The surge in large language models (LLMs) has fundamentally reshaped the landscape of GPU usage patterns, creating an urgent need for more efficient management strategies. While cloud providers employ spot instances to reduce costs for low-priority (LP) tasks, existing schedulers still grapple with high eviction rates and lengthy queuing times. To address these limitations, we present GFS, a novel preemptive scheduling framework that enhances service-level objective (SLO) compliance for high-priority (HP) tasks while minimizing preemptions to LP tasks. Firstly, GFS utilizes a lightweight forecasting model that predicts GPU demand among different tenants, enabling proactive resource management. Secondly, GFS employs a dynamic allocation mechanism to adjust the spot quota for LP tasks with guaranteed durations. Lastly, GFS incorporates a preemptive scheduling policy that prioritizes HP tasks while minimizing the impact on LP tasks. We demonstrate the effectiveness of GFS through both real-world implementation and simulations. The results show that GFS reduces eviction rates by 33.0%, and cuts queuing delays by 44.1% for LP tasks. Furthermore, GFS enhances the GPU allocation rate by up to 22.8% in real production clusters. In a production cluster of more than 10,000 GPUs, GFS yields roughly $459,715 in monthly benefits.

Touchscreen interfaces have become ubiquitous in modern household appliances, yet they remain largely inaccessible to the blind due to their lack of tactile feedback. Existing accessibility solutions, such as tactile overlays, remote control applications, and proximity-based interfaces, suffer from failing to capture real-time appliance states, limited generalizability, indirect interactions, or strong environmental dependencies. In this work, we introduce SingSprite a novel appliance interaction system that allows blind users to seamlessly identify and control appliances by recognizing their unique acoustic signatures. The key insight is that many appliances emit distinct, device-specific humming sounds during operation, primarily generated by internal power supply components. SingSprite captures these acoustic fingerprints using commodity smartphone microphones, applying a tailored background noise cancellation scheme to enhance signal clarity. To address the issue of low mobile sampling rates, we integrate a Variational Mode Decomposition (VMD) approach with an autoencoder framework for effective feature extraction. The HumNet classifier, which concurrently classifies appliance types and operational states, demonstrates robust performance with an F1 score of 0.95 across a diverse set of 100 appliances. Additionally, user studies conducted with blind participants confirm the system’s usability and its practical effectiveness in real-world scenarios. Additionally, user studies conducted with blind participants confirm the system’s usability and its practical effectiveness in real-world scenarios.SingSprite offers a hardware-free and scalable accessibility solution, providing proximity-aware interaction in smart homes without the need for additional infrastructure, paving the way for more inclusive and intuitive environments for blind users. Using natural device acoustics, SingSprite pioneers a universal, proximity-sensitive interaction paradigm that significantly advances accessibility in smart home environments, without requiring any modifications to existing infrastructure.

Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.

Smart devices are proliferating in every aspect of our lives, providing convenience but also exposing us to the risk of information leakage at any moment. Attackers can monitor the user and infer private information such as personality and preferences by stealing the behavioral information. In this paper, we investigated the potential threat of information stealing via the leakage current of laptops and electrodes in wearable devices (e.g., smart watches and bracelets). Specifically, the leakage current in the laptop adapter can flow from the metal casing into the human body and be collected by electrodes in wearable devices when the user is using a laptop with a metal casing (e.g., MacBook). We verified the correlation between leakage current and the working states of the laptop, where different operations corresponding to different CPU instructions can generate different leakage currents. Based on this, we proposeLeakThief, a system that consists of three components: leakage current detection, application operation detection, and application recognition. The experiments in a real-world environment demonstrated that the proposed system can recognize 25 common applications with high accuracy, including launching-based (96.4%) and in-application operation-based recognition (81.2%).

Real-time audio streaming transmission and processing play a crucial role in time-sensitive applications such as food delivery services and ride-hailing platforms, where rapid response is essential. However, existing server-based audio streaming architectures struggle to handle the high concurrency of massive mobile devices efficiently. Traditional compression methods like MP3 and AAC offer limited compression ratios, while deep learning-based approaches often fail to meet the real-time transmission demands of edge computing environments. In this paper, we propose a novel edge-to-server audio streaming architecture that leverages Mel filter bank spectral features to achieve ultra-high compression efficiency. Our system integrates audio denoising, Mel feature extraction, and quantization-based compression at the edge, effectively suppressing environmental and device-induced noise while achieving an extreme compression ratio of 0.39% relative to the original uncompressed audio. Compared to conventional methods like MP3, our approach further reduces the file size by 96.1%. The decompressed Mel features remain task-independent, enabling seamless support for various general-purpose audio processing tasks in the server. We evaluate our system across three key audio tasks: speech recognition, speech emotion recognition, and audio classification. Extensive experiments on five different mobile devices demonstrate a 93.10% reduction in transmission latency at 1 Mbps bandwidth compared to 64 kbps MP3 audio, while maintaining task performance within a 5% deviation from state-of-the-art (SOTA) models across six mainstream audio datasets. These results highlight the efficiency, robustness, and scalability of our approach for real-time edge-to-server audio processing.

Protecting privacy-sensitive hardware usage on mobile devices is crucial. Although mobile operating systems (OSs) and smartphone manufacturers have set the permission settings, attackers can evade these defenses using covert methods, enabling malicious camera recording, microphone eavesdropping, and screen capture. Electronic devices emit unique yet weak electromagnetic interference (EMI) signals when accessing privacy-sensitive hardware. But, these signals are easily affected by foreground application activities and geomagnetic fluctuations caused by device movement. Our prior work showed that supervised learning can extract EMI features correlated with privacy hardware states from complex magnetometer readings, but it requires substantial labeled data, limiting practical deployment to new device models or OS versions. To eliminate this reliance on labeled data, this paper proposes a multimodal contrastive learning framework that leverages the device's built-in magnetometer and synchronized system logs as dual-modal inputs. Through self-supervised training, the framework can learn the intrinsic associations between EMI features and the operating states of privacy-sensitive hardware. Building on this, we design an EMI-based eavesdropping classifier that can analyze a user device's magnetometer readings offline to detect covert eavesdropping activities. Experimental results show that the proposed method can effectively identify eavesdropping behavior related to access to camera, microphone, and screen recording data. Testing across ten diverse mobile devices achieved an average classification accuracy of 89.1% on Android devices and 88.5% on iOS devices for identifying the specific hardware being eavesdropped upon.

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.

Accurate workload prediction is essential to ensure application Quality of Service (QoS), cost efficiency, and compliance with Service Level Agreements (SLAs) during cloud-based deployment. However, existing methods struggle to achieve accurate forecasts across multiple temporal scales and often fail to generalize well with limited historical data. To tackle these problems, we propose LLM4Load-Turbo, a Prompt-Driven LLM Framework with Knowledge Distillation for Efficient Multi-Scale Workload Prediction. Firstly, we design a structured prompt with dataset introduction, task description, and workload features characterization to extract multi-scale features. Secondly, we introduce a cross-modality alignment mechanism combined with label embedding to further enhance predictive accuracy and generalization, effectively mitigating the container cold-start problem. Thirdly, we propose a two-level knowledge distillation strategy, enabling LLM4Load-Turbo to maintain high accuracy while substantially reducing inference latency, memory footprint, and computational cost. Specifically, our framework achieves up to an 89.27% improvement in inference speed and a 93.30% reduction in model parameter scale compared to state-of-the-art baselines. Extensive experiments on four real cloud workload datasets validate the effectiveness of our framework. For multi-scale workload prediction, LLM4Load-Turbo improves up to 50.44%. For container cold-start scenarios, LLM4Load-Turbo improves up to 93.54%. These results demonstrate the potential of LLM4Load-Turbo to enable dynamic and efficient resource management in modern cloud systems.

As cardiovascular diseases and arrhythmias rise globally, pacemakers have become a critical therapeutic option for managing cardiac rhythm disorders. Accurate identification of pacemaker implantation sites is essential for personalized pacing therapy and optimal clinical outcomes. While 12-lead electrocardiogram (ECG) signals provide a non-invasive means to infer implantation locations, they are susceptible to noise and morphological variability, posing challenges for high-accuracy localization. To advance data-driven solutions in this domain, we present PILDE, the first publicly available dataset specifically designed for pacemaker implantation site identification, comprising 12-lead ECG recordings from 733 patients across four distinct implantation locations. Based on this dataset, we propose STELLAR, a novel deep learning framework that integrates a Spatio-Temporal Lead-Harmonic Mechanism to model both the temporal dynamics of ECG waveforms and the spatial coherence across leads. Extensive experiments demonstrate that STELLAR outperforms conventional deep models-including CNN, LSTM, and Transformer baselines-on both the PILDE and PTB-XL datasets. Specifically, STELLAR achieves an average accuracy improvement of 10.45 % on PILDE and 14.19 % on PTB-XL, with significant gains in sensitivity and F1-score for minority classes. These results highlight the robustness and precision of STELLAR in automating implantation site identification, offering a promising tool for pre-procedural planning and clinical decision support. The source code and dataset access information will be made publicly available.

Magnetic resonance imaging (MRI) provides highquality soft tissue contrast images and is crucial in medical diagnosis. However, systems face trade-offs between image resolution and scan time. Low-resolution MRI scans reduce scan time and patient burden but lose critical details needed for accurate diagnosis. To address this problem, super-resolution techniques have been developed to improve the clarity of lowresolution input images. Single-image super-resolution (SISR), which minimizes patient scanning time, has gradually become a research focus, but existing methods often struggle to balance the reconstruction of low-frequency structural information and high-frequency details. In this paper, we propose a novel superresolution up-sampling pipeline that enhances both the highfrequency and low-frequency components of magnetic resonance imaging. In addition, we introduce an enhanced loss function that includes symmetry and edge constraints to preserve critical structural details for improved diagnostic accuracy. The extensive experiments across multiple datasets validate the effectiveness of our SISR model. Source code will be made publicly available.

Electroencephalography (EEG) analysis plays a crit-ical role in applications such as brain-computer interfaces, epilepsy detection, and cognitive state recognition. However, EEG data are often limited in volume due to high acquisition costs and exhibit complex spatio-temporal coupling across multiple channels. Convolutional Neural Networks (CNNs) have become the predominant approach for EEG signal analysis, owing to their effectiveness in local feature extraction and compatibility with grid-like sensor topologies. Nevertheless, the locality as-sumption inherent in conventional CNN s restricts their ability to capture functional connectivity and dynamic dependencies between spatially distant channels. To address this limitation, we propose NLCTCN, a novel non-local Temporal Convolutional Network that leverages a hierarchical greedy strategy to identify and exploit long-range correlations in multi-channel time series. We further introduce a new fusion scheme, integrated into an end-to-end lightweight CNN architecture to effectively combine these non-local interactions and optimize their configurations for improved predictive performance. Experimental results are presented on 10 real-world EEG datasets. These datasets cover human physiology, cognitive tasks, and clinical applications. The results show that NLCTCN significantly outperforms state-of-the-art methods. On average, NLCTCN achieves an accuracy improvement of 7.5 %. These results validate the effectiveness and superiority of the proposed approach in modeling non-local spatio-temporal dynamics under data-scarce and multi-channel conditions.

We introduce M 2 Silent, which enables multi-user silent speech interactions in shared spaces using multi-directional speakers.Ensuring privacy during interactions with voice-controlled systems presents significant challenges, particularly in environments with multiple individuals, such as libraries, offices, or vehicles.M 2 Silent addresses this by allowing users to communicate silently, without producing audible speech, using acoustic sensing integrated into directional speakers.We leverage FMCW signals as audio carriers, simultaneously playing audio and sensing the user's silent speech.

Elastic Block Storage (EBS) has a pivotal role in modern data center infrastructure, providing reliable, high-performance and flexible block storage service to users. In Alibaba Cloud, EBS is the most widely used service and has been supporting the operation of millions of virtual disks. However, even with layers of load balancing and caching, we still observe significant traffic skewness across the EBS stack. This motivates us to comprehensively investigate symptoms and root causes behind the traffic patterns and, more importantly, explore the fixes for the identified issues.

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems’ user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes AMSER, a real-time speech emotion recognition framework using signal compression and task offloading. AMSER utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24x faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 69.83% emotion recognition accuracy (IEMOCAP).

As application migration to the cloud becomes the mainstream way of application deployment, accurate workload prediction is critical to ensure the quality of service (QoS) and cost-efficiency of the applications and meet service level agreements (SLAs) with users. Short-term workload prediction can handle workload fluctuations over a short duration, while long-term prediction can capture trend and periodic changes for workload. However, existing studies are unable to deal with long-term forecasts in multiple scales effectively; moreover, new containers lack historical data, leading to inaccurate prediction, while existing methods perform poorly due to a lack of generalization ability. To tackle these problems, we propose LLM4Load, an LLM Prompt-Driven Framework for Multi-Scale Workload Prediction. Firstly, we design a structured prompt with dataset introduction, task description, and workload features characterization to extract multi-scale features. Secondly, we introduce a cross-modality alignment mechanism combined with label embedding to further enhance performance. Leveraging the generalization capability of LLM, we also solve the container cold-start problem. The abundant experiments on four real cloud workload datasets validate the effectiveness of LLM4Load. For multi-scale workload prediction, LLM4Load improves up to 42.40%. For container cold-start scenarios, LLM4Load improves up to 93.72%. These results highlight the potential of LLM4Load to drive dynamic and efficient resource management in modern cloud systems.

Graph Neural Networks (GNNs) excel in many applications but struggle when trained with noisy labels, especially as noise can propagate through the graph structure. Despite recent progress in developing robust GNNs, few methods exploit the intrinsic properties of graph data to filter out noise. In this paper, we introduce ProCon, a novel framework that identifies mislabeled nodes by measuring label consistency among semantically similar peers, which are determined by feature similarity and graph adjacency. Mislabeled nodes typically exhibit lower consistency with these peers, a signal we measure using pseudo-labels derived from representational prototypes. A Gaussian Mixture Model is fitted to the consistency distribution to identify clean samples, which refine prototype quality in an iterative feedback loop. Experiments on multiple datasets demonstrate that ProCon significantly outperforms state-of-the-art methods, effectively mitigating label noise and enhancing GNN robustness.

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%.

Non-invasive continuous glucose monitoring for diabetes patients remains challenging despite ongoing interest. This paper presents CGMM, a novel non-invasive wireless glucose monitoring system integrated into wearable devices. It features a specially designed metasurface that couples with the wearable's antenna and tissue fluid beneath the skin, amplifying frequency response changes caused by subtle glucose concentration variations. To address individual tissue variability and optimize the passive metasurface design, we develop a tunable metasurface and a one-shot calibration method to obtain the impedance for optimal resonance in glucose sensing environments with unknown parameters. The calibrated impedance is then used for the inverse design and fabrication of an economical passive metasurface. We implement prototypes of CGMM and conduct extensive experimental evaluations. In human experiments involving ten participants using the prototype with LibreVNA, the overall performance is quantified with relative errors ranging from -5.02% to 6.93% and an RMSE of 9.65 mg/dL.

Diffractive neural networks (NNs) have garnered attention for directly implementing wireless signal processing at the physical layer. However, they are limited by a constrained weight learning space and activation functions, which restricts their data processing capabilities. To address this, we propose an RF circuit-based weighted diffraction NN (WDNN) that rivals digital NNs in processing ability. We design a weighted asymmetric RF coupler unit that, when stacked into a network, enables diffractive propagation with arbitrary connection weights. Additionally, an activation module is introduced that utilizes RF amplifiers operating in their nonlinear regions. We validate the effectiveness of the proposed WDNN through three tasks: 32-level amplitude modulated (AM) signal decoding, 31-class angle of arrival (AoA) estimation, and 2-class Wi-Fi based fall detection. After training, WDNN achieves the accuracy of 98.5%, 93.7%, and 90.8% in the AM decoding, AoA estimation, and fall detection tasks, respectively; while the diffractive NN SOTA achieves only 21.6%, 16.9%, and 63.3%. We also implement the prototypes of WDNN and SOTA, and real-world experimental results demonstrate that our method achieves an average accuracy improvement of up to 76.85% across various tasks compared to SOTA.

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.

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.

This paper presents MODepth, a multi-frame monocular depth estimation system based on the controlled motion of an optical image stabilization (OIS) module. By actively injecting acoustic signals, we induce regular translational movements of the OIS lens, resulting in controllable camera pose changes and simplifying inter-frame pose estimation. Leveraging multi-frame images captured under OIS-controlled lens movements, we design a high-precision depth estimation network, MODNet, and introduce the principal point offset estimation module and pose estimation modules to fully exploit geometric information across frames. To validate the effectiveness of our approach, we collect a new dataset MODdata with 1100 samples in nearly 220 indoor scenarios and benchmark our model as an OIS-based multi-frame depth estimation method, comparing it to ground truth obtained from a depth sensor and other state-of-the-art monocular depth estimation algorithms. Our method achieves competitive or superior performance compared to fully supervised baselines, reaching an RMSE of 0.439, which outperforms all evaluated methods, demonstrating that self-supervised fine-tuning with OIS-induced parallax is a viable alternative to ground-truth supervision. Code and dataset are available at: https://github.com/liangjindeamo-yuer/MODEPTH

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes Amser+, a real-time speech emotion recognition framework using signal compression and task offloading. Amser+utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24× faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 72.85% emotion recognition accuracy (IEMOCAP).

With the sheer volume of data in today’s world, archive storage systems play a significant role in persisting the cold data. Due to stringent cost concerns, one popular design is to organize disks into groups and periodically switch them to be powered on for serving user requests. Scheduling thus becomes critical for both CapEx and performance. Unfortunately, field results indicate that existing schedulers can be often suboptimal. Our further analysis suggests that the main reason is the mismatch between the ever-changing workloads and the fixed set of coarsely-configured parameters in current heuristic-based schedulers. In this article, we propose MasterPlan , a reinforcement learning (RL) based scheduler for archive storage systems. By identifying the unique characteristics of archive storage service, we design a state space and reward function for the RL agent. MasterPlan includes a continuous action encoding approach to guarantee efficient exploration, and a meta adaptation module to extract features of workload series. Experiments show that MasterPlan can achieve 1.25× throughput, 2.16× 99 th latency and 1.47× power draw improvement compared to existing solutions.

An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studies, the key to efficient auto-scaling lies in accurately learning performance patterns, i.e., the relationship between performance metrics and workloads in data-driven schemes. However, we notice that there are two significant challenges in characterizing performance patterns for large-scale microservices. Firstly, diverse microservices demonstrate varying sensitivities to heterogeneous machines, causing difficulty in quantifying the performance difference in a fixed manner. Secondly, frequent version upgrades of microservices result in uncertain changes in performance patterns, known as pattern drifts, leading to imprecise resource capacity estimation issues. To address these challenges, we propose Humas, a heterogeneity- and upgrade-aware auto-scaling framework for large-scale microservices. Firstly, Humas quantifies the difference in resource efficiency among heterogeneous machines for various microservices online and normalizes their resources in standard units. Additionally, Humas develops a least-squares density-difference (LSDD) based algorithm to identify pattern drifts caused by upgrades. Lastly, Humas generates capacity adjustment plans for microservices based on the latest performance patterns and predicted workloads. The experiment results conducted on 50 real microservices with over 11,000 containers demonstrate that Humas improves resource efficiency and performance stability by approximately 30.4% and 48.0%, respectively, compared to state-of-the-art approaches.

Privacy-preserving federated learning (PPFL) is a promising secure distributed learning paradigm, which enables collaborative training of a global machine learning model through sharing encrypted local models instead of sensitive raw data. PPFL, however, is vulnerable to model poisoning attacks. Most existing Byzantine-robust PPFL solutions typically employ two non-colluding servers to achieve secure model detection and aggregation by executing interactive security protocols, which incur considerable computation and communication overheads. To tackle this issue, we propose an efficient and secure federated learning (ESFL) technique to accelerate the detection of poisonous models in PPFL. First, to improve computational efficiency, we construct a lightweight non-interactive efficient decryption functional encryption (NED-FE) scheme to protect the data privacy of local models. Then, to ensure high communication performance, we elaborately design a non-interactive privacy-preserving robust aggregation strategy, which efficiently detects the blind poisonous models and aggregates benign models. Finally, we implement ESFL and conduct extensive theoretical analysis and experiments. The numerical results demonstrate that ESFL not only achieves the confidentiality and robustness design goals but also maintains high efficiency. Compared with the baseline, ESFL effectively reduces the aggregation latency by up to 88%.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

Multivariate time series forecasting is applied in many domains, such as finance, transportation, and industry. The main challenge of precise forecasting lies in accurately capturing latent dependencies. Recent studies develop various frameworks to reduce computational complexity or to enhance the learning of intricate relationships, while lacking interpretability and generality. In this article, we aim to elucidate the capture of dependencies as the recognition of patterns. We believe that patterns can be formally described from two aspects: the shapes of segments that frequently repeat and the corresponding forms of repetitions. Drawing upon this idea, we design a multivariate time series forecasting model named PRformer , 1 which incorporates a pattern-oriented attention mechanism and a pattern-based projector. The attention mechanism can perceive different forms of repetitions by embedded with various similarity evaluation metrics between segments, and filter out noise from segments to extract potential patterns with a statistical-driven weighting scheme. The pattern-based projector is employed to form the forecasting results by deriving the representative patterns from the set of potential ones. By incorporating explicit definitions of patterns, PRformer is interpretable and general to various time series scenarios. Experimental results on seven datasets demonstrate that PRformer outperforms six state-of-the-art models by about 10.7% in forecasting accuracy.

Data imputation is crucial in the analysis of incomplete time series, such as forecasting and classification, which involves learning dependencies among the observed values to infer missing ones. As there are no ground truths for missing values, the challenge of time series imputation lies in preventing the model from overfitting to spurious correlations. In this article, we believe that ensuring dependency consistency between observed and imputed values in a sequence is paramount for data imputation. Based on this idea, we propose a model called IR 2 -Net , 1 which combines an incomplete representation mechanism (IRM) with an iterative reconstruction framework (IRF) to establish a closed-loop learning-validation imputation paradigm. Firstly, IRM facilitates the representation of dependencies in incomplete sequences while preserving their distributions and semantics, effectively preventing the model from capturing spurious correlations. Secondly, IRF enables the model to reconstruct identical complete sequences separately based on imputed and observed values, ensuring that the dependencies of imputed values remain consistent with those of the observed ones. We conduct experiments on four datasets and compare IR 2 -Net with seven state-of-the-art imputation models. The experiment results show that IR 2 -Net outperforms all the baselines by 4.1%–23.4% in terms of accuracy. Moreover, IRF and IRM are two general modules that can be easily integrated into two existing models, significantly enhancing their performance by 18.3%–42.0%.

Wearable devices, including smartwatches, are increasingly popular among consumers due to their user-friendly services. However, transmitting sensitive data like social media messages and payment QR codes via commonly used low-power Bluetooth exposes users to privacy breaches and financial losses. This study introducesTouchHBC, a secure and reliable communication scheme leveraging a smartwatch's built-in electrodes. This system establishes a touch-based human communication system utilizing a laptop's leakage current. As the transmitting device, the laptop modulates this current via the CPU. Simultaneously, the smartwatch, equipped with built-in electrodes, captures the current traversing the human body and decodes it. The modulation and decoding processes involve techniques such as amplitude modulation, variational mode decomposition, channel estimation, and retransmission mechanisms.TouchHBCfacilitates communication between laptops and smartwatches. Real-world tests demonstrate that our prototype achieves a throughput of$19.83bps$. Moreover,TouchHBCoffers the potential for enhanced interaction, including improved gaming experiences through vibration feedback and secure touch login for smartwatch applications by synchronizing with a laptop. Furthermore, the system can be integrated with high-throughput communication protocols such as Bluetooth, enhancing its scalability while maintaining a strong foundation of security.

Various characteristics of mobile applications (apps) and associated in-app services can reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to restrict access to data related to mobile app usage. This paper outlines a novel approach to extracting detailed app usage information by analyzing electromagnetic (EM) signals emitted from mobile devices during app-related tasks. The proposed system, MagSpy, recovers user privacy information from magnetometer readings that do not require access permissions. This EM leakage becomes complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. To address these challenges, MagSpy employs multiple techniques to extract and identify signals related to app usage. Specifically, the geomagnetic offset signal is canceled using accelerometer and gyroscope sensor data, and a Cascade-LSTM algorithm is used to classify apps and in-app services. MagSpy also uses CWT-based peak detection and a Random Forest classifier to detect PIN inputs. A prototype system was evaluated on over 50 popular mobile apps with 30 devices. Extensive evaluation results demonstrate the efficacy of MagSpy in identifying in-app services (96% accuracy), apps (93.5% accuracy), and extracting PIN input information (96% top-3 accuracy).

Acoustic tracking technology, leveraging the ubiquitous presence of speakers and microphones in commercial off-the-shelf (COTS) mobile devices, has become a versatile tool across various applications. However, current phase-based acoustic tracking methods encounter significant limitations in tracking fast movements, thereby restricting their practical utility. This paper identifies three practical challenges to enable fast hand motion tracking using acoustic signals: 1) high mobility, 2) low signal-to-noise ratio (SNR), and 3) variations in hardware frequency response. The high mobility introduces Doppler shift and phase ambiguity which is the primary cause of failure in fast movement tracking, while the latter two factors can further impair the tracking performance in practical scenarios involving high mobility. To address the high mobility issue, we effectively compensate the Doppler shift in the Channel Impulse Response (CIR) for better selection of channel taps and then propose a novel phase derivative approach to mitigate the phase ambiguity. To enhance the real-world robustness, we integrate multiple algorithms including an SNR enhancement algorithm inspired by time-domain beamforming and a hardware frequency response compensation approach that addresses both amplitude and phase distortions. Additionally, an LSTM-based distance reconstruction algorithm is further implemented to correct residual phase noise. Implemented on Android platforms under the name SwiftTrack+, our system demonstrates superior performance in tracking fast movements. Through extensive evaluations, SwiftTrack+ proves its efficacy across diverse scenarios, significantly broadening the scope and reliability of acoustic tracking applications.

Event matching is a key component in a large-scale content-based publish/subscribe system. The performance of most existing algorithms is easily affected by the subscription matching probability. In this paper, we propose a new data structure, named AWAW B+-Tree, which is based on the width of the predicates, to efficiently index the subscriptions. The most notable feature ofAW B+-Treeis its ability to combine the advantages of different matching methods, thus achieving high and robust performance in dynamic environments. First, we implement both a forward matching method (AFM) and a backward matching method (ABM) based onAW B+-Tree. Then, we introduce a hybrid matching method (AHM) that combines AFM and ABM. Moreover, we extendAW B+-Treein three aspects: approximate matching, string type matching, and fine-grained parallelization. We conducted extensive experiments to evaluate the performance of the proposed matching algorithms on synthetic and real-world datasets. The experiment results reveal that AHM achieves a reduction in matching time by up to 53.8% compared to the state-of-the-art method. Additionally, AHM exhibits improved performance robustness, with up to a 76.9% reduction in terms of the standard deviation of matching time. Particularly in dynamic scenarios, AHM is at least 2.3 times faster and 41.3% more stable than its counterparts. Furthermore, by implementing parallelization, the matching speed of 8 threads can be accelerated by 4.16 times compared to the single-thread matching speed.

Multi-hop cooperative task offloading (MCTO) allows resource-constrained edge clouds to collaborate and assist each other in completing computation-intensive tasks, such as training machine learning models through device-edge-cloud (DEC) computing. However, internal fake service attacks can pose a threat to the security and reliability of MCTO in DEC computing. In this paper, we propose a trust model based on a directed acyclic graph (DAG) and Proof-of-Work (PoW) to safeguard tasks against potential attacks. The edge node selection for task offloading involves two key steps: offloading confirmation and trust-based node selection. To mitigate the unreliability caused by internal fake service attacks during cooperative offloading, we propose a multi-hop offloading node selection algorithm based on the soft actor-critic (SAC) coalition. This algorithm helps identify trustworthy nodes for constructing secure offloading paths. Our experimental results demonstrate that the proposed algorithm effectively counters internal fake service attacks and significantly reduces cooperative offloading latency compared to existing leading approaches.

Visible light communication (VLC) is considered a solution to the scarcity of radio frequency communication resources due to its abundant spectrum resources and rapid intensity modulation capability. It has a wide range of applications in indoor positioning and intelligent transport systems. For example, in Connected and Autonomous Vehicle scenarios, VLC uses traffic lights to warn vehicles at different distances in abnormal situations, thus preventing potential traffic accidents. To facilitate fast, long-range VLC communication in such one-to-many communication scenarios, current systems typically use optical cameras or digital micro-mirror devices as receivers. However, there are several challenges associated with these devices. Optical cameras have a limited sampling rate, resulting in reduced effective throughput. Other receivers, such as digital micro-mirror devices, are relatively costly, which hinders their widespread use. In this paper, we propose a novel, low-cost, and high-speed VLC scheme. We use a low-cost material called Polymer-Dispersed Liquid Crystal as the measurement matrix, reducing the cost by 99% compared to digital micro-mirror devices. We implement hierarchical coding based on compressive sensing to reduce data redundancy and thus improve communication throughput. Empirical experiments conducted using four pho-to diodes at the receiver show a 120% improvement in overall throughput compared to existing one-to-many VLC systems.

Content-based publish/subscribe systems have the capability to achieve fine-grained data distribution, rapidly forwarding data from publishers to subscribers with specific requirements. The event matching algorithm is a fundamental component, quickly searching for subscriptions that match an event based on constraints defined by subscribers. As data scales continue to expand, there is a heightened demands for more efficient, robust, and versatile event matching algorithms. In this paper, we propose a novel data structure called the Dual-ended Interval Tree (DI-Tree). Firstly, given the splitting point, this data structure classifies intervals into three categories based on the joint distribution of their left and right endpoints in the attribute value domain. Furthermore, the DI-Tree utilizes blue and green nodes to store these three categories of intervals, resulting in enhanced indexing efficiency. Moreover, by utilizing the DITree to efficiently search matching and unmatching intervals, we develop innovative forward and backward event matching algorithms. Additionally, to enhance matching efficacy and reduce memory usage, we introduce key optimization techniques, focusing on improving node balance and bitset optimization. We conduct extensive experiments to evaluate the performance of DITree. When compared with five state-of-the-art event matching algorithms, the DI-Tree demonstrates an average reduction of up to 76.8 % in terms of matching time. This significant improvement highlights the effectiveness of our proposed strategies and the potential of DI-Tree in optimizing event matching performance.

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%).

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.

Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this issue, we propose an innovative indoor GNSS positioning system called GPMS, which uses passive metasurface technology to redirect GNSS signals from outdoors into indoor spaces. These passive metasurfaces are strategically optimized for indoor coverage by steering and scattering the GNSS signals across a wide range of incident angles. We further develop a novel localization algorithm that can determine which metasurface the signal goes through and localize the user using the set of metasurfaces as anchor points. A distinct advantage of our localization algorithm is that it can be implemented on existing mobile devices without any hardware modifications. We implement the prototype of GPMS, and deploy six metasurfaces in two indoor environments, a 10×50 m2 office floor and a 15×20 m2 lecture room, to evaluate system performance. In terms of coverage, our GPMS increases the C/N0 from 9.1 dB-Hz to 23.2 dB-Hz and increases the number of visible satellites from 3.6 to 21.5 in the office floor. In terms of indoor positioning accuracy, our proposed system decreases the absolute positioning error from 30.6 m to 3.2 m in the office floor, and from 11.2 m to 2.7 m in the lecture room, demonstrating the feasibility and benefits of metasurface-assisted GNSS for indoor positioning.

The demand for ultra-high-resolution imaging in mobile phone photography is continuously increasing. However, the image resolution of mobile devices is typically constrained by the size of the CMOS sensor. Although deep learning-based super-resolution (SR) techniques have the potential to overcome this limitation, existing SR neural network models require large computational resources, making them unsuitable for real-time SR imaging on current mobile devices. Additionally, cloud-based SR systems pose privacy leakage risks. In this paper, we propose M3Cam, an innovative and lightweight SR imaging system for mobile phones. M3Cam can ensure high-quality 16× SR image (4× in both height and width) visualization with almost negligible latency. In detail, we utilize an optical image stabilization (OIS) module for lens control and introduce a new modality of data, namely gyroscope readings, to achieve high-precision and compact optical flow estimation modules. Building upon this concept, we design a multi-frame-based SR model utilizing the Swin Transformer. Our proposed system can generate a 16× SR image from four captured low-resolution images in real-time, with low computational load, low inference latency, and minimal reliance on runtime RAM. Through extensive experiments, we demonstrate that our proposed multi-modal optical flow model significantly enhances pixel alignment accuracy between multiple frames and delivers outstanding 16× SR imaging results under various shooting scenarios. Code and dataset are available at: https://github.com/liangjindeamo-yuer/M3CAM

The convenient text input system is a pain point for devices such as AR glasses, and it is difficult for existing solutions to balance portability and efficiency. This paper introduces HandPad, the system that turns the hand into an on-the-go touchscreen, which realizes interaction on the hand via human capacitance. HandPad achieves keystroke and handwriting inputs for letters, numbers, and Chinese characters, reducing the dependency on capacitive or pressure sensor arrays. Specifically, the system verifies the feasibility of touch point localization on the hand using the human capacitance model and proposes a handwriting recognition system based on Bi-LSTM and ResNet. The transfer learning-based system only needs a small amount of training data to build a handwriting recognition model for the target user. Experiments in real environments verify the feasibility of HandPad for keystroke (accuracy of 100%) and handwriting recognition for letters (accuracy of 99.1%), numbers (accuracy of 97.6%) and Chinese characters (accuracy of 97.9%).

The distributed nature of multitier swarm attacks renders it more difficult for a single-tier intrusion detection system (IDS) to secure cross-tier computation offloading in multitier sensor edge cloud (SEC). To perceive and prevent such attacks, we model IDSs in different layers as an IDS federation network (IDFN) and present a generic framework to prevent cooperative attacks and reconfigure the defense strategy of IDFN across the three-tier SEC. The framework provides single-tier, two-tier, and three-tier dynamic awareness models based on the susceptible-infected-susceptible (SIS) dynamical equations to characterize the update process of message states to obtain the equilibrium solution between alarm messages and normal messages captured by IDSs. For swarm attack events from multitier SEC, we model the cross-tier cooperative interactions between IDSs and swarm attackers as an event–condition–action (ECA) regret learning game (ERLG) to achieve a distributed IDS reconfiguration to reduce the overall SEC alarm messages while ensuring the equilibrium of message states with the cooperation of IDSs. Simulation results demonstrate that our proposed scheme is superior to other reconfiguration mechanisms under swarm attacks in three-tier SEC.

For software-defined networking (SDN), multi-field packet classification plays a key role in the processing of flows, mainly involving fast packet classification and dynamic rule updates. Due to the increasing complexity and size of rulesets, it is becoming more difficult to design a packet classification algorithm which achieves fast lookup and update. In this paper, we propose a novel structure, PT-Tree, for packet classification with high overall performance. PT-Tree cascades the prefixes of multiple discriminatory bytes to achieve efficient partitioning of the ruleset, thereby reducing the search space and ensuring the performance of both lookup and update. Meanwhile, a multi-granularity priority-aware pruning mechanism (MPPM) based on PT-Tree filters out most of the candidate subsets, which further improves the lookup speed. In addition, we propose an auxiliary tree-based optimization method (ATOM) to cope with severely overlapping rules in the search space. Therefore, PT-Tree can better handle the case where the rules in certain fields are skewed. We conduct comprehensive experiments to evaluate the performance of PT-Tree. The results show that compared with the state-of-the-art, the lookup time of PT-Tree is reduced by at least 49.95% on average. Moreover, PT-Tree is also at least 7.13x and 33x faster than the baselines in terms of the update and construction speed on average, respectively. Meanwhile, the performance stability of PT-Tree on multiple rulesets improves by up to 13.68 times.

Packet classification, as a crucial function of networks, has been extensively investigated. In recent years, the rapid advancement of software-defined networking (SDN) has introduced new demands for packet classification, particularly in supporting dynamic rule updates and fast lookup. This paper presents a novel structure called DBTable for efficient packet classification to achieve high overall performance. DBTable integrates the strengths of conventional packet classification methods and neural network concepts. Within DBTable, a straightforward indexing scheme is proposed to eliminate rule replication, thereby ensuring high update performance. Additionally, we propose an iterative method for generating a discriminative bitset (DBS) to evenly partition rules. By utilizing the DBS, rules can be efficiently mapped in a hash table, thus achieving exceptional lookup performance. Moreover, DBTable incorporates a hybrid structure to further optimize the worst-case lookup performance, primarily caused by data skewness. The experiment results on 12 256k rulesets show that, compared to seven state-of-the-art schemes, DBTable achieves an overall lookup speed improvement ranging from 1.53x to 7.29x, while maintaining the fastest update speed.

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.

Low Earth Orbit (LEO) satellite communication is essential for wireless communication. While manufacturing and launching LEO satellites have become efficient and cost-effective, ground stations remain expensive due to complex designs for handling severe path losses and precise beam tracking. Hence, it is important to develop low cost and high-performance ground stations for widespread adoption of LEO satellite communication. Towards realizing this goal, we design a passive metasurface-enhanced LEO ground station system, named PMSat, combining metasurface's fine-grained beamforming capability with a small-size phased array's adaptive steering and focusing. For uplink, we jointly optimize the phase array codebook and uplink metasurface phase profile, and realize electronic steering by switching the codeword. We further jointly optimize the downlink metasurface phase profile to improve the focusing performance and enhance the received signal strength (RSS) over a wide range of incident angles. Our PMSat prototype consists of a single passive metasurface with 21 × 21 elements for uplink and 22 × 22 for downlink, along with 1 × 4 receiving and 1 × 4 transmitting phased array antennas. The effectiveness of our proposed PMSat is validated through extensive experiments, and results demonstrate that the optimized metasurface improves the SNR by 8.32 dB and 16.57 dB for uplink and downlink, respectively.

Smart devices are proliferating in every aspect of our lives, providing convenience but also exposing us to the risk of information leakage at any moment. Attackers can monitor the user and infer private information such as the personality and preferences by stealing the behavior information. In this paper, we investigated the potential threat of information stealing via the leakage current of laptop and electrodes in wearable devices (e.g. smart watches and bracelets). Specifically, the leakage current in the laptop adapter can flow from the metal casing into the human body and be collected by electrodes in wearable devices when the user is using a laptop with a metal casing (e.g. MacBook). We verified the correlation between leakage current and working states of the laptop, where different operations corresponding to different CPU instructions can generate different leakage currents. Based on this, we propose LeakThief, the system consists of three components, leakage current detection, application operation detection and application recognition. The experiments in real-world environment demonstrated that the proposed system is able to recognize 10 common applications with high accuracy, including launching-based (97.5%) and in-application operation-based recognition (83.8%).

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.

Content-based publish/subscribe systems are widely used in many fields.Event matching is the core component to achieve fine-grained content-based data distribution.Many efficient algorithms have been proposed to improve event matching performance.However, in large-scale content-based publish/subscribe systems, event matching is still the performance bottleneck of the entire system due to the need to perform a lot of operations, such as additions, comparisons and bitmarkings.In this paper, we explore to convert various nonlogical operations into efficient logical ones, and propose a bitsetbased optimization paradigm (BOP) for matching algorithms.On the one hand, BOP can eliminate expensive operations in the matching process, greatly improving matching performance.On the other hand, BOP can stabilize the performance of matching algorithms, ensuring the quality of service of data distribution.We apply BOP to optimize two existing matching algorithms, namely TAMA and REIN.The experimental results show that BOP shortens the matching time of TAMA and REIN by more than 60%.In addition, the performance of optimized versions is more stable than the original matching algorithms.

Accurately forecasting workloads in terms of throughput that is quantified as queries per second (QPS) is essential for microservices to elastically adjust their resource allocations. However, long-term QPS prediction is challenging in two aspects: 1) generality across various services with different temporal patterns, 2) characterization of intricate QPS sequences which are entangled by multiple components. In this paper, we propose a knowledge auto-embedding Informer network (KAE-Informer) for forecasting the long-term QPS sequences of microservices. By analyzing a large number of microservice traces, we discover that there are two main decomposable and predictable components in QPS sequences, namely global trend & dominant periodicity (TP) and low-frequency residual patterns with long-range dependencies. These two components are important for accurately forecasting long-term QPS. First, KAE-Informer embeds the knowledge of TP components through mathematical modeling. Second, KAE-Informer designs a convolution ProbSparse self-attention mechanism and a multi-layer event discrimination scheme to extract and embed the knowledge of local context awareness and event regression effect implied in residual components, respectively. We conduct experiments based on three real datasets including a QPS dataset collected from 40 microservices. The experiment results show that KAE-Informer achieves a reduction of MAPE, MAE and RMSE by about 16.6%, 17.6% and 23.1% respectively, compared to the state-of-the-art models.

Summary When large‐scale content‐based publish/subscribe systems face dynamic workloads, it is challenging to stabilize event delivery latency. In this article, we propose a latency‐aware parallelism framework (Lap) to address this challenge. Specifically, we propose a lightweight parallelism method called PhSIH for event matching algorithms. In addition, we design a reactive parallelism degree adjustment (RPDA) mechanism in the backpressure way to determine the parallelism degree. We implement Lap in Apache Kafka and evaluate the parallelism effect of PhSIH and the adaptability of RPDA on trace data. The experiment results demonstrate that PhSIH achieves linear speedup on three existing algorithms and RPDA possesses a desirable adaptability to the dynamic workloads.

The efficiency of human-computer interaction is greatly hindered by the small size of the touch screens on mobile devices, such as smart phones and watches. This has prompted widespread interest in handwriting recognition systems, which can be divided into active and passive systems. Active systems require additional hardware devices to perceive movements of handwriting or the tracking accuracy is not adequate for handwriting recognition. Passive methods use the acoustic signal of pen rubbing and are susceptible to environmental noise (above 60$dB$). This paper presents a novel handwriting recognition system based on vibration signals detected by the built-in accelerometer of smartphones. The proposed scheme is implemented in three stages: signal segmentation, signal recognition, and word suggestion.VibWriteris highly resistant to interferences since the normal environmental noise (below 70$dB$) will not cause the vibration of the accelerometer. Extensive experiments demonstrated the efficacy of the system in terms of accuracy in letter recognition (75.3%), word recognition (86.4%) and number recognition (79%) in a variety of writing positions under a variety of environmental conditions.

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.

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.

The newly emerging “fail-slow” failures plague both software and hardware where the victim components are still functioning yet with degraded performance. To address this problem, this article presents Perseus , a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly pinpoint and analyze fail-slow failures at the granularity of drives. Within a 10-month close monitoring on 248K drives, Perseus managed to find 304 fail-slow cases. Isolating them can reduce the (node-level) 99.99th tail latency by 48%. We assemble a large-scale fail-slow dataset (including 41K normal drives and 315 verified fail-slow drives) from our production traces, based on which we provide root cause analysis on fail-slow drives covering a variety of ill-implemented scheduling, hardware defects, and environmental factors. We have released the dataset to the public for fail-slow study.

Object-based storage systems have been widely used for various scenarios such as file storage, block storage, blob (e.g., large videos) storage, and so on, where the data is placed among a large number of object storage devices (OSDs). Data placement is critical for the scalability of decentralized object-based storage systems. The state-of-the-art CRUSH placement method is a decentralized algorithm that deterministically places object replicas onto storage devices without relying on a central directory. While enjoying the benefits of decentralization such as high scalability, robustness, and performance, CRUSH-based storage systems suffer from uncontrolled data migration when expanding the capacity of the storage clusters (i.e., adding new OSDs), which is determined by the nature of CRUSH and will cause significant performance degradation when the expansion is nontrivial. This article presents MapX , a novel extension to CRUSH that uses an extra time-dimension mapping (from object creation times to cluster expansion times) for controlling data migration after cluster expansions. Each expansion is viewed as a new layer of the CRUSH map represented by a virtual node beneath the CRUSH root. MapX controls the mapping from objects onto layers by manipulating the timestamps of the intermediate placement groups (PGs). MapX is applicable to a large variety of object-based storage scenarios where object timestamps can be maintained as higher-level metadata. We have applied MapX to the state-of-the-art Ceph-RBD (RADOS Block Device) to implement a migration-controllable, decentralized object-based block store (called Oasis ). Oasis extends the RBD metadata structure to maintain and retrieve approximate object creation times (for migration control) at the granularity of expansion layers. Experimental results show that the MapX -based Oasis block store outperforms the CRUSH-based Ceph-RBD (which is busy in migrating objects after expansions) by 3.17× ∼ 4.31× in tail latency, and 76.3% (respectively, 83.8%) in IOPS for reads (respectively, writes).

Erasure coding (EC) has been widely used in cloud storage systems to provide both high reliability and low storage cost. Previous literatures show that the cross-rack update operations are prevalent for many applications in erasure-coded cloud storage systems, which introduces significant I/O amplification, load imbalance and high latency. Several existing methods have been proposed to mitigate these problems. However, they ignore the correlations among chunks when performing data placement. Thus numerous stripes and racks participate in the update leading to extra I/Os and cross-rack traffic. Moreover, they don’t take into account the parallelism of network transmission which loses the potential update performance gains.To address the issues, we propose a novel Graph-based cross-Rack Parallel Update (GRPU) scheme to improve the update performance for erasure-coded cloud storage systems. The key idea of GRPU is to place the correlated chunks in the same stripe and rack, and transmit the chunks in parallel based on the network distance. The data placement and transmission paths selection are guided by two kinds of graphs. To demonstrate the effectiveness of GRPU, we conduct several experiments in a local cluster. The results show that, compared to the state-of-the-art methods, GRPU reduces the cross-rack traffic by up to 34.66% and the average response time by up to 61.69%, respectively.

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.

Deep learning (DL) jobs normally run on GPU clusters. Some DL jobs need to be scheduled preemptively to avoid long waiting times. However, preempting a DL job is time-consuming, which consists of suspending and resuming. Suspending needs to complete the training process of the current epoch, and resuming needs to reload the model and the training data. The existing schedulers almost do not consider the overhead of preempting jobs; thus, they may preempt jobs with large time loss, increasing the waiting time and the makespan.In this paper, we present PickyMan, a preemptive scheduler to minimize the overhead of preempting jobs to reduce the average waiting time and the makespan. PickyMan has some innovations. (1) Predict execution time using network traffic and database. It predicts the execution time of a DL job by profiling the network traffic from storage nodes to computation nodes and using a database, without the requirement of allocating extra resources from the cluster. It can use profiled information of only four jobs to predict the execution times for other same-model jobs, and most of the predicted errors are less than 10%. (2) Modeling the overhead of preemption. It builds a model to predict the time loss of job suspensions and resumptions with an average error of less than 5%. (3)We abstract the problem of choosing the appropriate jobs for preemption as one of finding an ordered division of the set of running jobs and solve it quickly with a greedy algorithm. By conducting experiments on the small-scale actual cluster and making large-scale simulations, PickyMan reduces the average waiting time by 10%–92% and further reduces the makespan by up to 14%, compared to existing methods.

Multi-frame super-resolution methods can generate high resolution images by combining multiple captures of the same scene; however, the performance of merged results are susceptible to degradation due to a lack of precision in image registration. In this study, we sought to develop a robust multi-frame super resolution method (called OISSR) for use on smartphone cameras with a optical image stabilizer (OIS). Acoustic injection is used to alter the readings from the built-in MEMS gyroscope to control the lens motion in the OIS module (note that the image sensor is fixed). We employ a priori knowledge of the induced lens motion to facilitate optical flow estimation with sub-pixel accuracy, and the output high-precision pixel alignment vectors are utilized to merge the multiple frames to reconstruct the final super resolution image. Extensive experiments on a OISSR prototype implemented on a Xiaomi 10Ultra demonstrate the high performance and effectiveness of the proposed system in obtaining the quadruple enhanced resolution imaging.

Optical image stabilizers (OIS) are widely used in digital cameras to counteract motion blur caused by camera shakes in capturing videos and photos. In this paper, we sought to expand the applicability of the lens-shift OIS technology for metric depth estimation, i.e., let a RGB camera to achieve the similar function of a time-of-flight (ToF) camera. Instead of having to move the entire camera for depth estimation, we propose DoCam, which controls the lens motion in the OIS module to achieve 3D reconstruction. After controlling the lens motion by altering the MEMS gyroscopes readings through acoustic injection, we improve the traditional bundle adjustment algorithm by establishing additional constraints from the linearity of the lens control model for high-precision camera pose estimation. Then, we elaborate a dense depth reconstruction algorithm to compute depth maps at real-world scale from multiple captures with micro lens motion (i.e., ≤ 3 mm). Extensive experiments demonstrate that our proposed DoCam can enable a 2D color camera to estimate high-accuracy depth information of the captured scene by means of controlling lens motion in the OIS. DoCam is suitable for a variety of applications that require depth information of the scenes, especially when only a single color camera is available and located at a fixed position.

This study reveals that on-board hardware modules leak electromagnetic (EM) emissions whenever audio or camera data is accessed, and proposes Magdefender scheme that explores the possibility of using the magnetometer built into mobile devices to detect eavesdropping instances by malicious apps and even the unscrupulous phone vendors. However, the target EM signals generated by accessing multimedia data is weak and tends to be buried beneath other noisy EM signals from apps running in the foreground. It is also subject to the external interference from geomagnetic signals generated by the device movement. To cope with the challenges, we adopt a generative adversarial networks (GAN) based model to facilitate the extraction of target EM signals indicating the occurrence of eavesdropping from the overall magnetometer readings. We also develop a neural network-based classifier with triplet loss embedding to identify the EM signals from the camera and/or microphones. Empirical results demonstrate the efficacy of MagDefenderin recognizing instances of eavesdropping on cameras/microphones data, with average accuracy of 97.3% when applied to the trained devices, and average 91.5% on unseen mobile devices.

Content-based publish/subscribe systems are an effective paradigm for implementing on-demand event distribution.Each event needs to be matched against subscriptions to identify the target subscribers.To improve the matching performance, many novel data structures have been proposed.However, the predicates included in subscriptions are handled the same way in most existing data structures, which is not efficient given the matching probability of predicates.In this paper, we propose a parallel ensemble matching framework called PEM, which uses multiple algorithms with complementary behavior on predicate matching probabilities.To achieve the performance balance of parallel matching, we design an elastic subscription classification method.We implement a prototype of PEM based on two existing algorithms.The experiment results show that PEM improves the matching performance by 43%.

The content-based publish/subscribe system is an effective paradigm for implementing on-demand event distribution. Each event needs to be matched against subscriptions to identify the target subscribers. To improve the matching performance, many novel data structures have been proposed. However, the predicates contained in subscriptions are handled the same way in most existing data structures, without considering their differences in matching probability. In this paper, we propose the concept of parallel ensemble matching (PEM) based on subscription partitioning. The basic idea is that we have the right algorithm handling the right subscriptions at the right time. First of all, we design a PEM framework by classifying subscriptions according to their matching probabilities and use the proper algorithms to process each subscription category. Furthermore, to deal with high-dimensional subscriptions, we propose a fine-grained PEM (fgPEM) that exploits matching algorithms with complementary behaviors by partitioning subscriptions into sub-subscriptions. We implement the prototype of PEM and fgPEM based on two existing algorithms. The experiment results show that PEM improves the matching performance by 43%. On the basis of PEM, fgPEM further improves the performance by 31%.

Spectral clustering (SC) is an unsupervised learning method that has been widely used in industrial product analysis. Compressive spectral clustering (CSC) effectively accelerates clustering by leveraging graph filter and random sampling techniques. However, CSC suffers from two major problems. First, the direct use of the dichotomy and eigencount techniques for estimating Laplacian matrix's$k$kth eigenvalue is expensive. Second, the computation of interpolation is time-consuming because it requires to repeat matrix-vector product for every cluster in each iteration. To address these problems, we propose a new method calledfast compressive spectral clustering(FCSC). Our method addresses the first problem by assuming that the eigenvalues approximately satisfy local uniform distribution, and addresses the second problem by recalculating the pairwise similarity between nodes with low-dimensional representation to reconstruct denoised laplacian matrix. The time complexity of reconstruction is linear with the number of non-zeros in Laplacian matrix. As experimentally demonstrated on both artificial and real-world datasets, our method significantly reduces the computation time while preserving high clustering accuracy comparable to previous designs, demonstrating the effectiveness of FCSC.

Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based on 2D U-Net, we have proposed an Embedded Clustering Sliced U-Net (ECSU-Net). ECSU-Net comprises of three modules named segmentation, intervertebral disc extraction (IDE) and fusion. The segmentation module follows an instance embedding clustering approach, where our three sliced sub-nets use axis of CT images to generate a coarse 2D segmentation along with embedding space with the same size of the input slices. Our IDE module is designed to classify vertebra and find the inter-space between two slices of segmented spine. Our fusion module takes the coarse segmentation (2D) and outputs the refined 3D results of vertebra. A novel adaptive discriminative loss (ADL) function is introduced to train the embedding space for clustering. In the fusion strategy, three modules are integrated via a learnable weight control component, which adaptively sets their contribution. We have evaluated classical and deep learning methods on Spineweb dataset-2. ECSU-Net has provided comparable performance to previous neural network based algorithms achieving the best segmentation dice score of 95.60% and classification accuracy of 96.20%, while taking less time and computation resources.

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.

Nowadays, Solid State Drive (SSD) arrays have been widely used in commercial big data centers and high-performance storage services. Meanwhile, in the era of explosive data growth, data centers need to implement the array scaling schemes to meet the increasing storage capacity requirements. The existing state-of-the-art scaling methods, such as Round-Robin (RR) and FastScale, aim at ensuring a uniform data redistribution. However, most of them are designed for Hard Disk Drive (HDD) arrays, ignoring lifetime difference among extended and former-used disks, which leads to several additional penalties in SSD arrays. Furthermore, due to the sudden interdisk lifetime disparity, the extended SSD disks trigger frequently wear-leveling operations for controlling the wearing balance into the predefined threshold. These reactions result in inefficient scaling and I/O performance degradation. To address the above problem, we propose a Lazy W ear-L eveling (Lazy-WL) mechanism to reduce the conventional wear-leveling overhead during the scaling process. Its core idea is to reduce the unnecessary intensive wear-leveling migration significantly, via narrowing the difference of program/erase (P/E) cycles among new-added and former deployed disks smoothly and gradually. To demonstrate the effectiveness of this approach, we conduct several simulation via Disksim and real implementation via a Hadoop cluster. The evaluation results show that, compared to the typical inter and intra disk wear leveling methods, Lazy-WL could lower the triggered wear-leveling operations by up to 92.9% and achieve a maximal 85.2% response time reduction, which suggests that Lazy-WL performs a balanced I/O distribution, and maintains high performance of SSD array with high scaling efficiency.

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.

In cloud storage systems, disk arrays are widely used because of their high reliability and low monetary cost. Due to the burst of I/O in sprinting computing scenarios (i.e. online retailer services on Black Friday or Cyber Monday), large scale cloud storage systems such as AWS S3 and GFS need to afford 10XI/O workloads. Therefore, rack level scaling for cloud disk arrays becomes urgent for sprinting services. Although several existing methods, such as Round-Robin(RR) and Scale-RS, are proposed to accelerate the scaling processes, the efficiencies of these approaches are limited. It is because that the cross-rack data migrations are ill-considered in their designs. To address the above problem, in this paper, we propose Rack-Scaling, a novel data redistribution method to accelerate rack level scaling process in cloud storage systems. The basic idea of Rack-Scaling is migrating appropriate data blocks within and among racks to achieve a uniform data distribution while minimizing the cross-rack migration, which costs more than intra-rack migration. We conduct simulations via Disksim and we also implement Rack-Scaling on Hadoop to demonstrate the effectiveness of Rack-Scaling. The results show that, compared to typical methods such as Round-Robin (RR), Semi-RR, Scale-RS and BDR, Rack-Scaling reduces the number of I/O operations and the data amount of cross-rack transmission by up to 90.4% and 99.9%, respectively, and speeds up the scaling by up to 8.77X.

Text input systems based on device-free finger tracking technologies have attracted considerable attention in the use scenarios of mobile and the Internet-of-Things (IoT) devices. Issues pertaining to 2D tracking have prompted interest in using 1D finger trajectories for the recognition of handwritten letters. Nonetheless, 1D tracking imposes two major challenges: (i) Trajectory information loss from 2D to 1D; and (ii) Inter-user diversity in writing traits. These challenges could possibly be overcome by collecting a large training dataset for every user; however, this would impose an unacceptable burden on users. This paper presents a text input system with multi-language support without training using acoustic-based 1D finger tracking technology. We developed a novel data augmentation scheme, in which the handwritten image dataset MNISTs are used to create artificial datasets (called TrackMNISTs). We compensate for the trajectory information loss of 1D by creating personal dataset (from TrackMNIST) to match the writing habits of individual users. The proposed data augmentation mechanism is also applicable to multilingual letter recognition. In experiments, MagicInput achieved outstanding classification accuracy on unseen users: 10 digits (98.3%), 26 uppercase/lowercase English letters (97.8%/95.3%), 49 Japanese characters (91.4%), and the 30 commonly used Chinese characters (93.8%).

Erasure codes (EC) have become a typical technology for distributed storage systems in place of data replication, providing similar data availability but lower storage cost. However, a great number of data computations and migrations during the EC recovery process bring high I/O and network latency penalties. Although several EC recovery methods have been designed to compromise the recovery penalty with high parallelism, the performance of these schemes was usually bounded by the straggler problems due to the various (I/O) performance among different nodes in the storage system. Moreover, the variation of the access popularity from the upper layer application causes the dynamic load fluctuation and asymmetry upon different nodes, which makes the scheduling more difficult during the recovery. To address the above problem, we propose a dynamic load-balanced scheduling algorithm for straggler recovery called EC-Scheduler. EC-Scheduler adjusts the recovery schedule dynamically with the awareness of continuous load fluctuation on the nodes, guaranteeing high parallelism and load balance ability simultaneously. To demonstrate the effectiveness of EC-Scheduler, we conduct several experiments in a cluster. The results show that, compared to typical recovery schemes such as Fast-PR and EC-Store, EC-Scheduler could achieve a 1.3X speed-up in the recovery process and 10X improvement in recovery load imbalance factor.

The efficiency of human-computer interaction is greatly hindered by the small size of the touchscreens on mobile devices, such as smart phones and watches. This has prompted widespread interest in handwriting recognition systems, which can be divided into active and passive systems. Active systems require additional hardware devices to perceive movements of handwriting or the tracking accuracy is not adequate for hand-writing recognition. Passive methods use the acoustic signal of pen rubbing and are susceptible to environmental noise (above 60dB). This paper presents a novel handwriting recognition system based on vibration signals detected by the built-in accelerometer of smart phones. VibWriter is highly resistant to interference since the normal environmental noise will not cause the vibration of the accelerometer. Extensive experiments demonstrated the efficacy of the system in terms of accuracy in letter recognition (76.15%) and word recognition (88.14%) when dealing with words of various lengths written by various users in a variety of writing positions under a variety of environmental conditions.

Various characteristics of mobile applications (apps) and associated in-app services have been used reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to rigorously restrict access to data related to mobile app usage. This paper outlines a novel approach to the extraction of detailed app usage information based on analysis of the electromagnetic (EM) signals emitted from mobile devices when executing app-related tasks. Note that this type of EM leakage becomes high-complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. This paper proposes a deep learning-based multi-label classification system to identify apps and in-app services based on magnetometer readings. The proposed MAGTHIEF system uses accelerometer and gyroscope data to cancel out the offset in geomagnetic signals followed by an elaborate deep region convolution neural network (DRCNN) to differentiate among multiple apps and the corresponding inapp services. Experiments on 50 apps demonstrated the efficacy of MAGTHIEF in identifying multiple apps and in-app services, achieving high average macro F1 scores of 0.87 and 0.95, respectively. MAGTHIEF also achieved time duration accuracy of 89.5% in recognizing app trajectory in the real-world scene.

With the development of multiaccess edge computing (MEC) technology at the network edge, efficient resources allocation and offloading between the resource-constrained edge clouds to maintain load balancing become a looming problem recently. However, most existing researches aimed at optimizing the allocation of computing resources are based on simulation and apply only for some typical Internet-of-Things (IoT) applications on mobile devices (i.e., they all lack of practicality and generality). In this article, we present a novel edge computing platform (CampEdge) with 36 edge nodes based on the wireless access point (AP) for adaptive resources allocation and computation offloading in a complicated dynamic campus environment. We first collected and sufficiently analyzed a large real-world WiFi data set, covering more than 8500 wireless APs and serves 44 000 active end users within an area of 3.1 km2over three months. A multiclass classification algorithm based on the random forest was then used with this data to accurately predict the state of resources usage at each edge node (i.e., in a busy state or normal state). To reasonably offload and transmit end-users' computational tasks to these edge nodes and optimize the total latency cost among the whole offloading process, we then illustrate a distributed computation offloading optimization strategy to formulate this complicated problem as a multiobjective latency optimization problem based on alternating direction method of multipliers (ADMM). Furthermore, we briefly discuss the convergence of our system. In experiments, CampEdge was shown to decrease user latency by up to 30%, compared to state-of-the-art methods. The proposed strategy was also shown to adaptively conform to a variety of compute-intensive and time-sensitive IoT applications for end users.

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.

The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health‐related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud‐based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single‐point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain‐based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K‐anonymity and searchable encryption techniques and propose a blockchain‐based privacy‐preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute‐based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K‐anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.

Multi-access edge computing (MEC) plays an important role in taking the cloud computing resources closer to the end-user at the network edge. The main challenge in such systems is to build models to efficiently predict and offload computational resources to maintain load balancing among edge clouds. In this paper, we present an Efficient Resources Allocation and Computation Offloading (ERACO) model with 36 edge nodes based on the large-scale wireless access point (AP) in a campus environment. We first collected and sufficiently analyzed this large WiFi dataset, covering more than 8,500 wireless APs and serves 44,000 active end-users within an area of 3.1 km2over three months. A multi-class classification algorithm was then used with this data to predict the usage of computing resources at each edge node. To reasonably offload these resources and decrease the latency between each edge node, we then propose the optimization strategy to formulate the complicated offloading problem as a multi-objective latency optimization problem using the alternating direction method of multipliers (ADMM). Experimental results show that the models decrease user latency by up to 30%, compared to state-of-the-art methods for a variety of applications.

With the explosive growth of data in cloud storage systems, Erasure Codes (ECs) have become a typical data redundancy technology because of its low storage cost and high reliability. However, due to a large amount of complex computations and transmissions among massive data and parities, the recovery of lost data in erasure coded storage systems incurs high I/O latency. Although several fast recovery approaches devote to mitigating the recovery time from the application level or device level, the performance of file level recovery is still restricted. It is because a part of the complicated relationships among data, parity and files are ignored in the design of recovery process. To address the above problems, we propose a novel File-aware Graph Recovery (FAGR) scheme, to improve the file level recovery performance during the reconstruction process. The key idea of FAGR is establishing a graph with the mappings among files, blocks, stripes, parities, nodes and the access frequencies of files, and guides the recovery process from file point of view. A corresponding model is established to analyze the cost efficiency of recovery process, which guarantees that FAGR reconstructs the popular files in advance to accelerate the recovery. To demonstrate the effectiveness of FAGR, we conduct several numerical analysis and experiments in clusters. The results show that, compared to typical fast recovery methods, FAGR reduces the average response time of files by up to 81.63 % and improves the throughput by up to 4.44 ×.

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.

Although many matching algorithms have been proposed to improve the matching efficiency of the content-based publish/subscribe system, existing work seldom consider the real-time of event dissemination from the perspective of event matching. On the basis of two existing matching algorithms, in this paper, we propose a subscription-classifying and structure-layering (SCSL) optimization method for matching algorithms, aiming to improve real-time by shortening the determining time of matching subscriptions. The basic idea of SCSL is that subscriptions with high matching probabilities should be processed first in the process of event matching and their storage positions in the data structure should be adjusted in line with changing probabilities. One challenge of SCSL is the trade-off that needs to be made between the gains of improving real-time performance by identifying matching subscriptions earlier and the cost of increasing matching time due to subscription classification and adjustment. We design a concise scheme to classify subscriptions, establish a lightweight adjustment mechanism to deal with dynamics and propose an efficient greedy algorithm to compute the adjustment solution, which alleviates the impact of SCSL on matching performance. The experiment results show that the 95thpercentile of the determining time of matching subscriptions is improved by about 70%. Furthermore, we integrate SCSL into Apache Kafka to augment it as a content-based publish/subscribe system and test the effect of SCSL based on real-world stock trace data, which witnesses about 40% improvement on the average event transfer latency and confirms that SCSL can effectively improve the real-time performance of content-based publish/subscribe systems.

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.

As massive data in modern cloud storage systems grow dramatically, it is a common method to partition and store data in multiple Availability Zones (AZs). Multiple AZs not only provide high reliability, but also reduce the network latency. Erasure Codes (ECs) are widely used in multiple AZs to provide high reliability at low storage cost. However, the recovery cost of EC is extremely high in multiple AZs' environment, which is mainly because a normal EC needs to reconstruct the lost data via transferring the data/parities across AZs. Although existing fast recovery approaches can save the I/O cost or network bandwidth in an effective manner, they are not suitable for multiple AZs. The reasons include low flexibility on various complex network scenarios, less consideration on crossing-AZ bandwidth, low capabilities on multiple disk/node failures, etc. To address the above problem, in this paper, we propose a crossing $\underline{\mathrm{A}}$vailability Zone Recovery (AZ-Recovery) method to efficiently improve the recovery performance for multiple AZs. AZ-Recovery investigates the complex homogeneous/heterogeneous network topologies, and finds an optimal data transmission path. Using this method, AZ-Recovery can significantly reduce the recovery cost and save the crossing AZ bandwidth in various failure scenarios. To demonstrate the effectiveness of AZ-Recovery, we evaluate various erasure codes via mathematical analysis and simulations in Network Simulator-3. The results show that, compared to the traditional erasure coding methods, AZ-Recovery saves the recovery bandwidth by up to 77.47%.

Efficient graph partitioning is vital for high-performance graph-parallel systems. Traditional graph partitioning methods attempt to both minimize communication cost and guarantee load balancing in computation. However, the skewed degree distribution of natural graphs makes it difficult to simultaneously achieve the two objectives. This article proposes topology refactorization (TR), a topology-aware method allowing graph-parallel systems to separately handle the two objectives: refactorization is mainly focused on reducing communication cost, and partitioning is mainly targeted for balancing the load. TR transforms a skewed graph into a more communication-efficient topology through fusion and fission, where the fusion operation organizes a set of neighboring low-degree vertices into a super-vertex, and the fission operation splits a high-degree vertex into a set of sibling sub-vertices. Based on TR, we design an efficient graph-parallel system (TopoX) which pipelines refactorization with partitioning to both reduce communication cost and balance computation load. Prototype evaluation shows that TopoX outperforms PowerLyra by up to 78.5% (from 37.2%) on real-world graphs and is faster than most other graph-parallel systems, while only introducing small overhead and memory consumption.

Block storage provides virtual disks that can be mounted by virtual machines (VMs). Although erasure coding (EC) has been widely used in many cloud storage systems for its high efficiency and durability, current EC schemes cannot provide high-performance block storage for the cloud. This is because they introduce significant overhead to small write operations (which perform partial write to an entire EC group), whereas cloud-oblivious applications running on VMs are often small-write-intensive. We identify the root cause for the poor performance of partial writes in state-of-the-art EC schemes: for each partial write, they have to perform a time-consuming write-after-read operation that reads the current value of the data and then computes and writes the parity delta, which will be used to “patch” the parity in journal replay. In this article, we present a speculative partial write scheme (called P ARI X) that supports fast small writes in erasure-coded storage systems. We transform the original formula of parity calculation to use the data deltas (between the current/original data values), instead of the parity deltas, to calculate the parities in journal replay. For each partial write, this allows P ARI X to speculatively log only the new value of the data without reading its original value. For a series of n partial writes to the same data, P ARI X performs pure write (instead of write-after-read) for the last n -1 ones while only introducing a small penalty of an extra network round-trip time to the first one. Based on P ARI X, we design and implement P ARI X Block Storage (PBS), an efficient block storage system that provides high-performance virtual disk service for VMs running cloud-oblivious applications. PBS not only supports fast partial writes but also realizes efficient full writes, background journal replay, and fast failure recovery with strong consistency guarantees. Both microbenchmarks and trace-driven evaluation show that PBS provides efficient block storage and outperforms state-of-the-art EC-based systems by orders of magnitude.

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.

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.

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.

Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we addressed this issue by developing a novel QR code (called mQRCode), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQRCode was > 98.6% within 10.2 frames via a multi-frame decryption method. The decryption rate for cameras positioned 20° off axis or > 10cm away from the designated location dropped to 0%, indicating that mQRCode is robust against attacks.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we address this issue by developing a novel QR code (called mQR code), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moiré pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQR codes is $> 98%$. The decryption rate for cameras positioned $20\degree$ off axis or $> 10cm$ from the designated location drops to $0%$, indicating that any attackers will be unable to steal a usable image.

NarrowBand-IoT (NB-IoT) is a radio access technology standardized by 3GPP to support a large set of use cases associated with the rapid deployment of massive machine-type communications. NB-IoT facilitates the connection of devices in inaccessible areas, extends battery life, and reduces device complexity. Unfortunately, the opacity of the underlying schema (i.e., the way that these benefits are achieved) makes it very difficult for most users and developers to manage deployment scenarios. In this study, we built an embedded system comprising a Raspberry Pi with an NB module, referred to as NBPilot, which interacts with NB networks to identify essential signalling messages transmitted by a Qualcomm NB modem. This system gives researchers and developers an unprecedented understanding of network behaviour as well as the ability to adjust them to their particular requirements. We employed the-state-of-art machine learning techniques for modeling and the analysis of NB performance. The efficacy of the proposed NBPilot system was established by applying it to a metropolitan NB-IoT network with over 2,000 NB sites for the collection and testing of data trace as well as the validation of a cellular station prior to going online.

Full WiFi coverage becomes more and more prevalent in corporate places, such as university, big mall, airport, and so forth. To achieve full WiFi coverage in a wide area is costly due to the large-scale AP deployment spending and considerable operating expenditure. However, with limited literature available, how to deploy and manage those APs in an efficient and economical way, is still unknown for system providers. To bridge this gap, in this article, we first collect large-scale AP usage data in our campus WiFi system, which contains over 8000 APs and serves more than 40 000 active end-users in the area of 3.0925 km2. After mining large-scale spatio-temporal user associations, we obtain several key insights as follows. First, Idle Phenomenon prevails throughout the trace, in which large portion of APs are wasted without any user association. Second, AP usages in different buildings have very distinct characteristics in terms of user association and traffic consumption. Third, diurnal usage patterns are very obvious not only at singe AP level but also at the building and the whole system level. Many deployment and management strategies can benefit from these insights, e.g., heterogeneous AP deployment and intelligent AP management. Among them, we then propose an intelligent large-scale AP management scheme, called LAM, to dynamically control large-scale APs (ON or OFF) for energy saving and meanwhile without loss of WiFi coverage. In LAM, based on history association records, the user load of each AP is predicted by machine learning algorithms, and those APs whose idle durations are longer than the length of the predefined time window, will be switched off during the duration. We conduct extensive trace-driven experiments to demonstrate its efficacy; on average, more than 70% of power consumption can be markedly saved with over 92% of WiFi coverage guaranteed, which is able to save empirical $59 000 per year just for our system.

Unikernel specializes a minimalistic LibOS and a target application into a standalone single-purpose virtual machine (VM) running on a hypervisor, which is referred to as (virtual) appliance . Compared to traditional VMs, Unikernel appliances have smaller memory footprint and lower overhead while guaranteeing the same level of isolation. On the downside, Unikernel strips off the process abstraction from its monolithic appliance and thus sacrifices flexibility, efficiency, and applicability. In this article, we examine whether there is a balance embracing the best of both Unikernel appliances (strong isolation) and processes (high flexibility/efficiency). We present KylinX, a dynamic library operating system for simplified and efficient cloud virtualization by providing the pVM (process-like VM) abstraction. A pVM takes the hypervisor as an OS and the Unikernel appliance as a process allowing both page-level and library-level dynamic mapping. At the page level, KylinX supports pVM fork plus a set of API for inter-pVM communication (IpC, which is compatible with conventional UNIX IPC). At the library level, KylinX supports shared libraries to be linked to a Unikernel appliance at runtime. KylinX enforces mapping restrictions against potential threats. We implement a prototype of KylinX by modifying MiniOS and Xen tools. Extensive experimental results show that KylinX achieves similar performance both in micro benchmarks (fork, IpC, library update, etc.) and in applications (Redis, web server, and DNS server) compared to conventional processes, while retaining the strong isolation benefit of VMs/Unikernels.

Full WiFi coverage is more and more prevalent in many places such as university, enterprise, big mall, etc. To achieve full WiFi coverage in a wide area is very costly. Not only extensive AP deployments are expensive, to operate and maintain such large-scale APs every day can also cost much, e.g., the huge power consumption. In this paper, we collect large-scale AP status data in our campus WiFi system, which contains over 8,000 APs and serves about 40,000 active end-users in the area of 3.0925 km2. After conducting empirical studies on AP loads, we find Idle Phenomenon prevails throughout the trace. A large portion of APs are running without any user association, which will inevitably lead to unnecessary energy consumption. Inspired by this, we propose an intelligent large-scale AP control scheme, named as ACE (i.e., AP Control with Energy saving), to dynamically control large-scale APs (On or Off for energy saving meanwhile without loss of WiFi coverage. In ACE, the load of each AP is predicted first by the random forest algorithm, and those APs whose idle durations last for more than the length of the pre-defined sliding window will be turned off. We conduct extensive trace-driven simulations to demonstrate the efficiency of the ACE scheme; specifically, more than 70% of power energy can be saved with over 92 % of user WiFi coverage guaranteed in average.

Classifying traffic flows into source applications is of great value for intelligent network management, which can help to detect malicious attacks, monitor the network, optimize network behaviors and then improve user experience, etc. However, to achieve high-accuracy traffic classification, especially in real time, is very challenging due to very complicated behaviors of traffic flows where network applications could often transmit traffics with encryption at randomized port numbers under highly dynamic network conditions. In this paper, by collecting extensive application traffic flows at the exit router of Shanghai Maritime University (the traffic rate can reach up to 7 GB/s at peak time), we identify that there is a very distinct characteristic in inner-connection of message (grouped by single or multiple consecutive TCP packets) sequence for different application flows. We then propose our traffic classification algorithm, which essentially adopts a Long Short-Term Memory (LSTM) neural network to output a classifier with message sequence vector (not necessarily covering all messages) of a traffic flow as the training input, to conduct online traffic flow classification. Extensive simulations are conduced considering varied training data size and diverse source applications, and an average about 97 % accuracy on per-flow classification can be achieved.

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.

Vehicular ad hoc network (VANET) has been widely recognized as a promising solution to enhance driving safety, by keeping vehicles well aware of the nearby environment through frequent beacon message exchanging. Due to the dynamic of transportation traffic, especially for those scenarios where the density of vehicles is high, the naive beaconing scheme where vehicles send beacon messages at a fixed rate with a fixed transmission power can cause severe channel congestion. In this paper, we investigate the risk of rear-end collision model and define a danger coefficient ρ to characterize the danger threat of each vehicle being in a rear-end collision. We then propose a fully-distributed beacon congestion control scheme, referred to as ABC, which guarantees each vehicle to actively adapt a minimal but sufficient beacon rate to avoid a rear-end collision based on individual estimates of ρ. In essence, ABC adopts a TDMA-based MAC protocol and solves a NP-hard optimal distributed beacon rate adapting (DBRA) problem with a greedy heuristic algorithm, in which a vehicle with a higher ρ will be assigned with a higher beacon rate while keeping the total required beacon demand lower than the channel capacity. We conduct extensive simulations to demonstrate the efficiency of ABC design in different traffic density and a large variety of underlying road topologies.

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.

Nowadays, an in-depth understanding of the Internet traffic profile is indispensable. Many network monitoring applications claim the connection-oriented analysis. However, many existing capture tools are too low-performance to cope with the large network traffic flow. Though some tools using traffic sampling can handle heavy network stream, there is information loss. While some parallel methods have been adopted to optimize the performance, there are insufficient extendible easy-use libraries. To feed this demand, this paper proposes a flexible parallel network library (Noff) which has the ability to process the network traffic at speed up to 6 Gbps. Noff supports two kinds of parallel processing procedures on the multi-core architecture to improve system compatibility and performance. Furthermore, Noff proposes a timing wheel scheme to process some network abnormity. For extendibility, Noff also offers many flexible callback interfaces. Some well-designed experimental evaluations indicate that Noff can perform about 25% better than other software libraries in terms of streams processing and can perform much better using a multi-core platform. Under the same circumstance, Noff can also process much higher concurrent traffic. Noff is currently deployed in a campus access point to process traffic at 500-800Mbps speed to offer stream information for a network security monitoring platform.

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.

Near-field communication (NFC) plays a crucial role in the operation of mobile devices to enhance applications such as payment, social networks, private communication, gaming, and etc. Despite of the convenience, existing NFC standards like ISO-13157 require additional hardware (e.g., loop antenna and dedicated chip) and thereby hindering their wide-scale applications. In this work, we seek to propose a novel near-field communication protocol, MagneComm, which utilizes Magnetic Induction (MI) signals emitted from CPUs and captured by magnetometers on mobile devices for communication. Since CPUs and magnetometers are readily available components in mobile devices, MagneComm eliminates the requirement for special hardware and complements existing near-field communication protocols by providing additional bandwidth. We systematically analyze the characteristics of magnetic signals of CPUs and facilitate MagneComm with one-way communication, full-duplex communication, and multi-transmitter schemes in accordance with the hardware availability on devices. We prototype MagneComm on both laptops and smartphones. Extensive evaluation results show that MagneComm achieves up to 110bps within 10cm.

With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, trade-off between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min–max aggregate sensing time. We rigorously prove that optimizing the min–max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2−1m⁠, where m is the number of member smartphones in the system. For the online allocation model, we propose two algorithms: greedy algorithm and Robin-Hood algorithm, which achieve the competitive ratio of at most m and m+1⁠, respectively. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the online greedy algorithm and Robin-Hood algorithms reduce the total sensing time 73% and 37.5%, respectively. The offline approximation algorithm and online greedy algorithm achieve much better min–max fairness compared to other algorithms.

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.

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.

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.

This paper discusses the time-constrained data delivery problem in vehicular ad hoc networks (VANETs). The unique characteristics of the network present great challenges to the issue. First, there are no always-connected forwarding routes between vehicles. Second, there is an intrinsic tradeoff between communication cost and delivery quality. Third, there is great uncertainty about vehicular mobilities. Exploiting vehicular trajectories, we present a constructive approach called TNC to tackle the challenges. In TNC, contact-based data forwarding and communication connections through the mobile network are incorporated. TNC first predicts the time-stamped inter-vehicle contacts and establishes expected contact graph, based on which it then computes the configuration for enhancing the connectivity of the network while introducing minimum number of mobile communication connections. Extensive simulations based on three real vehicular traces collected from 2,000 taxis and 1,400 buses in Shanghai, and 2,500 taxis in Shenzhen have been conducted and results demonstrate the efficacy of our approach.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POSTcan greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes.

In recent years, wireless networks (including WLANs and cellular networks) have been widely deployed in enterprise, public areas and homes. In wireless networks, users are observed to act collaboratively rather than arriving or leaving independently. In this paper, we have analysed real WiFitraces deployed in an enterprise in 2012 and cellular network traces from a urban areas in China in 2013. We find that users in wireless networks are likely to be social related in either time field or spatial field. We also observe that in WLANs, user behavior like leaving together may cause significant AP load unbalance problem, which may result in sub-optimal network throughput and unfair bandwidth allocation among users. Inspired by those observations, we come up with an online heuristic algorithm, which can actively learn users' sociality information and assign them to the optimal APs.

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

Acquiring instant walking distance is desirable in indoor localization and map construction. However, due to the blackout of Global Positioning System (GPS) in indoor settings, to accurately estimate the indoor walking distance with minimum hardware requirement is very challenging. In this paper, we propose a lightweight scheme, called DiSen, to range the instant walking distance of smartphone users. After analysing the extensive walking trace data, we find that people have rather consistent walking behaviour even though they may change their walking speeds in different situations. Furthermore, the relationship between stride length and step frequency while walking can be well estimated using non-linear sigmoid model. Inspired by such insights, we first design a stride segmenting method to obtain reliable and accurate step frequency information from raw accelerometer readings. We then train a sigmoid model using acceleration and GPS information collected when a user walks in outdoor conditions and finally apply the model to indoor walking distance ranging. Real-world experiment results show that, in different walking speeds, DiSen can reach average distance estimation accuracy of 96%.

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.

With the popularity of intelligent mobile devices, enormous urban information has been generated and required by the public. In response, ShanghaiGrid (SG) aims to providing abundant information services to the public. With fixed schedule and urban-wide coverage, an appealing service in SG is to provide free message delivery service to the public using buses, which allows mobile device users to send messages to locations of interest via buses. The main challenge in realizing this service is to provide efficient routing scheme with privacy preservation under highly dynamic urban traffic condition. In this paper, we present an innovative scheme BusCast to tackle this problem. In BusCast, buses can pick up and forward personal messages to their destination locations in a store-carry-forward fashion. For each message, BusCast conservatively associates a routing graph rather than a fixed routing path with the message in order to adapt the dynamic of urban traffic. Meanwhile, the privacy information about the user and the message destination is concealed from both intermediate relay buses and outside adversaries. Both rigorous privacy analysis and extensive trace-driven simulations demonstrate the efficacy of BusCast scheme.

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.

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicle traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts one greedy heuristic to select the most “centric” vehicles as seeds for mobile advertising. Extensive trace-driven simulation results show that POST can greatly improve the coverage and the intensity of advertising.

With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, tradeoff between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min-max aggregate sensing time. We rigorously prove that optimizing the min-max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2 - 1/m, where m is the number of member smartphones in the system. For the online allocation model, we propose a greedy online algorithm which achieves a competitive ratio of at most m. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the greedy online algorithm reduces more than 73% total sensing time, and both algorithms achieve over 3x better min-max fairness.

Networks are constantly generating an enormous amount of rich diverse information. Such information creates exciting opportunities for network analytics. However, a major challenge to enable effective network analytics is the presence of missing data, measurement errors, and anomalies. Despite significant work in network analytics, fundamental issues remain: (i) the existing works do not explicitly account for anomalies or measurement noise, and incur serious performance degradation under significant noise or anomalies, and (ii) they assume network matrices have low-rank structure, which may not hold in reality.

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.

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 degrades 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 data, based on which unnecessary high-power-state tails of radio are cut out leveraging the Fast Dormancy mechanism. Extensive trace-driven simulation results demonstrate the efficacy of SmartCut design. On average, SmartCut can save up to 56.57% energy on average while having little side-effect to user experience.

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.

Well-balanced workload among wireless access points (APs) in a wireless local-area network (WLAN) can improve the user experience for accessing the Internet. Most load balancing solutions in WLANs focuses on the optimization of AP operations, assuming that the arrivals and departures of users are independent. However, through the analysis of AP usage based on a real WLAN trace of one-month collected at the Shanghai Jiao Tong University (SJTU), we find that such an assumption does not hold. In fact, due to users' social activities which is particularly time for enterprise environments, they tend to arrive or leave in unison, which would disruptively affect the load balance among APs. In this paper, we propose a novel AP allocation scheme to tackle the load balancing problem in WLANs, taking into account the social relationships of users. In this scheme, users with intense social relationships are assigned to different APs so that jointly departure of those users would have minor impact on the load balance of APs. Given that the problem of allocating an AP for each user so that the average of the sums of social relation intensity between any pair of users in each AP is NP-complete, we propose an online greedy algorithm. Extensive trace-driven simulations demonstrate the efficacy of our scheme. Comparing to the state-of-the-art method, we can achieve about 64.7 percent balancing performance gain on average during peak hours in workdays.

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.

Reliable communications are essential for most applications in wireless sensor networks (WSNs). In traditional approaches, the per-hop and end-to-end (E2E) recovery schemes are widely used. These schemes, however, suffer from low E2E success rate and poor energy efficiency in large-scale real environments. Through empirical studies, in this paper we identify three major problems that hinder the efficient and reliable communications. To address these problems, we propose a novel in-middle recovery scheme and realize it by designing and implementing a proliferation routing. Proliferation routing integrates three core technologies, namely, capability-based path finder, a randomized dispersity, and reproduction. Proliferation routing offers great flexibilities for transmissions. It cannot only be applied with any Medium Access Control (MAC) protocols and routing metrics, but also obtains a desired service quality (i.e., transmission success rate, energy cost, etc.) by controlling the system parameters. To demonstrate the effectiveness of proliferation routing, we thoroughly analyze its performance. We also conduct performance evaluations through implementation experiments as well as simulations. In a specific experimental setup, proliferation routing can increase the E2E transmission success rate up to 80 percent compared with the well-known hop-based routing and flooding.

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Inter-contact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on inter-contact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of taxies, exhibits a light tail such as one of an exponential distribution, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the inter-contact time obeys a power law. By performing a least squares fit, we establish an exponential model that can accurately depict the tail behavior of the inter-contact time in VANETs. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers' route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace.

In this paper, we present Passive Geographical Routing protocol (PGR), a routing mechanism particularly designed for Vehicle Ad hoc Networks (VANET). PGR has no route discovery phase which is needed by DSR or AODV. In PGR, each node periodically disseminates its location and velocity information along four orthogonal directions of its velocity to distribute routing state in the network. Data packets are first sent in a random direction, and then biased toward the direction according to the received routing state in intermediate nodes. With the nodes’ location and velocity information and assistance of the digital city road map, PSR can prolong the valid duration of routing state stored in the intermediate node. We evaluate PGR with large realistic traffic data in Shanghai. The experiments show that PGR offers a high packet delivery ratio comparing with the traditional routing protocols. The routing overhead is controlled in the network scales as O(n) for N-node networks. This routing scheme has good scalability properties in high dynamic topology of VANET.

Due to critical resource restrictions, wireless sensor networks (WSNs) often face a trade-off between the cost of data transmission and the accuracy of event detection. By exploring the potential spatial and temporal correlations among sensory data, a WSN may intelligently select only a subset of nodes, whose data can still keep the major properties of those collected by the whole network, to transmit. Two important issues are examined in this study. First, which of those sensors should be selected? Second, how can the lifetime of the selected sensors be maximized? We propose a Singular Value Decomposition (SVD) based Sensory Data Suppression (SSS) Mechanism, which removes unnecessary data transmissions and prolong the lifetime of sensor networks. We also balance transmission duties among sensor nodes by leveraging the load balancing algorithms with both one-attribute and multi-attribute scenarios.

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

This paper focuses on the design and prototype of a version-enabled peer-to-peer content distribution system based on distributed hash table. The system is different from the CVS system; it manages the version at item level, not project level. The system provides the user version management functionality, which is absence in most of the current peer-to-peer content distribution network. In addition, the version management functionality can be used as a concurrent version system in team collaboration system. In such application, it provides more features such as serverless, robustness and reliability. The peer-to-peer nature of our system is more suitable to the human collaboration model than the client-server model. The system is now being prototyped using OpenDHT on Java2 platform

The variance of user interest is a significant factor for designing peer-to-peer content-based retrieval system which leads to the birth of this paper. This paper gives the preliminary scheme to solve the problem of Web search of Internet scale. All the personalized peers form an interest-oriented Web server union (IOWSU) by employing structured P2P routing mechanism. After a query request from a peer is posted to the union, an optimized and deterministic result based on the search peer's interest is returned from the union. The result of our simulation demonstrates the method proposed in this paper is practicable and efficient

We study two key problems arising from mobile peer-to-peer media streaming: the stability of interconnection between supplying peers and requesting peers in the mobile peer-to-peer streaming system; and fast capacity amplification of the entire mobile peer-to-peer streaming system. Many algorithms for the fast capacity amplification of the peer-to-peer streaming system have been proposed. However, these algorithms are inefficient in the mobile ad-hoc networks. Topological dynamics of mobile peer-to-peer are exacerbated by the changes of mobile user's location and interest. We observe that the mobile users exhibit correlated mobility patterns in practical ad-hoc networks. We use the stable group algorithm to characterize user mobility in mobile ad-hoc networks. Based on the stable group, we then propose a distributed stable-group differentiated admission control algorithm (SGDAC/sub p2p/), which leads to fast amplifying of the system's total streaming capacity using self-growing. Finally, extensive simulation results are presented to compare between the SGDAC/sub p2p/ and traditional methods to prove the superiority of the algorithm.