable communication. For downlink, i.e., WiFi to BLE communication, WiBWi introduces an encoding mapping scheme in the sight of BLE receiver with little modification to accomplish high throughput and robustness. Extensive evaluation shows that WiBWi can offer near-optimal throughput (near the maximum throughput of BLE) and extremely low bit error rate (less than 1%).

It is critical but difficult to efficiently model re-sources in space-terrestrial integrated networks (STINs). Existing work is not applicable to STINs because they lack the joint consideration of different movement patterns and fluctuating loads. In this paper, we propose the time-varying resource graph (TVRG) to model STINs from the resource perspective. Firstly, we propose the STIN mobility model to uniformly model different movement patterns in STINs. Then, we propose a layered Resource Modeling and Abstraction (RMA) approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we propose an efficient TVRG-based Resource Scheduling (TRS) algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that the RMA-TRS outperforms related schemes in terms of throughput, end-to-end delay and flow completion time.

Quick response (QR) codes have been widely used in mobile applications due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported that attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose SCREENID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In SCREENID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, SCREENID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that SCREENID can differentiate screens of different models, types, and manufacturers, thus improve the security of QR codes.

Augmented reality/virtual reality (AR/VR) has extended beyond 3D immersive gaming to a broader array of applications, such as shopping, tourism, education. And recently there has been a large shift from handheld-controller dominated interactions to headset-dominated interactions via voice interfaces. In this work, we show a serious privacy risk of using voice interfaces while the user is wearing the face-mounted AR/VR devices. Specifically, we design an eavesdropping attack, Face-Mic, which leverages speech-associated subtle facial dynamics captured by zero-permission motion sensors in AR/VR headsets to infer highly sensitive information from live human speech, including speaker gender, identity, and speech content. Face-Mic is grounded on a key insight that AR/VR headsets are closely mounted on the user's face, allowing a potentially malicious app on the headset to capture underlying facial dynamics as the wearer speaks, including movements of facial muscles and bone-borne vibrations, which encode private biometrics and speech characteristics. To mitigate the impacts of body movements, we develop a signal source separation technique to identify and separate the speech-associated facial dynamics from other types of body movements. We further extract representative features with respect to the two types of facial dynamics. We successfully demonstrate the privacy leakage through AR/VR headsets by deriving the user's gender/identity and extracting speech information via the development of a deep learning-based framework. Extensive experiments using four mainstream VR headsets validate the generalizability, effectiveness, and high accuracy of Face-Mic.

With the increasing integration of humans and the cyber world, user authentication becomes critical to support various emerging application scenarios requiring security guarantees. Existing works utilize Channel State Information (CSI) of WiFi signals to capture single human activities for non-intrusive and device-free user authentication, but multi-user authentication remains a challenging task. In this paper, we present a multi-user authentication system, MultiAuth, which can authenticate multiple users with a single commodity WiFi device. The key idea is to profile multipath components of WiFi signals induced by multiple users, and construct individual CSI from the multipath components to solely characterize each user for user authentication. Specifically, we propose a MUltipath Time-of-Arrival measurement algorithm (MUTA) to profile multipath components of WiFi signals in high resolution. Then, after aggregating and separating the multipath components related to users, MultiAuth constructs individual CSI based on the multipath components to solely characterize each user. To identify users, MultiAuth further extracts user behavior profiles based on the individual CSI of each user through time-frequency analysis, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that MultiAuth is accurate and reliable for multi-user authentication with 87.6% average accuracy and 8.8% average false accept rate.

Air pollution is an important environmental issue of increasing concern, which impacts human health. Accurate air quality prediction is crucial for avoiding people suffering from serious air pollution. Most of the prior works focus on capturing the temporal trend of air quality for each monitoring station. Recent deep learning based methods also model spatial dependencies among neighboring stations. However, we observe that besides geospatially adjacent stations, the stations which share similar functionalities or consistent temporal patterns could also have strong dependencies. In this paper, we propose an Attentive Temporal Graph Convolutional Network (ATGCN) to model diverse inter-station relationships for air quality prediction of citywide stations. Specifically, we first encode three types of relationships among stations including spatial adjacency, functional similarity, and temporal pattern similarity into graphs. Then we design parallel encoding modules, which respectively incorporate attentive graph convolution operations into the Gated Recurrent Units (GRUs) to iteratively aggregate features from related stations with different graphs. Furthermore, augmented with an attention-based fusion unit, decoding modules with a similar structure to the encoding modules are designed to generate multi-step predictions for all stations. The experiments on two real-world datasets demonstrate the superior performance of our model beyond state-of-the-art methods.

Abstract Data trading has attracted increasing attention over the years as a cost-effective business paradigm, probably producing a tremendous amount of economic value. However, the study of query-based trading in the user data market is still in the initial stage. To design a practical user data trading mechanism, we have to consider three major challenges: privacy concern, compensation cost minimization and revenue maximization in a Bayesian environment. By jointly considering these challenges, we propose a profit-maximizing mechanism for user data trading with personalized differential privacy, called READ, which comprised two components, READ-COST for cost minimization and READ-REV for revenue maximization. Especially, READ adopts personalized differential privacy to satisfy each data owner’s diverse privacy preferences. READ-COST greedily selects the most cost-effective data owner to achieve the sub-optimal data query cost. Given this query cost, READ-REV calculates the maximum expected revenue in a Bayesian setting. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that READ achieves all desired properties and approaches the optimal profit.

Abstract Human–computer interaction through touch screens plays an increasingly important role in our daily lives. Besides smartphones and tablets, laptops are the most prevalent mobile devices for both work and leisure. To satisfy the requirements of some applications, it is desirable to re-equip a typical laptop with both handwriting and drawing capability. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, to accurately track hand movements and recognize writing characters in the air without additional hardware. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop and then analyzes energy features and Doppler shifts of acoustic signals received by the microphone to track the trajectory of hand movements. Furthermore, we propose a state machine-based trajectory optimization method to correct the unexpected trajectory and employ a stroke direction sequence model based on probability estimation to recognize characters users write in the air. Experimental results show that VPad achieves the average error of 1.55 cm for trajectory tracking and the accuracy over 90% of character recognition merely through built-in audio devices on a laptop.

Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder–decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.

The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose$FingerPass$which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes.$FingerPass$separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user’s identity during each interaction of finger gestures. Experiments in real environments show that$FingerPass$can achieve the authentication accuracies of 90.6 percent under in-domain scenarios and 87.6 percent under cross-domain scenarios, as well as$186.6\;ms$response time during interactions.

This paper demonstrates the feasibility of a side-channel attack to infer keystrokes on touch screen leveraging an off-the-shelf smartphone. Although there exist some studies on keystroke eavesdropping attacks on touch screen, they are mainly direct eavesdropping attacks, i.e., require the device of victims compromised to provide side-channel information for the adversary, which are hardly launched in practical scenarios. In this work, we show the practicability of an indirect eavesdropping attack, KeyListener, which infers keystrokes on QWERTY keyboards of touch screen leveraging audio devices on a smartphone. We investigate the attenuation of acoustic signals, and find that a user's keystroke fingers can be localized through the attenuation of acoustic signals received by the microphones in the smartphone. We then utilize the attenuation of acoustic signals to localize each keystroke, and further analyze errors induced by ambient noises. To improve the accuracy of keystroke localization, KeyListener further tracks finger movements during inputs through phase change and Doppler effect to reduce errors of acoustic signal attenuation-based keystroke localization. In addition, a binary tree-based search approach is employed to infer keystrokes in a context-aware manner. The proposed keystroke eavesdropping attack is robust to various environments without the assistance of additional infrastructures. Extensive experiments demonstrate that the accuracy of keystroke inference in top-5 candidates can approach 90 percent with a top-5 error rate of around 6 percent, which is a strong indication of the possible user privacy leakage of inputs on QWERTY keyboard.

Purchase intentions have a great impact on future purchases and thus can be exploited for making recommendations. However, purchase intentions are typically complex and may change from time to time. Through empirical study with two e-commerce datasets, we observe that behaviors of multiple types can indicate user intentions and a user may have multiple coexisting category-level intentions that evolve over time. In this article, we propose a novel Intention-Aware Recommender System (IARS) which consists of four components for mining such complex intentions from user behaviors of multiple types. In the first component, we utilize several Recurrent Neural Networks (RNNs) and an attention layer to model diverse user intentions simultaneously and design two kinds of Multi-behavior GRU (MGRU) cells to deal with heterogeneous behaviors. To reveal user intentions, we carefully design three tasks that share representations from MGRUs. The next-item recommendation is the main task and leverages attention to select user intentions according to candidate items. The remaining two (item prediction and sequence comparison) are auxiliary tasks and can reveal user intentions. Extensive experiments on the two real-world datasets demonstrate the effectiveness of our models compared with several state-of-the-art recommendation methods in terms of hit ratio and NDCG.

For large-scale spatiotemporal-aware publish/subscribe systems, it is critical to design an efficient forwarding engine to achieve fast matching and maintenance of events and subscriptions. For this goal, we propose a novel data structure called MO-Tree to index both subscriptions and events in a unified way. The design philosophy behind MO-Tree is to keep the data structure concise, which manifests in three aspects: limiting the height of MO-Tree, trading space for time, and avoiding node merging and splitting. The difficulty in designing MO-Tree is how to efficiently index width-variable intervals. We present a multi-level cell-overlapping partition scheme and build a theoretical model to optimize the cell width in each level. To evaluate the performance of MO-Tree, a series of experiments is conducted on real-world trace datasets. The experiment results show MO-Tree significantly outperforms the state-of-the-art in terms of matching speed and maintenance cost.

Prediction tasks about students such as predicting students' academic performances have practical real-world significance at both the student level and the college level. With the rapid construction of smart campuses, colleges not only offer residence and academic programs but also record students' daily life. The digital footprints provide an opportunity to offer better solutions for prediction tasks. In this paper, we aim to propose a general deep neural network which can jointly model student heterogeneous daily behaviors generated from digital footprints and social influence to deal with prediction tasks. To this end, we design a variant of LSTM and a novel attention mechanism to model the daily behavior sequence. The proposed LSTM is able to consider context information (e.g., weather conditions) while modeling the daily behavior sequence. The proposed attention mechanism can dynamically learn the different importance degrees of different days for every student. Based on behavior information, we propose an unsupervised way to construct a social network to model social influence. Moreover, we design a residual network based decoder to model the complex interactions between the features and get the predicted values such as future academic performances. Qualitative and quantitative experiments on two real-world datasets collected from a college have demonstrated the effectiveness of our model.

Drivers' identities are essential information that can facilitate a broad range of applications. For example, by understanding who is driving the vehicle when an accident happens, insurance companies could determine the liability and payment in a car accident claim case with high confidence. Another example, pick-up service companies could track the identities of their drivers to ensure that authorized drivers are driving esteemed clients to their destinations. While there are existing studies that can utilize video cameras and dedicated sensors to identify drivers, they either have privacy issues or require additional hardware, which is not practical enough for daily uses. In this paper, we devise a low-cost driver identification system, which can determine drivers' identities by using sensors readily available in wearable devices. Our system captures the unique driving behaviors during pervasive but momentary driving events (i.e., turning at intersections) with motion sensors, which are widely integrated into commodity wearable devices (e.g., smartphones and activity trackers). Toward this end, we extensively analyze people's driving behaviors and identify the critical turning events that capture people's unique behavioral patterns for driver identification. We design a fine-grained turning segmentation method that divides sensor data into critical turning stages (i.e., before, during, and after-turn stages), which provide multiple dimensions of turning behavioral metrics facilitating driver identification. The system extracts unique turning behavior features from time and frequency domains to enable driver identification based on drivers' turning behaviors at different types of turns. Extensive experiments are conducted with 12 drivers and various types of turns in real-road conditions. The results demonstrate that our system can identify drivers with high accuracy and low falsepositive rate based on one single turning event.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical to many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developed MagPrint, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting. MagPrint has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation using a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users, MagPrint achieves 94.3% accuracy in classifying users from these traces, which represents an 10.9% improvement over the state-of-the-art classification method.

Traditional one-time user authentication processes might cause friction and unfavorable user experience in many widely-used applications. This is a severe problem in particular for security-sensitive facilities if an adversary could obtain unauthorized privileges after a user's initial login. Recently, continuous user authentication (CA) has shown its great potential by enabling seamless user authentication with few active participation. We devise a low-cost system exploiting a user's pulsatile signals from the photoplethysmography (PPG) sensor in commercial wrist-worn wearables for CA. Compared to existing approaches, our system requires zero user effort and is applicable to practical scenarios with non-clinical PPG measurements having motion artifacts (MA). We explore the uniqueness of the human cardiac system and design an MA filtering method to mitigate the impacts of daily activities. Furthermore, we identify general fiducial features and develop an adaptive classifier using the gradient boosting tree (GBT) method. As a result, our system can authenticate users continuously based on their cardiac characteristics so little training effort is required. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks.

Quick response (QR) codes have been widely used in mobile applications, due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, QR codes have been reported suffering attacks for being sniffed just before the QR code is scanned, which lead to financial loss. In this study, we propose ScreenID, for enhancing the QR code security by identifying its authenticity, which embeds a QR code with information of unique screen fingerprint - PWM frequency. PWM frequencies are adjusted to different values by screen manufacturers, therefore can successfully differentiate screens. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types and manufacturers and thus improve the security of QR codes.

With increasing private and sensitive data stored in mobile devices, secure and effective mobile-based user authentication schemes are desired. As the most natural way to contact with mobile devices, finger touches have shown potentials for user authentication. Most existing approaches utilize finger touches as behavioral biometrics for identifying individuals, which are vulnerable to spoofer attacks. To resist attacks for on-touch user authentication on mobile devices, this paper exploits physical characters of touching fingers by investigating active vibration signal transmission through fingers, and we find that physical characters of touching fingers present unique patterns on active vibration signals for different individuals. Based on the observation, we propose a behavior-irrelevant on-touch user authentication system, TouchPass, which leverages active vibration signals on smartphones to extract only physical characters of touching fingers for user identification. TouchPass first extracts features that mix physical characters of touching fingers and behavior biometrics of touching behaviors from vibration signals generated and received by smartphones. Then, we design a Siamese network-based architecture with a specific training sample selection strategy to reconstruct the extracted signal features to behavior-irrelevant features and further build a behavior-irrelevant on-touch user authentication scheme leveraging knowledge distillation. Our extensive experiments validate that TouchPass can accurately authenticate users and defend various attacks.

Acoustic communication is an increasingly popular alternative to existing short-range wireless communication technologies for mobile devices, such as NFC and QR codes. Unlike the current standards, there are no requirements for extra hardware, lighting conditions, or Internet connection. However, the audibility and limited throughput of existing studies hinder their deployment on a wide range of applications. In this paper, we aim to redesign acoustic communication mechanism to push the boundary of potential throughput while keeping the inaudibility. Specifically, we propose BatComm, a high-throughput and inaudible acoustic communication system for mobile devices capable of throughput rates 12X higher than contemporary state-of-the-art acoustic communication for mobile devices. We theoretically model the non-linearity of microphone and use orthogonal frequency division multiplexing (OFDM) to transmit data bits over multiple orthogonal channels with an ultrasound frequency carrier. We also design a series of techniques to mitigate interference caused by sources such as the signal's unbalanced frequency response, ambient noise, and unrelated residual signals created through OFDM, amplitude modulation (AM), and related processes. Extensive evaluations under multiple realistic settings demonstrate that our inaudible acoustic communication system can achieve over 47kbps within a 10cm communication range. We also show the possibility of increasing the communication range to room scale (i.e., around 2m) while maintaining high-throughput and inaudibility. Our findings offer a new direction for future inaudible acoustic communication techniques to pursue in emerging mobile and IoT applications.

Crowd sensing has emerged as a compelling paradigm for collecting sensing data over a vast area. It is of paramount importance for crowd sensing systems to provide effective incentive mechanisms. This paper studies the critical problem of maximizing the aggregate data utility under a budget constraint in incentive mechanism design in crowd sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behavior, and private cost information of smartphones users. Most of existing mechanisms do not consider the important performance objective—maximizing the redundancy-aware data utility of sensing data collected from smartphones users. Furthermore, they do not consider the practical constraint on budget. In this paper, we propose an incentive mechanism based on a reverse auction framework. It consists of an approximation algorithm for winning user determination and a critical payment scheme. The approximation algorithm guarantees an approximation ratio for the aggregate data utility at polynomial-time complexity. The critical payment scheme guarantees truthful bidding. The rigorous theoretical analysis demonstrates that our mechanism achieves truthfulness, individual rationality, computational efficiency and budget feasibility.

Fuzzy rough-set-based feature selection is an important technique for big data analysis. However, the classic fuzzy rough set algorithm takes all the data correlations into account, which leads to the centralized computing mode, requiring high computing and memory space resources. With the increasing amount of data in the big data era, the centralized server cannot afford the computation of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this article, we propose the novel distributed fuzzy rough set (DFRS)-based feature selection, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime, and its feature selection accuracy is nearly the same as the traditional centralized computing.

Business location selection is crucial to the success of businesses. Traditional approaches like manual survey investigate multiple factors, such as foot traffic, neighborhood structure, and available workforce, which are typically hard to measure. In this article, we propose to explore both satellite data (e.g., satellite images and nighttime light data) and urban data for business location selection tasks of various businesses. We extract discriminative features from the two kinds of data and perform empirical analysis to evaluate the correlation between extracted features and the business popularity of locations. A novel neural network approach named R 2 Net is proposed to learn deep interactions among features and predict the business popularity of locations. The proposed approach is trained with a regression-and-ranking combined loss function to preserve accurate popularity estimation and the ranking order of locations simultaneously. To support the location selection for multiple businesses, we propose an approach named AR 2 Net with three attention modules, which enable the approach to focus on different latent features according to business types. Comprehensive experiments on a real-world dataset demonstrate that the satellite features are effective and our models outperform the state-of-the-art methods in terms of four metrics.

The increasing amount of trajectory data facilitates a wide spectrum of practical applications in which large numbers of trajectory range and similarity queries are issued continuously. This calls for high-throughput trajectory query processing. Traditional in-memory databases lack considerations of the unique features of trajectories, while specialized trajectory query processing systems are typically designed for only one type of trajectory queries. This paper introduces GAT, a unified GPU-accelerated framework to process batch trajectory queries with the objective of high throughput. GAT follows the filtering-and-verification paradigm where we develop a novel index GTIDX for effectively filtering invalid trajectories on the CPU, and exploit the massive parallelism of the GPU for verification. To optimize the performance of GAT, we first greedily partition batch queries to reduce the amortized query processing latency. We then apply the Morton-based encoding method to coalesce data access requests from the GPU cores, and maintain a hash table to avoid redundant data transfer between CPU and GPU. To achieve load balance, we group size-varying cells into balanced blocks with similar numbers of trajectory points. Extensive experiments have been conducted over real-life trajectory datasets. The results show that GAT is efficient, scalable, and achieves high throughput with acceptable indexing cost.

Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles' dynamics and drivers' behaviors at the same time. In this paper, we propose a steering tracking system, SteerTrack, which tracks the rotation angle of the steering wheel in real time leveraging audio devices on smartphones. SteerTrack seeks a device-free approach for steering tracking without requiring installation of specialized sensors on the steering wheels nor asking drivers to wear sensors on their wrists. Since the steering wheel is operated by a driver's hands, the rotation angle of the steering wheel can be tracked based on movements of the driver's hands. SteerTrack first builds an acoustic signal field inside of a vehicle and then analyzes the echoes reflected from the driver's hands with relative correlation coefficient (RCC) and reference frame to track the movement trajectory of hands under different steering maneuvers. Given the tracked movement trajectory, SteerTrackfurther develops a geometrical transformation-based method for estimating the rotation angle of the steering wheel in 3D driving environments by projecting the steering wheel to a 2D ellipse. Through extensive experiments in real driving environments with five volunteers for several weeks, SteerTrack can achieve an average steering wheel estimation error of 1.48 degree during driving, and 4.61 degree for turns.

The number of Internet of Things is growing exponentially, among which the ZigBee devices are being widely deployed, incurring severe collision problem in ZigBee networks. Instead of collision avoidance or packet retransmissions which introduce extra time/energy overhead, existing methods try to decompose multi-packet collision directly. For example, state-of-the-art mZig exploits collision-free chips to decompose the collided chips iteratively, however, suffers from the high bit error rate and low frame reception rate which limit the practical applications. Toward this end, we observe three major issues of existing solutions: 1) all existing solutions adopt the priori-chip-dependent decomposition pattern, leading to the error propagation; 2) the available samples for chip decoding can be scarce, resulting in severe scarce-sample errors; 3) existing solutions assume the consistent frequency offset for consecutive packets, leading to inaccurate frequency offset estimation. To solve these issues in collision decomposition, we propose FORWARD, a novel physical layer design to enable accurate collision decoding in ZigBee. The key idea is to generate all possible overlapping combinations as reference waveforms. The decomposition is determined by comparing the collided signal with the reference waveforms. Such a priori-chip-independent design has the advantages to eliminate the error propagation. To ensure sufficient samples for decoding, FORWARD always choose the longest segment as reference. Furthermore, the real-time channel estimation and frequency offset calibration ensure the accurate collision decoding. We implement FORWARD on USRP platform and evaluate its performance. Experimental results demonstrate that FORWARD reduces bit error rate by order of magnitude and increases frame reception rate by 10% ~ 50% compared with the state-of-the-art.

Financial fraud detection is an important topic in business intelligence, and neural networks have been effectively applied to construct detection models for this problem. Nevertheless, the timeliness of fitted models degrades over time when they are deployed in online detection systems. To maintain model performance when the labels of new transactions are not available, we propose an incremental virtual learning (IVL) method to update neural networks continually. The basic idea of IVL is to make the output distribution of neural networks smoother in the adversarial direction by unlabeled data. IVL uses local distribution smoothness (LDS) as the loss function at the unsupervised incremental learning stage. When updating a neural network by IVL, unlabeled data can be fed into the neural network periodically, so IVL can be implemented in a real online system without violating time constraints. To evaluate the effectiveness of IVL, experiments are conducted on a real dataset, demonstrating that neural networks augmented by IVL (IVL-NN) perform better than unoptimized neural networks in term of area under the curve (AUC). Furthermore, the standard deviation of the daily AUCs improves up to 19.09%, which indicates that IVL improves the stability of neural networks.

As wireless communication is tailored for low-power devices while the number of Internet of Things is growing exponentially, the collision problem in ZigBee is worsen. The classical approaches of solving collision problems lie in collision avoidance and packet retransmission, which could incur considerable overhead. The new trend is to decompose multipacket collision directly, however, the high bit error rate limits its practical applications. Toward this end, we observe three major issues in the existing solutions: 1) all existing solutions adopt the priori-chip-dependent decomposition pattern, leading to the error propagation; 2) the available samples for chip decoding can be scarce, resulting in severe scarce-sample errors; 3) existing solutions assume the consistent frequency offset for consecutive packets, leading to inaccurate frequency offset estimation. To solve the issues of collision decomposition in ZigBee, we propose FORWARD, a novel physical layer design to enable highly accurate collision decomposition in ZigBee. The key idea is to generate all possible collided combinations as reference waveforms. The decomposition is determined by comparing the collided signal with the reference waveforms. Such a priori-chip-independent design has the advantages to eliminate the cumulative errors incurred from error propagation. When decoding, FORWARD always choose the longest segment to ensure sufficient samples for decoding. Furthermore, the recursive calibration design is approaching the real-time frequency offset and dynamically compensates the reference waveform. We implement FORWARD on USRP based testbed and evaluate its performance. Experimental results demonstrate that FORWARD reduces bit error rate by 4.96× and increases throughput 1.46~2.8× compared with the state-of-the-art mZig.

Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic flows are affected by multiple complex factors, such as spatial and temporal dependencies of regions and external factors. In this paper, we propose a deep hybrid spatio-temporal dynamic neural network, called DHSTNet, to predict both inflows and outflows in every region of a city. More specifically, it consists of four main components, i.e., closeness influence taking the instantaneous variations of traffic flows, period influence regularly identifying daily changes of traffic crowd flows, weekly component identifying the patterns of weekly traffic flows and external component acquiring external factors. We design a branch of deep hybrid recurrent convolutional neural network units to model the first three temporal properties, i.e., closeness, period influence, and weekly influence. The external components are feed into two fully connected neural networks. For different branches, our proposed model assigns different weights and then combines the output of the four components. Experimental results based on two large-scale real-world datasets demonstrate the superiority of our model over the existing state-of-the-art methods.

The increasing volume of spatial-textual data generated from check-ins and reviews facilitates many practical applications. In these applications, similarity joins are the basic operation which further supports a wide range of similarity queries. However, this compute-intensive join operation calls for more efficient and effective algorithm to support real-time queries. Exploiting general purpose GPUs (GPGPUs) is one natural solution. Nevertheless, existing join algorithms utilizing GPGPU lack considerations on unique features of spatial-textual trajectories, especially the textual domain, thus resulting in poor performance. In this paper, we first propose a baseline spatial-textual trajectory similarity join algorithm, which is essentially based on a traditional algorithm for relational joins on GPGPUs. Then, by analyzing the bottleneck of this baseline, we further develop a fine-grained join algorithm which consists of memory access techniques and optimizations. We propose a strategy of flip to ensure coalesced memory accesses, and a batch scheduler for dividing the whole task into batches to fit the global memory while alleviating imbalanced throughput. Extensive experiments have been conducted over real-life datasets and the results show that our fine-grained algorithm performs the best and reduces the latency effectively.

Oceans are important for scientific research and also for global economic and military security. Usually, wireless ad hoc networks are chosen to transform real-time data collected by ocean monitoring sensors (nodes). Due to the random motion of waves or the random direction of the wind, nodes in the network might become detached from the coverage of the network. In this case, the detached nodes can either send the collected data directly to the base station at the cost of consuming more energy or wait for a period of time to rejoin the network with the price of sacrificing the real time of the collected data. In this paper, we model the optimal waiting time for detached nodes before directly sending the data in the dynamic environment of ocean monitoring. For this purpose, we need to address two problems. The first is how to calculate the rate of coverage with a different number and different broadcast radii of nodes. The second is when a node detaches from the coverage of the network, how much time will it need to wait before it rejoins the network. We first establish the motion model of nodes, which is the basis to deduce the probability distribution of a certain time when the detached node rejoins the network. Based on the probability distribution, the waiting time of the detached nodes can be optimally determined, aiming to achieve a good balance between energy consumption and data timeliness. Finally, a series of simulations is conducted to validate the effectiveness of our proposed method.

Nowadays, the container-based virtualization technologies have become very popular due to lightweight nature, scalability, flexibility and others. Kubernetes is one of the most popular container cluster management systems, which enables users to deploy applications on the container easily, so more and more web applications are deployed in a Kubernetes clusters. However, a Kubernetes cluster is generally designed to handle the peak of workloads, so that most of resources are idle in usual time, which results in an huge waste of resource. Hence, it is necessary to design a system to improve the cluster resource utilization and promise Quality of Service (QoS) in a Kubernetes cluster. In this paper, we propose a generic system to dynamically adjust the scale of a Kubernetes cluster, which is able to reduce the waste of resource on the premise of QoS guarantee. The proposed system contains four modules: monitor module, QoS module, scaling module, and executing module. First, the monitor module uses two open-source tools, Heapster and InfluxDB, to monitor and store real-time status of a Kubernetes cluster. Then, to guarantee QoS in the Kubernetes cluster, the QoS module presents a method to automatically decide a threshold of CPU utilization that is able to meet requirements of a specific application. Next, the scaling module provides a cluster scaling algorithm to get an ideal number of nodes in the Kubernetes cluster, which is used to allocate resources in a cluster-level allocation. Finally, according to the ideal number of nodes, the executing module adjusts the scale of the Kubernetes cluster to carry out the application. Extensive experiments in real environments show that, on average, the proposed system improves CPU utilization of a Kubernetes cluster by 28.99%.

The matching algorithm is a critical component of the content-based publish/subscribe system, whose performance has direct effects on the QoS of the whole system. Aiming to improve and stabilize the matching performance, we propose a lightweight parallelization method called PhSIH on the basis of three existing algorithms. PhSIH fulfills Parallelization by horizontally Segmenting the Indexing Hierarchy of data structures to support multiple threads performing matching tasks in parallel on a common data structure. PhSIH can adaptively adjust the degree of parallelism according to the changing workloads in order to meet the performance requirement. The main work of PhSIH concerns dynamically adjusting the degree of parallelism and computing a task allocation solution for parallel threads. PhSIH is implemented in Apache Kafka to augment it as a content-based publish/subscribe system, which makes Kafka suitable for real-time fine-grained event dissemination scenarios, such as stock ticks. To evaluate the parallelization effect and adaptability of PhSIH, a series of experiments are conducted based on synthetic and real-world data. The experiment results demonstrate that PhSIH achieves a good parallelization effect on the three existing algorithms and possesses a desirable adaptability that stabilizes the performance of the matching algorithms.

Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.

Dredging is the removal of sediment from the bottom of lakes, rivers, harbors, and other water bodies. It is a routine necessity in waterways around the world because the natural process of sand and silt washing downstream results in the sediment gradually filling channels and harbors. However, during the dredging operation, some dredgers do not transport the sediment to the designated area as expected but throw it near the waterway meaning sediment may return to the waterway in a short period. This paper proposes an online dredging operation anomaly detection (DOAD) method to detect this kind of irregular behavior during the dredging operation based on automatic identification system (AIS) data. First, we establish a feature system to extract behavior features from AIS data. Furthermore, we jointly utilize t-distributed stochastic neighbor embedding (t-SNE) with neural networks and a Gaussian mixture model (GMM) to train a detection model in a semi-supervised way. Through the trained model, irregular behaviors can be efficiently detected in real time during the dredging operation. The effectiveness of DOAD is evaluated according to a series of experiments. To the best of our knowledge of the published literature, this work is the first to introduce the application of AIS data to detect irregular behaviors during dredging operations.

Deep learning is popular in many areas, but users must manually specify the resource configuration when submitting deep learning training jobs, usually over-provisioning resources. This kind of unreasonable resource configuration method results in slow training and low resource utilization. Therefore, it would be more convenient and efficient if users only need to specify the quality of service (QoS) for their jobs, and then the resources will be autoconfigured to meet the QoS. To satisfy this demand, we present a QoS-oriented scheduling and autoscaling framework that schedules and autoscales deep learning training jobs in the Kubernetes cluster. This paper focuses on the most important QoS requirement for deep learning training jobs: deadline. The goal of the framework is to guarantee that as many jobs as possible can be accomplished before their specified deadlines. To reach this goal, the framework schedules deep learning jobs by implementing a heuristic scheduling policy based on resource status and job deadline, and autoscales resource configuration by exploiting a characteristic of deep learning jobs: the predictability of training time. This predictability is used to predict whether a job can be accomplished before its deadline and estimate appropriate resource configuration if necessary. We implemented the framework by modifying the default scheduler of Kubernetes and conducted experiments to evaluate its performance. The experiment results show that our scheduling policy can improve the completion rate by 26% when the cluster resources are insufficient, and our autoscaling policy can improve the completion rate to 100% when the cluster resources are sufficient. We also show that the framework improves the utilization of allocated CPUs to 100%. Our proposed framework points to a new way of submitting and managing deep learning training jobs in the cluster.

This paper demonstrates the feasibility of a side-channel attack to infer keystrokes on touch screen leveraging an off-the-shelf smartphone. Although there exist some studies on keystroke eavesdropping attacks on touch screen, they are mainly direct eavesdropping attacks, i.e., require the device of victims compromised to provide side-channel information for the adversary, which are hardly launched in practical scenarios. In this work, we show the practicability of an indirect eavesdropping attack, KeyListener, which infers keystrokes on QWERTY keyboards of touch screen leveraging audio devices on a smartphone. We investigate the attenuation of acoustic signals, and find that a user's keystroke fingers can be localized through the attenuation of acoustic signals received by the microphones in the smartphone. We then utilize the attenuation of acoustic signals to localize each keystroke, and further analyze errors induced by ambient noises. To improve the accuracy of keystroke localization, KeyListener further tracks finger movements during inputs through phase change and Doppler effect to reduce errors of acoustic signal attenuation-based keystroke localization. In addition, a binary tree-based search approach is employed to infer keystrokes in a context-aware manner. The proposed keystroke eavesdropping attack is robust to various environments without the assistance of additional infrastructures. Extensive experiments demonstrate that the accuracy of keystroke inference in top-5 candidates can approach 90% with a top-5 error rate of around 6%, which is a strong indication of the possible user privacy leakage of inputs on QWERTY keyboard.

In recent decades, countless efforts have been put into the research and development of short-range wireless communication, which offers a convenient way for numerous applications (e.g., mobile payments, mobile advertisement). Regarding the design of acoustic communication, throughput and inaudibility are the most vital aspects, which greatly affect available applications that can be supported and their user experience. Existing studies on acoustic communication either use audible frequency band (e.g., <20kHz) to achieve a relatively high throughput or realize inaudibility using near-ultrasonic frequency band (e.g., 18-20kHz) which however can only achieve limited throughput. Leveraging the non-linearity of microphones, voice commands can be demodulated from the ultrasound signals, and further recognized by the speech recognition systems. In this poster, we design an acoustic communication system, which achieves high-throughput and inaudibility at the same time, and the highest throughput we achieve is over 17x higher than the state-of-the-art acoustic communication systems.

The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose FingerPass which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. We investigate CSI of WiFi signals in depth and find CSI phase can be used to capture and distinguish the unique behavioral characteristics from different users. FingerPass separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user's identity during each interaction of finger gestures. Experiments in real environments show that FingerPass can achieve 91.4% authentication accuracy, and 186.6ms response time during interactions.

Given the increasing amount of time people spent on driving, the physical and mental health of drivers is essential to road safety. Breathing patterns are critical indicators of the well-being of drivers on the road. Existing studies on breathing monitoring require active user participation of wearing special sensors or relatively quiet environments during sleep, which are hardly applicable to noisy driving environments. In this work, we propose a fine-grained breathing monitoring system, BreathListener, which leverages audio devices on smartphones to estimate the fine-grained breathing waveform in driving environments. By investigating the data collected from real driving environments, we find that Energy Spectrum Density (ESD) of acoustic signals can be utilized to capture breathing procedures in driving environments. To extract breathing pattern in ESD signals, BreathListener eliminates interference from driving environments in ESD signals utilizing background subtraction and Ensemble Empirical Mode Decomposition (EEMD). After that, the extracted breathing pattern is transformed into Hilbert spectrum, and we further design a deep learning architecture based on Generative Adversarial Network (GAN) to generate fine-grained breathing waveform from the Hilbert spectrum of extracted breathing patterns in ESD signals. Experiments with 10 drivers in real driving environments show that BreathListener can accurately capture breathing patterns of drivers in driving environments.

Multipath profiling is to characterize multipath components of wireless channels, which can be done using Channel State Information (CSI) from WiFi devices. To do so with satisfactory accuracy, recent studies rely on either a large number of receiving antennas or large bandwidth. However, it is difficult for commodity WiFi devices to meet these requirements. In this paper, we propose a scheme, WiZoom, that can perform accurate multipath profiling using single-band CSI from commodity WiFi devices. In order to achieve accurate multipath profiling with limited bandwidth, WiZoom first incorporates the MUltiple SIgnal Classification (MUSIC) algorithm with CSI to estimate ToAs of multipath components, and then combines multiple antennas to improve the resolution of ToA estimation. WiZoom further estimates attenuations and phase shifts for multipath components using the ToAs. So far, multipath components are fully characterized, and all these estimated parameters form the multipath profile. We evaluate the performance of WiZoom using commodity WiFi devices in real environment, and results show that WiZoom achieves high accuracy in multipath profiling.

Abstract The advent of big data era has given rise to the big data trading market because of the potentially enormous economic value. However, designing an effective trading mechanism for the data trading market is still in its infancy. Existing several incentive mechanisms have neglected the important fact that data consumers have both preferences and complex conflicts of interest (CoI) relations among them. In response to the limitations of existing trading mechanisms, we propose DTPCI, a truthful double auction mechanism for a Data Trading market with two unique characteristics of consumers’ Preferences and complex CoI relations among them. However, three major challenges have to be addressed, i.e. diverse market preferences, the complex CoI relations of data consumers and the strategic behaviors of both two sides. To jointly address the three challenges, we propose DTPCI to achieve nonnegative social welfare, which features a group rule and a data trading rule. The group rule generates all conflict-free virtual groups based on the CoI graph. The data trading rule adopts the group buying to share data and expense. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that DTPCI achieves all the desired economic properties.

Air quality has gained much attention in recent years and is of great importance to protecting people’s health. Due to the influence of multiple factors, the limited air quality monitoring stations deployed in cities are unable to provide fine-grained air quality information. One cost-effective way is to infer air quality with records from existing monitoring stations. However, the severe data sparsity problem (e.g., only 0.2% data are known) leads to the failure of most inference methods. We observe that remote sensing data are of high quality and have a strong correlation with the air quality. Therefore, we propose to integrate remote sensing data and ubiquitous urban data for the air quality inference. But there are two main challenges, i.e., data heterogeneity and incompleteness of the remote sensing data. To address the challenges, we propose a two-stage approach. In the first stage, we infer and predict air quality conditions of some places leveraging the remote sensing data and meteorological data with two proposed ANN-based methods, respectively. This stage significantly alleviates the data sparsity problem. In the second stage, the records and estimated air quality data are put in a tensor. A tensor decomposition method is applied to complete the tensor. The features extracted from urban data are classified into the spatial features (i.e., road features and POI features) and the temporal features (i.e., meteorological features) as the constraints to further address the data sparsity problem. In addition, an iterative training framework is proposed to improve the inference performance. Experiments on a real-world dataset show that our approach outperforms state-of-the-art methods, such as U-Air.

To prevent users privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, and so on, to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although the state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Toward this end, we explore liveness verification of user authentication leveraging users mouth movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users speaking mouths through acoustic sensing on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking mouths and find that there are unique mouth movement patterns for different individuals. To characterize the mouth movements, we propose a deep learning-based method to extract efficient features from Doppler profiles and employ softmax function, support vector machine and support vector domain description to construct multi-class identifier, binary classifiers and spoofer detectors for mouth state identification, user identification and spoofer detection, respectively. Afterward, we develop a balanced binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.2% accuracy in user identification and 93.1% accuracy in spoofer detection.

Mobile Crowdsensing (MCS) is a promising paradigm that utilizes ubiquitous mobile devices to collect environmental data. Specially, location data is critical among all kinds of data because most MCS applications are location-based. Faulty data and missing values, however, may exist in the collected location data due to various reasons. This brings forth an important issue of detecting faulty location data in the presence of missing values. To address this issue, we propose I(TS, CS), a joint faulty data detection framework that combines TimeSeries and Compressive Sensing techniques. The framework adopts a DETECT-and-CORRECT approach to iteratively detect faulty data and reconstruct the dataset, which bypasses the tradeoff between false positive ratio (Type-I error) and false negative ratio (Type-II error), and thus detects more faulty data without increasing False Positive Rate. We have evaluated the proposed I(TS, CS) framework based on a real trace consisting of the trajectories of 2000 taxies, showing I(TS, CS) dramatically improve the performance of both faulty data detection and data reconstruction.

The increasing amount of trajectory data facilitates a wide spectrum of practical applications. In many such applications, large numbers of trajectory range and similarity queries are issued continuously, which calls for high-throughput trajectory query processing. Traditional in-memory databases lack considerations of the unique features of trajectories, thus suffering from inferior performance. Existing trajectory query processing systems are typically designed for only one type of trajectory queries, i.e., either range or similarity query, but not for both. Inspired by the massive parallelism on GPUs, in this paper, we develop a GPU-accelerated framework, named GAT, to support both types of trajectory queries (i.e., both range and similarity queries) with high throughput. For similarity queries, we adopt the Edit Distance on Real sequence (EDR) as the similarity measure which is accurate and robust to noise in real-world trajectories. GAT employs a GPU-friendly index called GTIDX to effectively filter invalid trajectories for both range and similarity queries, and exploits the GPU to perform parallel verifications. To accelerate the verification process on the GPU, we apply the Morton-based encoding method to reorganize trajectory points and facilitate coalesced data accesses for individual point data in global memory, which reduces the global memory bandwidth requirement significantly. We also propose a technique of grouping size-varying cells into balanced blocks with similar numbers of trajectory points, to achieve load balancing among the Streaming Multiprocessors (SMs) of the GPU. We conduct extensive experiments to evaluate the performance of GAT using two real-life trajectory datasets. The results show that GAT is scalable and achieves high throughput with acceptable indexing cost.

Human-computer interaction based on touch screens plays an increasing role in our daily lives. Besides smartphones and tablets, laptops are the most popular mobile devices used in both work and leisure. To satisfy requirements of many emerging applications, it becomes desirable to equip both writing and drawing functions directly on laptop screens. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, for trajectory tracking without additional hardware. It employs acoustic signals to accurately track hand movements and recognize characters user writes in the air. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop. Then VPad applies Sliding-window Overlap Fourier Transformation technique to find Doppler frequency shift with higher resolution and accuracy in real time. Furthermore, we analyze frequency shifts and energy features of acoustic signals received by the microphone to track the trajectory of hand movements. Finally, we employ a stroke direction sequence model based on possibility estimation to recognize characters users write in the air. Our experimental results show that VPad achieves the average trajectory tracking error of only 1.55cm and the character recognition accuracy of above 90% merely through two speakers and one microphone on a laptop.

A distributed cluster provides a common computation platform to efficiently run different types of jobs, such as real-time and batch jobs that have different QoS requirements. One challenge of a distributed cluster is to find a scheduling strategy that can reduce the overtime ratio of real-time jobs as much as possible while also improving the completion time of batch jobs, aiming to promote QoS even though the resources in the cluster are stretched. To address this issue, many efficient scheduling methods have been proposed. However, most existing approaches are naive and coarse-grained in means of either killing batch jobs to preempt resources for real-time jobs or reserving some resources in advance for real-time jobs to minimize the overtime ratio of real-time jobs, which prolongs the completion time of batch jobs and reduces the resource utilization of the cluster. In this paper, we propose UpPreempt, a fine-grained preemptive scheduling strategy for container-based clusters. When scheduling real-time jobs, UpPreempt considers the deadline of jobs and the resource usage of existing batch jobs. Performing resource preemption from multiple batch jobs is the basic idea of UpPreempt. The set of batch jobs to be preempted and the amount of resources taken from each job are finely determined. In this way, UpPreempt greatly alleviates the effect of preemption on the batch jobs in terms of completion time and avoids reserving resources for real-time jobs. We implement UpPreempt in YARN to evaluate its performance. The evaluation with various workloads shows that our proposed scheduling strategy can achieve a good trade-off between the overtime ratio of real-time jobs and the completion time of batch jobs without reducing the resource utilization of the cluster.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.

Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles' dynamics and drivers' behaviors at the same time. In this paper, we propose a steering tracking system, SteerTrack, which tracks the rotation angle of steering wheel in real time leveraging audio devices on smartphones. SteerTrack seeks a device-free approach for steering tracking without requiring installation of specialized sensors on steering wheels nor asking drivers to wear sensors on their wrists. Since the steering wheel is operated by a driver's hands, the rotation angle of steering wheel can be tracked based on movements of the driver's hands. SteerTrack first builds an acoustic signal field inside of a vehicle and then analyzes the echoes reflected from the driver's hands with relative correlation coefficient(RCC) and reference frame to track the movement trajectory of hands under different steering maneuvers. Given the tracked movement trajectory, SteerTrack further develops a geometrical transformation-based method for estimating the rotation angle of steering wheel in 3D driving environments by projecting the steering wheel to a 2D ellipse. Through extensive experiments in real driving environments with 5 volunteers for several weeks, SteerTrack can achieve an average error of 4.61 degree for estimating the rotation angle of steering wheel.

It is of significant importance to provide incentives to smartphone users in mobile crowd sensing systems. Recently, a number of auction-based incentive mechanisms have been proposed. However, an auction-based incentive mechanism may unexpectedly release the location privacy of smartphone users, which may seriously reduce the willingness of users participating in contributing sensing data. In an auction-based incentive mechanism, even if the location of a user is not enclosed in his/her bid submitted to the platform, the location information may still be inferred by an adversary by using the prices of the tasks required by the user. We take an example to show how an attack can recover the location information of a smartphone user by merely knowing his/her bid. To defend against such an attack, we propose a method to protect location privacy in auctions for mobile crowd sensing systems. This method encrypts prices in a bid so that the adversary cannot access and hence the location privacy of users can be protected. In the meanwhile, however, the auction can proceed properly, i.e. the platform can select the user offering the lowest price for each sensing task or the platform can choose users with budget constraint. We demonstrate the effectiveness of our proposed method with theoretical analysis and simulations.

Sensor-cloud computing is envisioned as a promising technology that can integrate various services by extending the computational capabilities of physical sensor nodes. It is prone to attack because of special characteristics of physical sensor nodes and virtual sensor-service nodes. Considering the intrusion detection threshold, false alarm probability of the intrusion detection system (IDS), and three different attacked scenarios, we devise a physical IDS (PIDS)-to-gateway and virtual IDS (VIDS)-to-gateway detection model for Sensor-Cloud. We formulate a two-layer gateway-assisted detection and defense decision problem involving multiple IDSs using an evolutionary game in order to optimize the intrusion detection strategy for lowering energy consumption and reducing alarm messages. We derive an evolutionary stable strategy and prove that the proposed mechanism achieves Nash equilibrium, such that each IDS completes cooperatively defense tasks. We propose a game-theoretic approach to achieve an energy-efficient cooperative defense scheme for sensor-cloud computing environments. The simulation results demonstrate that the proposed mechanism achieves energy-efficient defense and increases security of data in the Sensor-Cloud.

Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alerting and avoiding car accidents. In this paper, we leverage built-in audio devices on smartphones to realize early recognition of inattentive driving events including Fetching Forward, Picking up Drops, Turning Back, and Eating or Drinking. Through empirical studies of driving traces collected in real driving environments, we find that each type of inattentive driving event exhibits unique patterns on Doppler profiles of audio signals. This enables us to develop an Early Recognition system, ER, which can recognize inattentive driving events at an early stage and alert drivers timely. ER employs machine learning methods to first generate binary classifiers for every pair of inattentive driving events, and then develops a modified vote mechanism to form a multi-classifier for all four types of inattentive driving events, for atypical inattentive driving events along with other driving behaviors. It next turns the multi-classifier into a gradient model forestto achieve early recognition of inattentive driving. Through extensive experiments with eight volunteers driving for about two months, ER can achieve an average total accuracy of 94.80 percent for inattentive driving recognition and recognize over 80 percent inattentive driving events before the event is 50 percent finished.

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a Finite State Machine is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that L3is accurate and robust in real driving environments. The experimental results show that, on average, L3achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.

Auctions have been adopted by many major cloud providers, such as Amazon EC2. Unfortunately, only simple auctions have been implemented. Such simple auction has serious limitations, such as being unable to accept elastic user demands and having to allocate different types of VMs independently. These limitations create a big gap between the real needs of cloud users and the available services of cloud providers. In response to the limitations of the existing auction mechanisms, this paper proposes a novel online auction mechanism for IaaS clouds, with the unique features of an elastic model for inputting time-varying user demands and a unified model for requesting heterogeneous VMs together. However, several major challenges should be addressed, such as NP hardness of optimal VM allocation, time-varying user demands and potential misreports of private information of cloud users. We propose a truthful online auction mechanism for maximizing the profit of the cloud provider in IaaS clouds, which is composed of a price-based allocation rule and a payment rule. In the allocation rule, the online auction mechanism determines the number of VMs of each type to each user. In the payment rule, by introducing a marginal price function for each type of VMs, the mechanism determines how much the cloud provider should charge each cloud user. With solid theoretical analysis and trace-driven simulations, we demonstrate that our mechanism is truthful, fair and individually rational, and has a polynomial-time complexity. In addition, our auction achieves a competitive ratio for the profit of the cloud provider, compared against the offline optimal one.

Double auction-based pricing model is an efficient pricing model to balance users' and providers' benefits. Existing double auction mechanisms usually require both users and providers to bid with the unit price and the number of VMs. However, in practice users seldom know the exact number of VMs that meets their task requirements, which leads to users' task requirements inconsistent with providers' resource. In this paper, we propose a truthful double auction mechanism, including a matching process as well as a pricing and VM allocation scheme, to bridge users' task requirements and providers' resources in two-sided cloud markets. In the matching process, we design a cost-aware resource algorithm based on Lyapunov optimization techniques to precisely obtain the number of VMs that meets users' task requirements. In the pricing and VM allocation scheme, we apply the idea of second-price auction to determine the final price and the number of provisioned VMs in the double auction. We theoretically prove our proposed mechanism is individual-rational, truthful and budget-balanced, and analyze the optimality of proposed algorithm. Through simulation experiments, the results show that the individual profits achieved by our algorithm are 12.35 and 11.02 percent larger than that of scaleout and greedy scale-up algorithms respectively for 90 percent of users, and the social welfare of our mechanism is only 7.01 percent smaller than that of the optimum mechanism in the worst case.

Benefiting from the pay-per-use pricing model of cloud computing, many companies migrate their services and applications from typical expensive infrastructures to the cloud. However, due to fluctuations in the workload of services and applications, making a cost-efficient VM configuration decision in the cloud remains a critical challenge. Even experienced administrators cannot accurately predict the workload in the future. Since the pricing model of cloud provider is convex other than linear that often assumed in past research, instead of typical scaling out strategy. In this paper, we adopt mix scale strategy. Based on this observation, we model an optimization problem aiming to minimize the VM configuration cost under the constraint of migration delay. Taking advantages of Lyapunov optimization techniques, we propose a mix scale online algorithm which achieves more cost-efficiency than that of scale out strategy. Experimental results shows that the mix scale algorithm saves 30.8% and 31.1% cost where controlling migration delay in a tolerable range under different workload respectively.

In the cloud computing architecture, virtual machine (VM) live migration is a fundamental research topic which has drawn extensive attention from communities of industry and academy. It is critical to transfer the VM memory pages that contain essential state information to resume the VM on another host during virtual machine (VM) live migration. There are many memory copy methods, such as pre-copy and post-copy. However, these methods have two limitations: application generality and performance imbalance. In this paper, we propose Ada-copy (adaptive copy), an adaptive memory copy strategy for VM live migration. The basic idea of Ada-copy is that the memory copy method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current dirty page rate of memory, Ada-copy can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations of existing memory copy methods. To evaluate the effectiveness of our proposed strategy, we experiment with the Ada-copy on a variety of migration tasks with different dirty page rate and diverse memory usage workloads. Evaluation results show, compared with traditional methods, Ada-copy can significantly reduce the total migration time by 26%, the VM downtime by 42% and the amount of pages transferred by 35% in average.

Auctions have been adopted by many major cloud providers, such as Amazon EC2. Unfortunately, only simple auctions have been implemented. Such simple auction has serious limitations, such as being unable to accept elastic user demands and having to allocate different types of VMs independently. These limitations create a big gap between the real needs of cloud users and the available services of cloud providers. In response to the limitations of the existing auction mechanisms, this paper proposes a novel online auction mechanism for IaaS clouds, with the unique features of an elastic model for inputting time-varying user demands and a unified model for requesting heterogeneous VMs together. However, several major challenges should be addressed, such as NP hardness of optimal VM allocation, time-varying user demands and potential misreports of private information of cloud users. We propose a truthful online auction mechanism for maximizing the profit of the cloud provider in IaaS clouds, which is composed of a price-based allocation rule and a payment rule. In the allocation rule, the online auction mechanism determines the number of VMs of each type to each user. In the payment rule, by introducing a marginal price function for each type of VMs, the mechanism determines how much the cloud provider should charge each cloud user. With solid theoretical analysis and trace-driven simulations, we demonstrate that our mechanism is truthful and individually rational, and has a polynomial-time complexity.

Real-time driving behavior monitoring is a corner stone to improve driving safety. Most of the existing studies on driving behavior monitoring using smartphones only provide detection results after an abnormal driving behavior is finished, not sufficient for driver alert and avoiding car accidents. In this paper, we leverage existing audio devices on smartphones to realize early recognition of inattentive driving events including Fetching Forward, Picking up Drops, Turning Back and Eating or Drinking. Through empirical studies of driving traces collected in real driving environments, we find that each type of inattentive driving event exhibits unique patterns on Doppler profiles of audio signals. This enables us to develop an Early Recognition system, ER, which can recognize inattentive driving events at an early stage and alert drivers timely. ER employs machine learning methods to first generate binary classifiers for every pair of inattentive driving events, and then develops a modified vote mechanism to form a multi-classifier for all inattentive driving events along with other driving behaviors. It next turns the multi-classifier into a gradient model forest to achieve early recognition of inattentive driving. Through extensive experiments with 8 volunteers driving for about half a year, ER can achieve an average total accuracy of 94.80% for inattentive driving recognition and recognize over 80% inattentive driving events before the event is 50% finished.

With the development of distributed energy generators and storages, the sustainability of a microgrid comprised of multiple electricity users is significantly increased. Maximizing the efficiency of generated renewable energy is vital to running a sustainable microgrid as it indicates reducing the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be shared with others who are short of energy. Unfortunately, coordinating the transfers of renewable energy among the users in a microgrid is particularly difficult, given the rational nature of users, the stochastic nature of renewable energy and the dynamic nature of energy demand of each user. In this paper, we consider the coupled problem of maximizing the renewable energy efficiency of a sustainable microgrid as well as stimulating rational users to share excessive renewable energy. We propose a near-optimal scheduling algorithm, which determines the amounts of renewable energy transferred among users in an online fashion. We also design an efficient pricing mechanism for the trade of energy among users based on double auction. We rigorously prove that our online scheduling algorithm is approximately optimal and the pricing mechanism guarantees the property of individual rationality of users. Comprehensive simulation results demonstrate the efficacy of our online algorithm and incentive mechanism.

Crowdsourcing to mobile users has emerged as a compelling paradigm for collecting sensing data over a vast area for various monitoring applications. It is of paramount importance for such crowdsourcing paradigm to provide effective incentive mechanisms. State-of-the-art auction mechanisms for crowdsourcing to mobile users are typically deterministic in the sense that for a given sensing job from a crowdsourcer, only a small set of smartphones are selected to perform sensing tasks and the rest are not selected. One apparent disadvantage of such deterministic auction mechanisms is that the diversity with respect to the sensing job is reduced. As a consequence, the quality of the collected sensing data is also decreased. This is due to failure to exploit the intrinsic advantage of the large set of diverse mobile users in a mobile crowdsourcing network. In this paper, we propose a randomized combinatorial auction mechanism for the social cost minimization problem, which is proven to be NP-hard. We design an approximate task allocation algorithm that is near optimal with polynomial-time complexity and use it as a building block to construct the whole randomized auction mechanism. Compared with deterministic auction mechanisms, the proposed randomized auction mechanism increases the diversity in contributing users for a given sensing job. We carry out both solid theoretical analysis and extensive numerical studies and show that our randomized auction mechanism achieves approximate truthfulness, individual rationality, and high computational efficiency.

Accessing to timely and accurate road condition information, especially about dangerous potholes is of great importance to the public and the government. In this paper, we propose a novel scheme, called P3, which utilizes smartphones placed in normal vehicles to sense and estimate the profiles of potholes on urban surface roads. In particular, a P3-enabled smartphone can actively learn the knowledge about the suspension system of the host vehicle without any human intervention and adopts a one degree-offreedom (DOF) vibration model to infer the depth and length of pothole while the vehicle is hitting the pothole. Furthermore, P3 shows the potential to derive more accurate results by aggregating individual estimates. In essence, P3 is light-weighted and robust to various conditions such as poor light, bad weather, and different vehicle types. We have implemented a prototype system to prove the practical feasibility of P3. The results of extensive experiments based on real trace demonstrate the efficacy of the P3 design. On average, P3 can achieve low depth and length estimation error rates of 13 and 16 percent, respectively.

With the rapid development of information technology, mobile devices have exhibited increasing popularity in recent years. To support the anytime-anywhere service model of mobile devices, one important problem related to the screen display arises when using these devices (e.g., smartphone and tablet) under various lighting conditions. On one hand, it is hard for users to see the display clearly under strong lighting conditions (e.g., sunlight). On the other hand, the screen appears dazzling under weak lighting conditions. This problem related to the mobile device display can significantly degrade user experience and undermine the successful deployment of the anytime-anywhere mobile service model. Existing solutions mainly focus on the automatic adjustment of brightness level under different light conditions. We show that merely utilizing brightness level to solve the display problem is not enough to maintain the user experience under both strong and weak lighting scenarios through experimenting with over 200 volunteers. In this work, we take a different approach by investigating automatic color scheme adjustment to improve user experience. We find that Readability, Comfort level, and Similarityare major factors that contribute to user experience. In recognizing these problems, we propose a system, ColorVert, which utilizes the DKL color space to adaptively transform color schemes by sensing ambient light to improve user experience under various lighting scenarios. Our experimental evaluation with over 200 precipitants and various mobile devices demonstrates that ColorVert is more effective in both maintaining as well as improving user experience compared with the existing automatic brightness adjustment system.

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e.,Weaving,Swerving,Sideslipping,Fast U-turn,Turning with a wide radius, andSudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormalDriving behaviorDetection and iDentification system,$D^{3}$, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods,Support Vector Machine(SVM) andNeuron Networks(NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that$D^{3}$achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.

To support mobile users in contributing sensing data for making urban management decisions, in ShanghaiGrid, unified data gathering operations are to be performed. For citywide coverage, public vehicles accept data from surrounding users and hand over to computing center through wireless base stations (BSs) deployed in the city. Meanwhile, several among the vehicles are hired as relays, which assist gathering from others with multicopy and multihop forwarding towards the BSs. However, the budget shared by deploying BSs and hiring relays is limited. We explore how to decide BS deployment and relay-based forwarding for efficient gathering under the budget. The challenge lies in the great uncertainty about collection opportunities of candidate locations and vehicles in future gathering processes. In this paper, we present an empirical approach for the problem. To tackle the challenges, we characterize collection performance as function of temporal data paths towards each candidate, and formulate the problem as a multiobjective optimization problem. To solve it, we reveal regular relations between the candidates and estimate expected importance of them with large set of real vehicular traces; and develop an algorithmic framework for BS deployment and corresponding forwarding strategy. Extensive trace-driven simulations demonstrate the efficacy of the approach.

With the development of renewable energy generators and electricity storages, microgrids become a promising technology of the smart grid. Maximizing the usage of renewable energy is vital to running a microgrid as it indicates reduction of the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be transferred to other users who need energy. Unfortunately, coordinating the transfers of renewable energy among the users in the microgrid is particularly difficult due to the stochastic nature of renewable energy, and the dynamic energy demand of each user. In this paper, we consider the problem of maximizing the long-term renewable energy usage by exchanging excessive renewable energy among users in a microgrid. We propose an online control algorithm which determines the amounts of renewable energy transferred among users in an online fashion. We rigorously prove that our online control algorithm is approximately optimal. We have conducted comprehensive simulation results that demonstrate the efficacy of our online algorithm.

Crowdsourcing distributed sensing to smartphones has become an appealing paradigm for harnessing the power of the crowd for collecting and sharing information. Market-driven auctions have been recognized as an effective way to matching sensing service demand and supply. A crowdsourcer acts as an auctioneer and smartphone workers act as bidders who are willing to supply sensing services to crowdsourcers. A number of auction mechanisms have been proposed. Unfortunately, most of them assume there is only one crowdsourcer, which is not true in the real world. In this paper, we consider the design of distributed auctions for a crowdsourced sensing market with multiple crowdsourcers and many smartphone workers. We employ a distributed reverse auction that works in a fully distributed fashion. The mechanism requires no one to release its private information. With extensive simulations, we demonstrate that our distributed mechanism achieves the optimal social welfare, and produces better performance than a competing algorithm.

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing work on tracking vehicles on lane-level mostly depends on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphone sensing of driving conditions for vehicle lane-level localization on highways. We analyze the driving traces collected from real driving environments, finding that each type of lane change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization (L3) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane change behaviors. Then a Gaussian Distribution is employed to track vehicles on lane-level with tolerance of false detections. Extensive experiments demonstrate that L3 is accurate and robust in real driving environments. The experimental results show that, on average, L3 achieves accuracy of 91.49% on lane change detection and 86.94% on lane-level localization.

Mobile crowd sensing (MCS) has become a promising paradigm to perceive the environment with the help of smart phones. A monetary award is an effective method to incentivize participants to contribute good quality data. However, the reward for long-term data collection in the wide area could be unaffordable for a MCS requester. In this paper, we enable data requesters to recruit sensing workers in a group buying way. Requesters with similar data demand can form a group to share the payment and sensing data, which reduces cost and increases the coverage of sensing data. Agents from different groups compete in buying sensing data from sensing workers. We propose TGBA, a Two-phase Group Buying based Auction mechanism for MCS. In phase I, requesters submit bids to their group agent, the group agent decides the winners and clearing prices. In phase II, group agents attend the auction for recruiting workers. TGBA is computationally efficient and possesses good economic properties such as individual rationality, budget balance and truthfulness.

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.

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users' sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, the anonymization techniques cannot provide strong and provable privacy guarantees. Differential Privacy is the only framework that can provide strong and provable privacy guarantees. However, we find that existing studies on differential privacy fail to provide effective data utility on multi-dimensional and large-scale datasets. Aim at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility. Furthermore, the theoretical analysis shows the noise variance of our sanitization algorithm is O(logo(1)n/ϵ2) which indicates the algorithm can achieve a higher data utility on large-scale datasets. Moreover, from the results of extensive experiments on an large-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility.

Wireless local area networks (WLANs) are pervasive but crowded nowadays. It is of great importance for access points (APs) to adapt to the changing traffic conditions. Exiting approaches for channel selection largely rely on local channel assessment and adopt greedy selection strategies. They suffer a major limitation that an AP fail to take various traffic demands of clients into account. We have witnessed that wireless sensor networks are increasingly deployed everywhere. A ZigBee sensor operates on the 2.4G radio spectrum which overlaps the spectrum used by most WiFi APs. As a result, a ZigBee sensor is able to sense the traffic of different AP channels. Motivated by this important observation, we present the design, implementation and evaluation of OutSence, a system that enables APs to takes traffic volumes of clients into account. It makes use of channel utilization sensed by ZigBee sensors and allows an AP to select a channel of good performance. The salient feature of OutSence is that it exploits in-situ ZigBee sensors for APs to quickly adapt to short-term traffic variations (e.g., order of minutes). We have fully implemented OutSence on Telos B sensor nodes and off-the-self APs. Extensive experiments have been conducted and conclusive results demonstrate that OutSence effectively improves overall WLAN performance.

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.

Mobile crowdsourcing to smartphones has emerged as a compelling paradigm for collecting sensing data over a vast area for various monitoring applications. It is of paramount importance for mobile crowdsourcing to provide incentive mechanisms. State-of-the-art auction mechanisms for mobile crowdsourcing are deterministic in the sense that given the real costs of the smartphone users, a fixed set of smartphone users are recruited for performing sensing tasks. This leads to serious issues including reduced diversity of sensing devices and starvation of some users. In this paper, we propose an approximate-truthful randomized combinatorial auction mechanism for the social cost minimization problem, which is NP-hard. We design an approximate task allocation algorithm that is near optimal with polynomial-time complexity and use it as a block to construct the whole randomized auction mechanism. We carry out numerical studies and show that our randomized auction mechanism achieves approximate truthfulness, individual rationality, and high computation efficiency. Moreover, the proposed mechanism increases diversity of devices and prevents starvation of some smartphones.

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers' awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast U-turn, Turning with a wide radius and Sudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a finegrained abnormal Driving behavior Detection and iDentification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. By extracting unique features from readings of smartphones' accelerometer and orientation sensor, we first identify sixteen representative features to capture the patterns of driving behaviors. Then, a machine learning method, Support Vector Machine (SVM), is employed to train the features and output a classifier model which conducts fine-grained identification. From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments, we show that D3achieves an average total accuracy of 95.36%.

Elevated roads are built worldwide to alleviate urban traffic congestions. An elevated road may overlap with the ground surface road. This poses a great challenge to auto navigation systems. It is particularly difficult for auto navigation systems to determine if an vehicle is running on an elevated road because altitude readings from GPS (Global Positioning Systems) receivers are of low accuracy. Without correct positioning of elevated or surface roads, an auto navigation system may recommend wrong routes which may lead to serious traffic accidents or cause drivers' anxiety. In this paper, we design a smartphone-based system called DEEL for detecting road elevation as the vehicle moves on urban roads. HMM-based models are built to characterize the patterns of vehicle dynamics with respect to the elevation of the road that the vehicle is moving on. The system leverages smartphone sensors to sense the instantaneous vehicle dynamics, including the 3D acceleration, the speed, and the number of perceived GPS satellites. Based on the sensed dynamics, it determines whether the vehicle is running on an elevated road or not in an online fashion. We have successfully implemented our system in Android-based smartphones. Extensive experiments demonstrate the system achieves a high accuracy of 92.1% with strong robustness to variations of vehicles, drivers and roads.

Sensing data collection is of great significance in mobile crowd sensing. We consider the problem of online maximization of data utility of the sensing data collected from smartphone users under a time average budget constraint. There are several major challenges, including random and unknown phone contexts, budget constraint and existence of data redundancy. Little work has studied this crucial problem. In this paper we formulate it as an online scheduling problem which determines sensing decisions for smartphones that are distributed over different regions of interest We first propose a centralized online scheduling algorithm based on stochastic optimal control. To address the poor scalability issue of the centralized algorithm, we further propose a distributed online scheduling algorithm based on distributed correlated scheduling. It does not require any priori knowledge of future smartphone contexts, and hence sensing decisions can locally be made by individual smartphones. Rigorous theoretical analysis show that our algorithms can achieve a time average data utility that is within O{1/V) of the theoretical optimum. Extensive simulations demonstrate that our algorithms produce high time average data utility.

When road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone. In this paper, we propose a vehicle Lane-Level Localization (L^3) system, which utilizes smartphone sensors to real-time locate the vehicle on lane-level on highways. The system is based on the observation that each type of lane change has its unique pattern on the vehicle's lateral acceleration. Extensive experiments demonstrate that L^3 is both accurate and robust in real driving environments.

With the rapid development of information technology, mobile devices have exhibited increasing popularity in recent years. To support the anytime-anywhere service model of mobile devices, one important problem related to the screen display arises when using these devices (e.g., smartphone and tablet) under various lighting conditions. On one hand, it is hard for users to see the display clearly under strong lighting conditions (e.g., sunlight). On the other hand, the screen appears dazzling under weak lighting conditions. This problem related to the mobile device display can significantly degrade user experience and undermine the successful deployment of the anytime-anywhere mobile service model. Existing solutions mainly focus on the automatic adjustment of brightness level under different light conditions. We show that merely utilizing brightness level to solve the display problem is not enough to maintain the user experience under both strong and weak lighting scenarios through experimenting with over 200 volunteers. In this work, we take a different approach by investigating automatic color scheme adjustment to improve user experience. We find that Readability, Comfort level and Similarity are major factors that contribute to user experience. In recognizing these problems, we propose a system, ColorVert, which utilizes the DKL color space to adaptively transform color schemes by sensing ambient light to improve user experience under various lighting scenarios. Our experimental evaluation with over 200 precipitants and various mobile devices demonstrates that ColorVert is more effective in both maintaining as well as improving user experience compared with the existing automatic brightness adjustment system.

3G technology has stimulated a wide variety of high-bandwidth applications on smartphones, such as video streaming and content-rich web browsing. Although having those applications mobile is quite appealing, high data rate transmission also poses huge demand for power. It has been revealed that the tail effect in 3G radio operation results in significant energy drain on smartphones. Recent fast dormancy technique can be utilized to remove tails but, without care, can degrade user experience. In this paper, we propose a novel scheme SmartCut, which effectively mitigates the tail effect of radio usage in 3G networks with little side-effect on user experience. The core idea of SmartCut is to utilize the temporal correlation of packet arrivals to predict upcoming packets, based on which unnecessary high-power-state tails of radio are cut out leveraging the Fast Dormancy mechanism. Both prototype experiment and extensive trace-driven simulation results demonstrate the efficacy of SmartCut design. On average, SmartCut can save up to 43 percent network energy while having little side-effect to user experience.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50 percent to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, energy-efficient engine (E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to user preference. The goal of E3is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3design. On average, E3can save up to 60 percent of the energy consumed by CPU and 35 percent of the overall energy consumption.

This paper focuses on workload allocation among mobile nodes in a mobile crowd sensing system. We take both two important objectives into account, including load balance and sensing data utility maximization. However, workload allocation achieving both objectives is particularly challenging. First, there is an intrinsic tradeoff between load balance and utility maximization. The system should strike a good balance between the two important objectives. Second, the number of mobile users can be large. A simple exhaustive search of workload allocation results can be prohibitively expensive. In this paper, we model workload allocation as a Nash bargaining game. We propose a distributed algorithm to solve the Nash bargaining game and determine the workload to each individual smartphone. It effectively decomposes the complex optimization problem into subproblems, and obtains the workload allocation solution by an iterative procedure imitating the bargaining process. This distributed algorithm can achieve a fair tradeoff between workload balance and data utility maximization, which is provably Pareto-efficient. We have conducted extensive simulations, and the results demonstrate that our algorithm achieves Pareto optimality and fairness between the two important objectives.

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.

In this paper we study a social welfare maximization problem for spectrum sharing in cognitive radio networks. To fully use the spectrum resource, the spectrum owned by the licensed primary user (PU) can be leased to secondary users (SUs) for transmitting data. We first formulate the social welfare of a cognitive radio network, considering the cost for the primary user sharing spectrum and the utility gained for secondary users transmitting data. The social welfare maximization is a convex optimization, which can be solved by standard methods in a centralized manner. However, the utility function of each secondary user always contains the private information, which leads to the centralized methods disabled. To overcome this challenge, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our proposed algorithm converges to the optimal solution. Numerical simulation results are presented to show that our proposed algorithm achieves optimal social welfare and fast convergence speed.

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer's readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points, where the true vehicle speed is known. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns, stopping and passing through uneven road surfaces, to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is 1.32mph, and the offline speed estimation error is as low as 0.75mph. Whereas the average error of GPS is 3.1mph and 2.8mph respectively.

Participatory sensing has gained increasing attention in recent years as it is promising for empowering large-scale monitoring and knowledge discovery, leveraging a crowd of public smartphone workers. In a participatory sensing system, the platform recruits workers to participate in multiple sensing processes. However, sensing processes have limited budgets. A smartphone is only satisfied when it receives a reward large enough to cover its cost caused, e.g., by resource consumption. Considering the rationality of smartphone workers, it is of great importance for the participatory sensing system to satisfy as many workers as possible, in order to stimulate more smartphone participation. In this paper, we propose a general sensing processes participation game framework with heterogenous workers and heterogenous sensing processes. We show that it is NP-hard to find a sensing processes participation solution which maximizes the number of satisfied workers. Inspired by the finite improvement property of the game, we design and implement an algorithm of sensing processes participation, which guarantees to reach a pure Nash equilibrium in polynomial time, and allows workers to change their strategy profiles asynchronously. Simulation results show that our algorithm is effective and efficient.

Data sharing in the cloud, fueled by favorable cloud technology trends, has emerging as a promising pattern in regard to enabling data more accessible to users in a convenient manner. To achieve data sharing, enterprises and customers in increasing numbers keep their data stored into cloud server. In this paper, we focus on seeking a solution that allows secure and effective access to the cloud data. We propose an effective and flexible privacy-preserving data policy, P2E, utilizing ciphertext policy attribute-based encryption (CP-ABE) and combining it with technique of identity-based encryption (IBE). In addition to ensuring strong data sharing security, the policy succeeds in preserving the privacy of cloud users. Security analysis indicates that the proposed policy is security and enforces fine-grained access control and full collusion resistance simultaneously. Furthermore, our performance analysis and experimental results show that P2E is as light as possible.

Traffic load is often unevenly distributed among the access points (APs) in enterprise WLANs. Such load imbalance results in sub-optimal network throughput and unfair bandwidth allocation among users. In this paper, we collect real traces from over twelve thousand WiFi users in Shanghai Jiao Tong University. Through intensive data analysis, we find that user behavior like leaving together may cause significant AP load imbalance problem. We also observe from the trace that users with similar application usage have the potential to leave together. Inspired by those observations, we propose an innovative scheme, Social-aware AP Selection Scheme(S3), which can actively learn the sociality information among users trained with their history application profiles and elegantly assign users based on the obtained knowledge. Both real prototype implementation and simulation results show that S3 is feasible and can achieve 41.2% balancing performance gain on average.

Cloud storage services enable users to remotely store their data and eliminate excessive local installation of software and hardware. One critical issue is how to enable a secure data collaboration service including data access and update in cloud computing. A data collaboration service is to support the availability and consistency of the shared data among multi-users. In this paper, we propose a secure and efficient data collaboration scheme SECO. In SECO, we employ a two-level hierarchical identity based encryption (HIBE) to guarantee data confidentiality against untrusted cloud. This paper is the first attempt to explore secure cloud data collaboration service that precludes information leakage and enables a one-to-many encryption paradigm, data writing operation and fine-grained access control simultaneously. Security analysis indicates that the SECO enforces fine-grained access control and collusion resistant. Extensive performance analysis and experiment results demonstrate that SECO is highly efficient and low overhead on computation and communication.

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50% to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, Energy-Efficient Engine(E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to individual user preference. The goal of E3 is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3 design. On average, E3 can save up to 58% of the energy consumed by CPU and 34% of the overall energy consumption.

Cloud computing has emerging as a promising pattern for data outsourcing and high-quality data services. However, concerns of sensitive information on cloud potentially causes privacy problems. Data encryption protects data security to some extent, but at the cost of compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of encrypted data over cloud. In this paper, we focus on addressing data privacy issues using SSE. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a two-round searchable encryption (TRSE) scheme that supports top-$(k)$ multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext. As a result, information leakage can be eliminated and data security is ensured. Thorough security and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

In this paper, we focus on data privacy of searchable symmetric encryption (SSE) in cloud computing. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness and then prove server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. In order to solve this problem, we propose a two round searchable encryption (TRSE) scheme, supporting top-k multi-keyword search, in which novel technologies, i.e., homomorphic encryption and vector space model, are employed. Vector space model helps to provide sufficient search accuracy, and homomorphic encryption enables users involve in the ranking while majority of computing work is still done on server-side by operations only on ciphertext. In this way, information leakage can be eliminated and data security is ensured. Thorough security analysis and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

BitTorrent has emerged as a very popular peer-to-peer file sharing system, which uses an embedded set of incentive mechanisms to encourage contribution and prevent free-riding. However, BitTorrent's ability to prevent free-riding needs further study. In this paper, we present a fluid model with two different classes of peers to capture the effect of free-riding on BitTorrent-like systems. With the model, we find that BitTorrent's incentive mechanism is successful in preventing free-riding in a system without seeds but may not succeed in producing a disincentive for free-riding in a system with a high number of seeds. The reason for this is that BitTorrent does not employ any effective mechanisms for seeds to effectively guard against free-riding. Therefore, we propose a seed bandwidth allocation strategy for the BitTorrent system to reduce the effect of seeds on free-riding. Finally, simulation results are given that validate what we have found in our analysis and demonstrate the effectiveness of the proposed strategy.

BitTorrent is a very popular P2P file sharing system. It has been successful at distributing large files quickly and efficiently. Embedded in BitTorrent is a set of incentive mechanisms to encourage sharing and contribute, and prevent systematic free-riding. In this paper, a fluid model with two classes of peers is used to capture the effect of free-riding in a BitTorrent system. With the model, we explore how does free-riding influenced the BitTorrent system. From results, it is shown that BitTorrent mechanism is successful to guard against free-riding. Finally, we discuss the dying process of a BitTorrent system and the probability of system dead that is induced by free-riding