As a significant biometric identification technology, face recognition (FR) is extensively utilized in identity verification and security surveillance systems. Current research predominantly relies on high-definition RGB camera-based methods. However, these methods are susceptible to various factors such as lighting conditions and disguises. This paper proposes a low-cost face recognition solution called Warm- Face based on thermal array sensors. By leveraging the thermal radiation of the face, we overcome the disturbances caused by lighting conditions and disguises, thereby achieving rapid and highly accurate face recognition. However, face recognition based on thermal array sensors still faces two major challenges. Firstly, in complex scenarios, thermal noise interference can lead to the thermal radiation characteristics of the target face becoming indistinguishable from the background. Secondly, due to their large network parameter sizes and high computational complexity, recognition models face challenges in simultaneously achieving low latency and high accuracy. WarmFace extracts facial regions through a semantic segmentation-based approach, effectively reducing the impact of background interference on recognition performance. Additionally, in the recognition model, we utilize a series of linear transformations instead of convolution operations to process the intrinsic features of images, which reduces redundancy in feature maps while preserving the essential information. Extensive real-world experiments validate the effectiveness of WarmFace in various environments, achieving an average recognition accuracy of 98.6%.

Many reconnaissance missions require a group of mobile terminals (such as soldiers, mobile robots, and unmanned boats) to jointly operate within a region which is far away from the command centre. When a critical event occurs and is detected by a terminal, it is often required for the terminal to upload some critical data to the command centre (or via the satellite). As the bandwidth of the upload link is usually low due to the long distance, uploading the critical data often has long latency. To reduce the latency, a feasible way is to utilize the nearby terminals’ idle uplinks to help with the upload process, which requires the terminal’s data to be disseminated to other terminals as soon as possible. This is a new dissemination problem because the data being disseminated is also partially being uploaded, which seems as a noveldata-leakingdissemination problem. To solve it, we propose LHDS (Low-latency Heterogeneous Dissemination Scheduling) scheme by transforming the problem into two special sub-problems, i.e., constructing a special degree-decreasing tree with maximum multichild nodes, and designing a leaking-sustained dissemination schedule for each subtree. Extensive simulation experiments have been conducted on LHDS as well as two heuristic algorithms (i.e.,DBOandS-GA) designed for baselines. The results show that LHDS scheme significantly outperforms theDBOandS-GAalgorithms in terms of total collaborative data uploading latency, with saving 41% and 42% latency on average, respectively.

The evolution of sixth-generation (6G) wireless communication significantly accelerates the Internet of vehicles innovation, catalyzing advancements in autonomous driving systems. The collaborative model utilizing 6G is expected to break through the vehicle’s inherent field-of-view deficiencies and heterogeneous computational resource constraints, further improving the efficiency of the technology. This paper proposes a novel 6G NOMA air-ground integrated sensing-computing framework that achieves high-quality collaborative perception and low-latency 3D computing offloading to alleviate restrictions through cooperative networking with unmanned aerial vehicles (UAVs) and road-side units (RSUs). To balance latency and UAV energy consumption for efficient collaboration, we formulate it as a mixed integer nonlinear programming problem (MINLP). Considering the time sensitivity of the perceptual task, we introduce queuing and Lyapunov optimization theory to transform the optimization objective into a Lyapunov drift-penalty function and derive its upper bound, which we model as a Markov decision process (MDP) and optimize it with Large Language Models (LLMs) assisted temporal replay deep reinforcement learning (TR-DRL). For perception quality improvement, an elite-guided binary-weighted firefly algorithm is developed to solve combinatorial optimization in perception fusion. Experimental results demonstrate 4.88% and 8.26% improvements in latency and energy efficiency respectively, alongside enhanced perception fusion quality 7.41% compared with advanced counterparts.

Deep learning technologies have seen widespread adoption in WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, which has received little attention within the WiFi sensing community. To more comprehensively understand the potential threats posed by perturbation attacks, we present a novel attack method, named WiIntruder, distinguishing itself with universality, robustness, and stealthiness. This paper intends to provide a catalyst that promotes the assessment of security in existing WiFi-based sensing systems. We achieve the three aforementioned salient features in WiIntruder through the following three steps: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, thereby enabling a universal perturbation attack vector applicable to a wide range of applications; (2) Mitigating the impact of perturbation signal distortion by optimizing key factors of device synchronization and wireless propagation through a heuristic particle swarm algorithm; and (3) Enhancing the diversity and stealthiness of attack patterns by randomly switching among perturbation surrogates generated by a generative adversarial network. Experimental results confirm the threat posed by WiIntruder to four common WiFi-based services, with the average accuracy decrease by 72.9% under black-box attack scenarios.

Vessel trajectory prediction is fundamental to intelligent maritime systems. Within this domain, short-term prediction of rapid behavioral changes in complex maritime environments has established multimodal trajectory prediction (MTP) as a promising research area. However, existing vessel MTP methods suffer from limited scenario applicability and insufficient explainability. To address these challenges, we propose a unified MTP framework incorporating explainable navigation intentions, which we classify into sustained and transient categories. Our method constructs sustained intention trees from historical trajectories and models dynamic transient intentions using a Conditional Variational Autoencoder (CVAE), while using a non-local attention mechanism to maintain global scenario consistency. Experiments on real Automatic Identification System (AIS) datasets demonstrates our method’s broad applicability across diverse scenarios, achieving significant improvements in both ADE and FDE. Furthermore, our method improves explainability by explicitly revealing the navigational intentions underlying each predicted trajectory.

The rapid development of the low-altitude economy (LAE) has accelerated the deployment of cargo unmanned aerial vehicles (UAVs) for intelligent logistics and delivery services. However, large-scale UAV operations still face multiple practical challenges, including unstable communication connectivity, limited onboard battery energy, and strict customer time-window constraints. To address these issues, this paper investigates the trajectory and task scheduling optimization problem for multi-UAV cooperative cargo pick-up under joint communication, energy, and time-window constraints. We develop a collision-aware cooperative multi-UAV optimization algorithm (CACMO) that integrates a Dueling Deep Q-Network (D3QN) for communication-aware trajectory learning with a simulated annealing (SA) based global task-sequence planner and an explicit inter-UAV conflict-resolution mechanism. The D3QN module enables adaptive trajectory generation in unknown and time-varying radio environments without requiring an a priori radio map, maintaining stable connectivity while reducing flight cost, whereas the SA module determines efficient task orders and enforces safe coordination among multiple UAVs through collision-aware refinement. Simulation results demonstrate that the proposed CACMO algorithm framework achieves an optimal balance between task completion time (1,719 seconds) and user satisfaction (score of 0.9969) under typical operating conditions, delivering a 70–75% reduction in total weighted cost compared to representative baseline methods. Crucially, this substantial improvement is achieved while explicitly enforcing multi-UAV collision avoidance-a critical constraint absent in most baseline methods. The framework maintains zero communication outage and guarantees safe inter-UAV separation throughout the mission while satisfying all energy and time window constraints in realistic urban environments, confirming its robustness and scalability for cooperative multi-UAV logistics operations within the LAE.

Voice-enabledmobile applications(apps) are exploding in popularity as they could be manipulated with voice commands to achieve convenient man-machine interaction. These voice-enabled apps also raise security and privacy concerns about whether they would maliciously invoke microphones to realize voice eavesdropping. To explore this issue, in this work, we design baleful apps to access the microphone covertly, the results of test studies demonstrate that covert eavesdropping attacks can bypass existing device detection schemes as well as are unnoticeable to human users. To prevent the covert voice eavesdropping attack, we propose a versatilemicrophone icon detection(MicID) scheme inspired by the groundtruth that authorization of the voice function requires the user to touch the specific microphone icon in most of voice-based apps. Specifically, we devise a deep learning model,lightweight YOLO(L-YOLO), to locate the microphone icon on the screen quickly and accurately. By determining whether the located microphone icon is touched by the user, we can judge whether the current microphone access belongs to the app's normal operation or illegal eavesdropping. Finally, we conduct extensive experiments by deploying the scheme on real devices and collecting dataset. The evaluation results show that the proposed MicID scheme achieves more than 99% accuracy with low computation cost.

In vehicular edge computing (VEC) networks, by implementing content caching and V2X connectivity, road side unit (RSU) and nearby vehicles can serve as platforms for rapid data retrieval to address mobile traffic explosion. However, due to the dynamic and multi-constrained environment consisting of heterogeneous vehicles and RSU, it is challenging to meticulously plan cooperative caching policies. Additionally, due to the diversity of contents and the mobility of vehicles, the caching policy space is massive, which can be fatal for vehicles with limited computing and energy. In this paper, we formulate cooperative content caching in VEC networks as Markov decision process (MDP), configuring caching policies for vehicles and RSU. Our aim is to minimize Lyapunov drift and long-term delay. To address the massive caching policies, we propose a two-stage deep reinforcement learning (TS-DRL) algorithm. In the first stage, an improved ant colony algorithm is used to generate unilateral suggestions and construct action space to avoid the curse of dimensionality. In the second stage, we combine the Noisy Net and Double Deep Q-Learning Network to avoid overestimating value and efficient exploration problem. Simulation results show that TS-DRL outperforms advanced algorithms in terms of delay and cache hit rate.

The mobile health (mHealth) system, leveraging mobile edge computing, can monitor health status and provide diagnosis. However, due to the privacy of medical data and the resource limitations of mobile devices, patients are unable to access diagnostic services provided by untrusted servers in real-time. Existing schemes present significant challenges in private heterogeneous data aggregation, model training and inference in the presence of malicious participants, and expensive resource consumption. To address these issues, in this paper, we propose a non-interactive privacy-preserving system with the naive Bayesian model, i.e.,$\mathsf {RobustHealth}$, for heterogeneous mHealth diagnosis. Specifically, we extract homogeneous features from heterogeneous datasets to enable efficient encrypted aggregation. We propose a novel private model training algorithm with enhanced security to against collusion-then-differential attacks. We develop a novel non-interactive private model inference algorithm using minimal lightweight cryptographic primitives, designed for patients under unstable network environments. We provide formal security proofs for our system using the Universal Composable (UC) framework. To validate the performance of$\mathsf {RobustHealth}$, we conduct extensive experiments on real-world heterogeneous datasets, and compared with related works. The results demonstrate a$\bf {4.37\%}$improvement in model accuracy, along with significant reductions in computational and communication overheads of$\bf {21.18\times }$and$\bf {4.24\times }$, respectively.

Person re-identification (Re-ID) can recognize users based on their clothing, body shape, and other information without the need for clear facial images, and is widely applied in the field of intelligent security. Traditional Re-ID systems mainly rely on high-definition RGB cameras, but the deployment of large-scale high-definition RGB cameras indoors has caused serious privacy and ethical concerns. Recently, wireless-based Re-ID systems (Wi-Fi, RFID, millimeter-wave radar, etc.) have shown promising prospects, but the limited sensing resolution hinders their practical deployment. In this paper, we propose WarmGait, a Re-ID system based on thermal array sensors, which can achieve high-precision Re-ID at low cost and minimize the invasion of user privacy. However, using thermal arrays for Re-ID still faces two major challenges. The first is the low and unclear texture resolution of images caused by low-cost infrared devices. The second is that existing gait recognition methods require maintaining the sequential constraint of gait images, which reduces the flexibility of gait recognition or Re-ID. To address these two challenges, we first designed an edge module inspired by Taylor Finite Difference (TFD) to aggregate image edge information to help improve the resolution of infrared devices. Then, we considered gait as a collection of gait profiles and extracted features from the frame level and collection level for recognition, breaking through the limitations of the number and order of input images. After extensive experimental evaluation, our model can achieve an average recognition accuracy of 87.3% in various scenarios, demonstrating the potential of WarmGait in Re-ID.

Ear-worn devices (earables) are increasingly central to smart system interactions involving privacy-sensitive data, yet secure user authentication on these devices remains a challenge. Existing methods often depend on auxiliary sensors like in-ear microphones or accelerometers, which are absent in many commercial earables. This paper introduces a novel biometric approach leveraging natural head gestures. We observe that head gestures generate unique air friction patterns detectable by out-ear microphones, producing sonic signatures shaped by the head and neck's musculoskeletal dynamics. These signatures serve as a robust basis for earable authentication. We propose HMPrint, a system that captures air-friction-induced sonic effects (AFiSe) from head gestures via outear microphones for authentication. HMPrint incorporates advanced spectral analysis, synthetic data generation using variational autoencoders, and a contrastive continual learning framework to enhance robustness against inconsistent wearing postures, varied movement patterns, and environmental noise. A proof-of-concept prototype was tested with 30 participants and 15 commercial earable models across diverse conditions. Results show that HMPrint achieves high authentication accuracy (97.49% recall), a low FAR (2.34%), and strong resistance to spoofing (98% success rate).

With the rapid advancement of edge computing, service mesh has emerged as a critical technology for improving network performance, owing to its flexibility and scalability. However, massive online applications in edge clouds pose significant challenges to microservice orchestration, including high concurrency, complex service dependencies, strict response delay requirements, and fast orchestration needs. Addressing these challenges requires efficient and fast orchestration strategies, but existing approaches often lack accurate models and effective algorithms to handle these complexities. To tackle the above challenges, this paper proposes an efficient Symmetric Microservice Deployment (SMD) algorithm for fast orchestration. First, accurate modeling is achieved with the queuing network, which analyzes intertwined requests and calculates detailed delays. Moreover, the SMD algorithm simplifies the coupling between deployment and routing by considering internal dependencies during deployment. This integrated approach eliminates the need for separate routing solutions and ensures provable optimal performance under symmetric deployment. Experimental results demonstrate that, compared to four baseline algorithms, the proposed method reduces response delay by 25.5% and execution time by 58.4%, showcasing the potential and advantages of the algorithm for optimizing microservice orchestration in edge clouds networks.

Integrating Generative AI (Gen-AI) into vehicular computing can significantly enhance road safety and driving experience. The performance of Gen-AI inference in vehicular computing is highly sensitive to the number of inference steps, where even minor adjustments can disrupt the balance between delay and inference quality. This raises a critical question: how can inference steps be optimally determined for diverse tasks, given the inherent delay-quality trade-offs? Existing approaches fail to offer an optimal solution due to the lack of flexible inference services and efficient memory bandwidth allocation strategies. To address these challenges, we develop aCriticality-awareResource-efficientInference (CARIN) framework, where inference steps are dynamically adjusted to balance delay and quality for multi-criticality tasks. Leveraging an accurate inference model, CARIN fully exploits in-vehicle resources to accelerate both parameter loading and task computing during inference. The joint optimization problem of step control, memory bandwidth allocation, and compute resource scheduling is formulated as a mixed-integer nonlinear programming (MINLP) problem, and solved by a novel learning-to-optimize (L2O) algorithm efficiently. Experimental results demonstrate that, for high-criticality tasks, the proposed approach achieves$28.4\%$latency reduction and$36\%$quality improvement over baseline methods.

Detecting stowaways hidden in various transport vehicles, including cars, trucks, containers, and trailers, is crucial to border security inspection systems. Existing solutions mainly rely on contact-based sensors and manual inspection, which significantly compromise the efficiency of border control operations. Therefore, there is an urgent need for an automated, efficient and fast non-contact vehicle hidden person detection system. In this paper, we propose SiVe, a novel border inspection system that utilizes laser Doppler vibrometer (LDV) to detect hidden people in the vehicle. We extract signals associated with human activities (such as breathing, heartbeat, low-frequency body movements, etc.) from complex laser reflection data to detect the presence of hidden people. Specifically, we first employs the Empirical Mode Decomposition (EMD) algorithm to extract and reconstruct signals associated with human activities in complex and noisy environments. Then based on the characteristics of EMD outputs, we design a Time-series Variation Feature extraction Identification network (TVFI-net) model that accurately captures complex time-varying patterns for efficient and reliable detection of hidden people presence. Extensive real-world experiments validate the effectiveness of SiVe in various environments. The system achieves an average presence detection accuracy of 99.93$\%$for sedans and MPVs, 98.37$\%$for light trucks, 98.07$\%$for heavy trucks, and 95.23$\%$for trailers in non-contact detection of hidden people across twelve different vehicle types, under various indoor and outdoor environments.

LoRa holds significant promise for marine monitoring and communication due to its advantages of long range, low power consumption, and low cost. However, in marine environments, its communication performance is severely degraded by the strong absorption of electromagnetic waves by seawater. To enhance the reliability of communication under weak channels in the marine environment, this article proposes FCLoRa, a LoRa receiver enhancement system for low signal-to-noise ratio (SNR) marine environments. FCLoRa adopts a dual-domain cooperative strategy between the transmitter and receiver. On the receiver side, it employs a multilevel accumulation scheme to detect packets by aggregating the energy of windowed symbol “fragments” and reconstructs complete signals by fusing weak, fragmented signals from multiple gateways. On the transmitter side, it builds a two-dimensional polarization fingerprint library based on antenna attitude sensing and dynamically adjusts the transmission direction to match the polarization characteristics of the base station, thereby minimizing signal loss. Experimental results show that FCLoRa achieves a packet detection rate of nearly 40% at an extremely low SNR of –35 dB, and improves the average SNR by 1.92 dB compared to conventional LoRa reception, demonstrating its practical value in extreme marine scenarios.

The widespread adoption of wireless communication systems in both military and civilian applications has significantly advanced technological progress and social development across various industries. However, narrowband interference signals pose a significant challenge, severely disrupting the normal operation of wireless communication equipment. A major obstacle in existing narrowband interference detection lies in enhancing robustness under complex channel propagation conditions and diverse, dynamically changing types of interference. In view of those challenges, we propose a robustness-enhanced narrowband interference detection method by utilizing unlabeled data. The proposed detection network incorporates soft-shrink technology to isolate irrelevant signal features while adaptively extracting and fusing original and time-frequency features. The proposed method leverages the distribution characteristics of interference frequency bands to enhance model robustness in varying channel propagation environments. Additionally, we design a pseudo-label-based model tuning process to exploit the potential of unlabeled data, further enhancing the model’s robustness. Comparative experiments demonstrate the superiority of the proposed method against various baselines, as well as against configurations incorporating individual network modules.

In recent years, Wi-Fi sensing has attracted much attention due to the widespread deployment of communication devices. Due to advancements in signal processing algorithms, contactless sensing technology based on Wi-Fi signals has now been widely applied. However, the limited bandwidth of Wi-Fi systems constrains the performance of Wi-Fi sensing, posing challenges for accomplishing more fine-grained tasks (distinguishing more gestures or multiple targets, etc.). To address this challenge, in this paper, we design a spectrogram enhancement network for Wi-Fi channel state information (CSI) based on the characteristics of Wi-Fi signals to improve the sensing capability of Wi-Fi signals. Specifically, we use a neural network to generate super-resolution spectrograms of CSI to distinguish different time-frequency components in the environment at a finer granularity. Through extensive evaluation, we demonstrate that our designed system can achieve finer-grained perception accuracy than the state-of-the-art systems.

With the aging population, there is an increasing demand for rehabilitation services for people with chronic diseases. However, limitations such as medical resources, geographic barriers, and cost make home rehabilitation an option for more patients. Existing wearable devices and vision methods are effective but face problems with portability, cost, and privacy concerns. As for existing wireless sensing methods, they can only extract coarse features for activity recognition. Therefore, we present EchoHealth, which utilizes a smart speaker for rehabilitation exercise detection and assessment. We upgrade the smart speaker into an active sonar system without hardware modification to generate acoustic micro-distance images with motion information. Then, time-domain motion detection and distance-domain feature extraction are utilized to filter out the effects of non-motion time and distance to extract patient motion features for motion recognition. We further assess the patient's rehabilitation exercises from five aspects, based on which EchoHealth provides rehabilitation guidance. Extensive experiments with 15 participants performing 12 rehabilitation motions confirmed that EchoHealth can achieve 97.4% average accuracy in recognition of rehabilitation motion and provide accurate rehabilitation indicators in various environments.

Deep learning-based multi-view stereo (MVS) methods enable dense point cloud reconstruction in texture-rich areas. However, existing methods incur significant computational costs to capture pixel dependencies for complete reconstruction in low-texture regions. Additionally, discrete depth layers in occluded environments hinder the cost volume’s ability to model object information effectively. To address these issues, we propose a spatial-aware multi-view stereo network with attention cost volume, termed SA-MVSNet. The network introduces the pixel-driven spatial interaction (PDSI) module, which integrates the hierarchical spatial location enhancement mechanism (HSLE) and the spatial context aggregation mechanism (SCA). Leveraging an efficient parallel architecture, the PDSI module captures pixel-level spatial dependencies with the HSLE and strengthens global contextual information through the SCA. This design improves the network’s ability to represent features in low-texture regions while maintaining high inference efficiency. Furthermore, SA-MVSNet incorporates an attention weight generation branch that refines the cost volume by aggregating multi-scale depth cues, effectively mitigating the impact of occlusion. Experiments on the DTU dataset and the Tanks and Temples dataset show that our method outperforms other learning-based methods, achieving superior performance and strong generalization ability.

Light Detection and Ranging (LiDAR) odometry is a critical technology widely applied in pose estimation for autonomous driving and in Simultaneous Localization and Mapping (SLAM). By using a laser scanner, LiDAR captures environmental information to enable precise spatial localization and mapping. However, traditional LiDAR odometry methods mainly depend on static environmental features for positioning and mapping, limiting adaptability in dynamic settings and reducing pose estimation accuracy. To overcome this limitation, we propose ALO, a novel adaptive LiDAR odometry approach designed for dynamic environments. First, an adaptive constant velocity model predicts the expected motion trajectory, supplying prior pose information, while a first-in-first-out voxel grid manages the local map in dynamic conditions. Next, a linear system with dynamic weights based on point-surface residuals is established, minimizing the influence of dynamic features on pose estimation. Finally, the predicted prior pose serves as the initial value for adaptive Iterative Closest Point (ICP) registration, enhancing pose estimation accuracy and enabling real-time local map updates. Extensive experiments on the public KITTI dataset demonstrate that the proposed method achieves at least a 23.69% improvement in pose estimation accuracy over existing solutions.

Vehicular edge computing (VEC) emerges as a promising paradigm for processing computing-intensive parallel vehicular tasks, where vehicular tasks can be offloaded to the edge nodes [e.g., roadside units (RSUs)] to seek less computing delay. Considering the impact of computation services on offloading efficiency, there are several works that jointly study the decision making of task offloading and service caching. However, the existing works fail to consider the time-varying service requests and ignore the time-slots correlation of the computation services. To bridge the gap, this work designs a service-aware parallel task offloading approach, which is the first work to jointly explore time-varying computation services and task offloading based on real-world vehicular trajectory data in VEC networks. Specifically, we first propose a computation service prediction algorithm using the real-world vehicular trajectory data. Guided by this, RSUs flexibly precache computation services. Then, we propose a learning-based parallel task offloading algorithm, which allows vehicles to make offloading decisions based on the history of the edge selections. Furthermore, we conduct simulations to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces task delay by 45%, 58%, and 55% compared to the algorithms without service-aware computation offloading under various CPU cycles, task numbers, and time slots.

Traffic flow prediction is crucial for intelligent transportation and aids in route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices. We propose an online transfer learning (OTL) framework with a multi-layer perceptron (MLP)-assisted graph convolutional network (GCN), termed OTL-GM, which consists of two parts: transferring source-domain features to edge devices and using online learning to bridge domain gaps. Experiments on four data sets demonstrate OTL’s effectiveness; in a comparison with models not using OTL, the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.

Automatic Modulation Classification (AMC)-oriented Deep Neural Networks (ADNNs) have received much attention in recent years for their wide range of applications. However, they are vulnerable to attacks. Adversarial Examples (AEs) of modulation signals with added weak perturbations can easily fool ADNNs. The study of AEs on AMC, on one side, can enhance the security of wireless communication systems; on the other side, it can provide an effective defence against potential attacks. Nevertheless, most existing attack methods generate AEs with low transferability. In this paper, we propose a Multiscale Discriminative Attack Method (MDAM) for modulated signals. The method strives to alleviate such transferability issue by destroying discriminative features in multi-layer. Specifically, we utilize interpretable class activation maps to distinguish the discriminative regions, ignoring the noise and focusing on the interference of the discriminative features. Beyond that, we propose a multi-layer activation disruption loss to constrain activations in the middle layers. In so doing, the AEs do not erroneously retain deep features of the original signal. We conduct extensive experiments on RadioML datasets and the local area network (LAN) communication dataset we collected to evaluate the effectiveness of MDAM in both white-box and black-box attack scenarios. The results show that MDAM outperforms existing methods.

Voice assistants have been widely adopted for their ability to provide non-touch human-computer interaction. However, while they offer convenience, their continuous listening for specific wake-up words raises privacy concerns, as it may lead to eavesdropping on user conversations. To investigate this issue, we devised covert eavesdropping attacks by perturbing and replaying events generated during the user’s normal activation of the voice assistant. The results demonstrate the feasibility and harmfulness of such eavesdropping attacks. To counter these covert voice eavesdropping attacks, we propose an effective defense scheme called CrossUnwind. This scheme leverages the groundtruth that voice assistant wake-up requires hardware to generate and send wake-up events. Specifically, we designed a novel tombstone file parsing process and an accurate event discrimination algorithm to obtain detailed call station information of the wake-up event without compromising the system. This allows us to determine whether the current wake-up event was generated by hardware. We deployed CrossUnwind on real devices and compared it to well-known machine learning and deep learning methods. The results demonstrate that CrossUnwind can achieve high accuracy in eavesdropping detection with faster speeds and lower resource utilization.

The advent of decentralized finance has ushered in a transformative era in the financial sector, leveraging blockchain technology to facilitate peer-to-peer transactions without traditional intermediaries. Amidst this innovation, the DeFi landscape faces the pervasive threat of rugpulls, where developers abruptly abandon projects post-fundraising, leaving investors with devalued assets. This growing concern highlights a critical research gap in the proactive detection and prevention of such fraudulent schemes. To combat this, we propose RUGSCREENER, a temporal graph neural network-based solution to identify rugpull risks within DeFi transactions. It employs a dynamic representation of blockchain interactions, enriched with comprehensive node attributes and effective temporal graph learning techniques based on memory and attention mechanisms, effectively capturing the rapid-moving and complex transaction patterns indicative of potential fraud. Our evaluation is based on a newly compiled Ethereum dataset that includes two subsets: an unlabeled set with 1,882,114 transactions from 29,595 tokens for temporal graph representation learning, and a labeled set with 128,819 transactions from 1,000 tokens (500 rugpull and 500 benign) for downstream evaluation. Using this dataset, RUGSCREENER achieves a balanced accuracy of 95.7% in detecting rugpull tokens. Our extensive evaluation, utilizing the Ethereum dataset comprising 1000 tokens, showcases its robust performance with a balanced accuracy of 95.7% in detecting rugpull tokens. Remarkably, RUGSCREENER surpasses existing state-of-the-art graph learning models in detecting rugpull tokens with enhanced accuracy and reliability.

SplitFed Learning (SFL) represents a compelling distributed learning paradigm tailored for resource-constrained edge computing scenarios, wherein the privacy threat posed by Gradient Inversion Attacks (GIA) remains challenging. The unique architecture of SFL restricts the fed server’s access only to the client-side model’sdeficient gradients, which lack essential information about the original data. This absence of complete gradient information hinders traditional GIA methods, which rely on complete gradient information for effective data reconstruction, thereby significantly diminishing their effectiveness in the SFL context. In this paper, we propose a novel attack against SFL calledDeficient Gradient-based Inversion Attack(DGIA), which reconstructs original training data by artificially amplifying the ℓ2norm of deficient gradients. Through extensive evaluation of how GIA performance varies with different gradient magnitudes, we observe a definitive correlation between the gradient ℓ2norm and attack performance. Based on this correlation, we further optimize DGIA to identify the optimal gradient amplification scale that maximizes the information encoded in deficient gradients. This compensates for the restricted access to complete gradients and enhances the attack performance. We conduct extensive experiments to demonstrate DGIA’s performance across various SFL scenarios compared with other GIA schemes and show attack efficacy under general defenses.

Generative agents, capable of simulating human behavior and collaborating on complex tasks, have the potential to revolutionize the investigation of traffic accidents. In this study, we designed TAA (Traffic Accident Agents), an advanced framework based on extended large language models (LLMs) to digitally model urban accident handling procedures and stakeholder interactions. TAA formalizes the roles, responsibilities, and interactions of all stakeholders through natural language encoding, utilizing this knowledge base to orchestrate its execution workflows. Facilitates trusted agent interactions, generates comprehensive reports, and employs memory mechanisms to plan and optimize subsequent actions. We evaluated TAA performance across multiple versions of ChatGPT, focusing on its capabilities to generate reliable interactions, make context-sensitive decisions, maintain extended dialogues, and produce accurate reports in streamlined accident resolution scenarios. Our analysis included evaluations of token consumption and economic costs to ensure scalability and practicality, with TAA achieving 87.9% effectiveness on the GPT 4omini benchmark. Experimental results demonstrate TAA’s successful execution of urban accident handling workflows with maintained informational consistency. The framework shows broad applicability to accident investigation, reconstruction, and archival documentation. This work pioneers the use of generative agents as collaborative human proxies, offering a transformative pathway to advance the future of traffic accident management and investigation.

As urbanization accelerates, the rise in private car usage has become a double-edged sword, symbolizing economic growth while exacerbating urban air pollution due to increased carbon emissions. This paper studies the problem of carbon emission prediction of private cars in urban environments, enabling effective carbon emissions reduction and energy conservation guidance. Existing methods struggle with costly carbon emission collection and rely on precise emission factors, inaccuracies in modeling spatial similarities across urban regions, and complexities in modeling global temporal variations. To solve these issues, the Multi-View CollAboRative graph network (MVCAR) framework is proposed for private car carbon emission prediction. MVCAR employs a trajectory-based method to estimate carbon emissions from private car mobility to represent the spatial-temporal carbon emissions effectively. To capture the geo-spatial and semantic regional similarities of the carbon emissions, MVCAR constructs multi-view graphs and utilizes multi-view graph convolution networks. Furthermore, MVCAR integrates collaborative gated recurrent networks to model the spatial-temporal correlations of carbon emissions. The collaborative gated recurrent networks include a multi-view gated recurrent unit (GRU) and a sequential GRU. The multi-view GRU models the multiple spatial-temporal correlations of carbon emissions. A learnable temporal module embeds various temporal features, and further feeds these features into sequential GRU to capture the temporal variations of carbon emissions. Finally, a collaborative strategy that synergistically combines multi-view and sequential GRUs through stacked training. Extensive experiments on real datasets demonstrate the superiority of the proposed MVCAR.

Carbon emissions caused by passenger cars in cities are essentially responsible for severe climate change and serious environmental problems. Exploring carbon emissions from passenger cars helps to control urban pollution and achieve urban sustainability. However, it is a challenging task to foresee the spatio-temporal distribution of carbon emission from passenger cars, as the following technical issues remain. i) Vehicle carbon emissions contain complex spatial interactions and temporal dynamics. How to collaboratively integrate such spatial-temporal correlations for carbon emission prediction is not yet resolved. ii) Given the mobility of passenger cars, the hidden dependencies inherent in traffic density are not properly addressed in predicting carbon emissions from passenger cars. To tackle these issues, we propose a Collaborative Spatial-temporal Network (CSTNet) for implementing carbon emissions prediction by using passenger car trajectory data. Within the proposed method, we devote to extract collaborative properties that stem from a multi-view graph structure together with parallel input of carbon emission and traffic density. Then, we design a spatial-temporal convolutional block for both carbon emission and traffic density, which constitutes of temporal gate convolution, spatial convolution and temporal attention mechanism. Following that, an interaction layer between carbon emission and traffic density is proposed to handle their internal dependencies, and further model spatial relationships between the features. Besides, we identify several global factors and embed them for final prediction with a collaborative fusion. Experimental results on the real-world passenger car trajectory dataset demonstrate that the proposed method outperforms the baselines with a roughly 7%-11% improvement.

In this paper, we consider a cargo autonomous aerial vehicle (AAV)-aided multi-parcel pick-up and delivery network, where the communication ability of the AAV is provided by the ground base stations (GBSs). For such a system setup, our goal is to optimize the trajectory of the cargo AAV while minimizing the combined impact of total energy consumption and total outage time. Simultaneously, we aim to maximize overall user satisfaction throughout the entire flight duration. More specifically, we propose a pick-up and delivery of AAV (PDU) framework to address this problem and this framework consists of two parts. First, a simulated annealing (SA) algorithm is used to obtain the pick-up and delivery (P&D) order of parcels. On the basis of obtaining the P&D order through SA, we further use deep reinforcement learning (DRL) to optimize the flight trajectory of the AAV to ensure the expected communication quality between the AAV and GBSs. To verify the effectiveness of our proposed algorithms, we design three baseline strategies for comparison, and also investigate the effect of using the PDU framework with different weights. Finally, numerical results show that the performance of PDU strategy is improved by about 5%-30% compared with other strategies in solving the performance tradeoff of AAV energy consumption, communication quality, and user satisfaction.

Recent years, point-of-sale (POS) terminals are no longer limited to wired connections, with many relying on Wi-Fi for data transmission. Although Wi-Fi offers the convenience of wireless connectivity, it introduces significant security vulnerabilities. This work presents a non-intrusive method for eavesdropping POS passwords via Wi-Fi sensing, named${\mathsf {BeamThief}}$. Instead of conventional Wi-Fi Channel State Information (CSI) readings, our approach employs Wi-Fi Beamforming Feedback Information (BFI) for an eavesdropping attack. Compared to CSI, which can only be extracted through intruding into the Access Point (AP) or from a limited selection of commercial Wi-Fi cards (e.g., Intel-5300), BFI readings can be more readily obtained from a broad array of commercial Wi-Fi devices. A key technological contribution of${\mathsf {BeamThief}}$is the development of an analysis model for predicting finger motion trajectories. This model is based on the physical relationship between BFI readings and finger motion, thus eliminating the need for extensive labeled training data. Furthermore, we employ Maximum Ratio Combining (MRC) to enhance the BFI series, ensuring performance across various scenarios. We implement${\mathsf {BeamThief}}$using everyday commercial Wi-Fi devices and conduct a series of experiments to assess the impact of this attack. Experimental results demonstrate that${\mathsf {BeamThief}}$achieves an accuracy rate 79$\%$in inferring 6-digit POS passwords within the top-100 attempts.

In recent years, with increasing user demands for convenience, privacy, and personalized experiences, gait recognition has been widely studied across various domains, such as indoor intrusion detection and smart homes. Although computer vision solutions are extensively researched for their visual intuitiveness, Wi-Fi sensing is emerging as a new research focus due to its ability to preserve privacy. However, previous studies have primarily relied on abstract features with limited interpretability or required multiple Wi-Fi links. To address these issues, we propose Wi-GR, which utilizes a Wi-Fi link to extract robust and highly interpretable gait features for user recognition. First, we construct a multi-path gait signal model to establish a clear relationship between Channel State Information (CSI) and gait motion. Then, we design a gait signal separation and enhancement method to mitigate the effects of external non-target reflections and internal multi-part reflections, which significantly impact the extraction and interpretability of gait features. Finally, fine-grained gait features that visualize gait patterns are generated using MUSIC-based and GAN-based multi-part velocity profile generation algorithms, tailored for single-person and multi-person scenarios, respectively. Numerous experiments have demonstrated that Wi-GR achieves single-person recognition accuracies of 95.3%, 94.0%, and 93.2% for 30 persons in the meeting room, corridor, and lobby, respectively, and an average accuracy of 88.3% for two-person recognition.

Edge computing (EC) reduces task processing and content download delay by providing computation and caching resources directly to task offloading (TO) users and content request (CR) users. However, existing studies often focus exclusively on either TO users or CR users within EC networks, neglecting the interaction between these two groups. To address this gap, we investigate the offloading and caching decision-making in scenarios where TO and CR users coexist. Furthermore, we employ full-duplex (FD) technology to enhance spectral utilization for edge-end transmissions. Specifically, we jointly optimize offloading and caching in FD-enabled EC networks. To accomplish this, we decompose the formulated optimization problem into three sub-problems using the alternating optimization (AO) method. We then propose a three-subproblem alternating iterative delay minimization algorithm to effectively tackle the challenges of offloading and caching. Additionally, we analyze the convergence and complexity of our proposed algorithm. Finally, we conduct extensive simulations to evaluate the effectiveness of our approach. The simulation results demonstrate that the delay reduction achieved by our algorithm is between 24.78% and 89.23% greater than that of comparative algorithms.

With the commercialization of fifth-generation (5G) mobile communication technology and the rapid proliferation of mobile devices (MDs), demand for data computation is surging. This growth increases the reliance of MDs on low latency and high throughput. For this purpose, Mobile Edge Computing (MEC) enhances the user's data processing capability by offloading computation tasks to servers at the network edge. However, achieving high efficiency in task offloading is challenging due to factors such as decision complexity, network dynamics, and user data privacy protection. Additionally, energy causal constraints and the coupling between offloading proportions and resource distribution cannot be ignored. In this paper, we first establish a dynamic task offloading problem to optimize the long-term throughput of the system. Using perturbed Lyapunov optimization, we transform MD delay and energy threshold constraints into the stability control of corresponding virtual queues. Then, we propose the Lyapunov-guided federated deep reinforcement learning (DRL) online task offloading algorithm called LyFOTO, which combines a federated learning (FL) framework and an Actor-Critic (AC) model. Under favorable communication conditions, the LyFOTO algorithm adaptively boosts system throughput; under poorer conditions, it properly delays task offloading, without violating queue backlog constraints. Through mathematical analysis, we discuss the performance of the LyFOTO algorithm. Simulation experiments validate that LyFOTO effectively balances system throughput and device battery energy. Finally, Comparative results show that LyFOTO outperforms other benchmark algorithms in maximizing system throughput while ensuring task backlog and energy threshold constraints.

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, aVehicle-assistedCollaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.

With the development of Wi-Fi sensing, wireless-based gait recognition has become increasingly important as it supports a wide range of applications (person identification, disease diagnosis, etc.). However, two serious challenges limit the universal deployment of such Wi-Fi vision schemes: i) the limited bandwidth of Wi-Fi severely restricts the granularity of gait recognition, and ii) users non-gait behaviors (e.g., stopping and turning) interfere with the extraction of gait-related features. In this paper, we propose CSID, which can achieve robust gait recognition under the limited bandwidth conditions of commercial Wi-Fi devices. Specifically, we use a neural network to generate super-resolution spectrograms of channel state information (CSI), overcoming the limitation of insufficient Wi-Fi bandwidth. To overcome the challenge of non-gait behavior interference, considering the human-incomprehensible nature of Wi-Fi spectrograms, we adopt cross-domain adversarial training and further extract gait features that are independent of the interference behaviors by learning domain-independent representations. We conducted a large number of experiments in different indoor environments, and the average person identification rate of the CSID system reached 91.6%. These results demonstrate that the CSID system is promising and could be used as a complement to visual person identification systems in the future.

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios.

As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabledCommitteeVoting basedTaskOffloadingModel (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits relatedThompsonSampling basedAdaptivePreferenceOptimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.

Clustered federated learning (CFL) leverages data distribution similarities to cluster clients, facilitating personalized model training under data heterogeneity. However, most existing CFL schemes pose potential privacy risks for clients (e.g., gradient inversion attacks) as they rely on individual gradients for clustering. This also renders them incompatible with secure aggregation mechanisms that are widely employed in federated learning for privacy protection. Moreover, CFL introduces the risk of malicious clients dominating several clusters and conducting poisoning attacks therein, thereby threatening secure model training. To address these issues, we propose ProCFL, a Privacy-Enhanced and Robust CFL framework incorporating gradient-free clustering and peer validation. Specifically, we first design a new protocol for measuring data distribution similarity among clients without using their gradient information. Then, we transform the client clustering process into a weighted set covering problem and introduce a diversity-optimized clustering algorithm to achieve near-optimal clustering results while eliminating any need for prior knowledge. Furthermore, we develop a post-hoc detection mechanism that employs peer validation to identify and discard malicious client models. Extensive experimental evaluation of ProCFL validates its superior model robustness and accuracy performance compared to existing schemes.

Aggregating geospatial data plays a crucial role in location-based services. However, collecting such sensitive data raises concerns about location privacy leakage. Local Differential Privacy (LDP), as a de facto privacy paradigm, has been widely employed to ensure individual location privacy. Nonetheless, existing approaches for aggregating geospatial data under LDP either suffer from compromised accuracy or involve complex computations. In this work, we propose a history-aware geospatial data aggregation framework to enhance both accuracy and efficiency while guaranteeing LDP. To this end, we first investigate an efficient aggregation method, namely General Randomized Response (GRR), and find that its variance of aggregation error follows the sum of two zero-mean binomial distributions. This reveals that multiple aggregations can boost the accuracy of GRR. To obtain multiple aggregations without compromising privacy, we adopt a Markov transition model to complement current aggregations from historical ones. However, learning the Markov transition matrix on perturbed data is challenging. Accordingly, we propose a privacy-aware Markov Transition Matrix Estimation (MTME) algorithm. Finally, we introduce a truth discovery-based refinement algorithm to iteratively derive an accurate aggregated result from multiple inaccurate aggregations. We evaluate our proposed method on two real-world trajectory datasets, and thorough experiments demonstrate its superior accuracy and very low time overhead compared to competitors.

In this study, we address the problem of unnecessary computations in traditional continuous subgraph matching (CSM) frameworks due to premature expansions of the search space in dynamic graphs. Traditional CSM frameworks expand small partial matches according to a specific matching order until the final results are obtained. This extension involves two sequential steps: computing candidate vertices for an unmapped query vertex and expanding the search space using these candidate data. However, this long-established search model has a potential flaw, as premature expansions of the search space can lead to unnecessary computations. To address this issue, we introduce a novel search process, NewSP. Unlike traditional methods, NewSP emphasizes operations rather than extensions, incorporating a unique feature of postponing expansion at the operation level. This approach prevents premature expansions without compromising the initial pruning power of the selected matching order. Furthermore, NewSP allows for multiple consecutive expansions, paving the way for a multi-expansion strategy for further optimization. Our model also enables the implementation of cache strategies for candidate set reuse, as it does not necessitate immediate expansion of a candidate set once identified. To improve performance, we propose an adaptive index filtering strategy independent of the specific index used. Comprehensive experiments demonstrate that our method improves by up to two to three orders of magnitude compared to traditional algorithms. A case study showed that NewSP can accelerate the majority of subgraph matching algorithms.

Nowadays, point-of-sale (POS) terminals are no longer limited to wired connections, and many of them rely on Wi-Fi for data transmission. While Wi-Fi provides the convenience of wireless connectivity, it also introduces significant security risks. Previous research has explored Wi-Fi-based eavesdropping methods. However, these methods often rely on limited environmental robustness of Channel State Information (CSI) and require invasive Wi-Fi hardware, making them impractical in real-world scenarios. In this work, we present SThief, a practical Wi-Fi-based eavesdropping attack that leverages beamforming feedback information (BFI) exchanged between POS terminal and access points (APs) to keystroke inference on POS keypads. By capitalizing on the clear-text transmission characteristics of BFI, this attack demonstrates a more flexible and practical nature, surpassing traditional CSI-based methods. BFI is transmitted in the uplink, carrying downlink channel information that allows the AP to adjust beamforming angles. We exploit this channel information to keystroke inference. To enhance the BFI series, we use maximal ratio combining (MRC), ensuring efficiency across various scenarios. Additionally, we employ the Connectionist Temporal Classification method for keystroke inference, providing exceptional generalization and scalability. Extensive testing validates SThief’s effectiveness, achieving an impressive 81% accuracy rate in inferring 6-digit POS passwords within the top-100 attempts.

Wi-Fi signals are commonly used for conventional communication, yet they can also realize low-cost and non-invasive human sensing. However, Wi-Fi sensing in Multi-person scenarios is still a challenging problem. In this paper, we propose M2-Fi to achieve multi-person respiration monitoring using a handheld device. M2-Fi leverages Wi-Fi BFI (beamforming feedback information) performs respiration monitoring. As a compressed version of the uplink CSI (channel state information), BFI transmission is unencrypted, easily obtained using frame capture, and does not require specific firmware to obtain. M2-Fi is based on an interesting experiment phenomenon that when a Wi-Fi device is very close to a subject, near-field channel changes caused by the subject significantly cancel out changes from other subjects. We employed VMD (Variational Mode Decomposition) to eliminate the interference caused by hand movement in the BFI time series. Subsequently, we devised a deep learning architecture based on GAN (Generative Adversarial Networks) to recover fine-grained respiration waveforms from the respiration patterns extracted from the BFI time series. Our experiments on collected 50-hour data from 8 subjects show that M2-Fi can accurately recover the respiration waveforms of multiple persons with handheld devices.

Butterfly counting is widely used to analyze bipartite networks, but counting butterflies in original bipartite networks can reveal sensitive data and pose a risk of individual privacy, specifically edge privacy. Current privacy notions do not fully address the needs of both user-user and user-item bipartite networks. In this paper, we propose a novel privacy notion, edge decentralized differential privacy (edge DDP), which preserves edge privacy in any bipartite network. We also design the randomized edge protocol (REP) to perturb real edges in bipartite networks. However, a significant amount of noise in perturbed bipartite networks often leads to an overcount of butterflies. To achieve accurate butterfly counting, we design the randomized group protocol (RGP) to reduce noise. By combining REP and RGP, we propose a two-phase framework called butterfly counting in limitedly synthesized bipartite networks (BC-LimBN) to synthesize networks for accurate butterfly counting. BC-LimBN has been rigorously proven to satisfy edge DDP. Our experiments on various datasets confirm the high accuracy of BC-LimBN in butterfly counting and its superiority over competitors, with a mean relative error of less than 10% at most. Furthermore, our experiments show that BC-LimBN has a low time cost, requiring only a few seconds on our datasets.

Decentralized federated learning (DFL) overcomes the single point of failure issue of centralized federated learning. Building upon DFL, blockchain-based federated learning (BFL) takes further strides in establishing trust, enhancing security, and fault tolerance. However, BFL based on the classical linear blockchain exhibits diminished training efficiency in heterogeneous environments and is limited by the performance bottleneck of blockchain. Recent solutions introduce the directed acyclic graph (DAG) blockchain to address these issues, yet they compromise the verifiability of BFL, struggle with handling outdated models, and have a slow convergence speed. In this paper, we propose TGFL, a decentralized federated learning framework based on the Tree-Graph blockchain. The underlying blockchain structure of TGFL is designed as a block-centered DAG to support verifiable and semi-asynchronous training. To facilitate fast convergence, we design a pivot chain generation algorithm that topologically sorts the semi-asynchronous training process, guiding participants in sampling appropriate models. The consensus mechanism, which is closely integrated with federated learning, ensures that the TGFL can effectively resist attacks on the model and the blockchain system. Extensive experiments in various settings demonstrate that TGFL can achieve better training efficiency and model accuracy compared to three baselines.

Real-time indoor crowd counting plays an important role in many applications such as crowd control, resource allocation and advertisement. Current research predominantly relies on camera-based methods. However, computer vision-based solutions raise severe privacy and ethical concerns. In this paper, we propose a privacy-preserving counting solution called BeamCount based on Wi-Fi sensing. Instead of using conventional Wi-Fi Channel State Information (CSI) readings, we utilize Wi-Fi Beamforming Feedback Information (BFI) for crowd counting estimation. Compared to CSI which can only be extracted from few commodity Wi-Fi cards (e.g., Intel 5300), BFI readings can be obtained from a large range of commodity Wi-Fi devices. We establish a mapping relationship between BFI and headcount and extract headcounts from BFI inputs through a carefully designed adversarial network. Owing to the adversarial network's cross-domain capability, the proposed counting system can achieve high accuracy across different environments, demonstrating its generalization capability. To mitigate the effect of BFI compression on sensing performance, we adopt a novel time series prediction model. Extensive real-world experiments validate the effectiveness of BeamCount in various environments, achieving an average counting accuracy of 93.6%.

Wi-Fi signals may help realize low-cost and noninvasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractical for multi-user scenarios. Moreover, these proposals all fail to exploit Wi-Fi’s multiple-in multiple-out (MIMO) capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi sensing framework to support realistic multi-user scenarios. To thwart unauthorized eavesdropping while retaining the sensing and communication capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to physically encrypt Wi-Fi channels, treating the sensed human activities as physical plaintexts. The encryption scheme is further enhanced via an optimization framework, aiming to strike a balance among i) risk of eavesdropping, ii) sensing accuracy, and iii) communication quality, upon securely conveying decryption keys to legitimate users. We implement a prototype of MIMOCrypt on an SDR platform and perform extensive experiments to evaluate its effectiveness in common application scenarios, especially privacy-sensitive human gesture recognition.

Self-service libraries need self-service book collection and monitoring of book quality to improve user experience This article proposes a privacy-preserving alternative RFbook, a book classification and moisture sensing system formed from an array of passive commercial RFID tags. We have three key observations in designing RFbook for such benefits. The first observation is that when tags are in the vicinity, their interrogation currents can alter each other’s circuit properties, based on which unique phase and amplitude signatures can be obtained from the backscattered signal. The second observation is that books with different thicknesses and sizes of material will have different signal features. Finally, we found that changes in book humidity are reflected in the reader’s received signal strength (RSS). To turn the high-level idea into a practical system, we built a prototype of RFbook and conducted comprehensive experiments to evaluate the system’s performance. The experimental results show that RFbook can distinguish different types of books with an average accuracy rate higher than 96% and monitor the humidity change of the book.

Password, fingerprint and face recognition are the most popular authentication schemes on smartphones. However, these user authentication schemes are threatened by shoulder surfing attacks and spoof attacks. In response to these challenges, eye movements have been utilized to secure user authentication since their concealment and dynamics can reduce the risk of suffering those attacks. However, existing approaches based on eye movements often rely on additional hardware (such as high-resolution eye trackers) or involve a time-consuming authentication process, limiting their practicality for smartphones. This paper presents DEyeAuth, a novel dual-authentication system that overcomes these limitations by integrating eyelid patterns with eye gestures for secure and convenient user authentication on smartphones. DEyeAuth first leverages the unique characteristics of eyelid patterns extracted from the upper eyelid margins or creases to distinguish different users and then utilizes four eye gestures (i.e., looking up, down, left, and right) whose dynamism and randomness can counter threats from image and video spoofing to enhance system security. To the best of our knowledge, we are among the first to discover and prove that the upper eyelid margins and creases can be used as potential biometrics for user authentication. We have implemented the prototype of DEyeAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results indicate that DEyeAuth achieves a high authentication accuracy of 99.38% with a relatively short authentication time of 6.2 seconds, and is effective in resisting image presentation, video replaying, and mimic attacks.

Modern smartphones hold massive amounts of private and potentially sensitive user data (e.g., identity and messages). User authentication is the key measure to protect such sensitive data from adversaries. In this article, we explore a novel authentication mechanism, LipAuth, leveraging the unique spatial-temporal features (i.e., both static physiological and dynamic behavioral characteristics) of human lips biometrics for secure and convenient user authentication, without requiring any special sensors on smartphones. The key principle behind LipAuth is that the geometric structure of lips is unique across different users while consistent and stable for the same user, which is dependent on three types of static features, i.e., lip width, thicknesses, and the joint characteristic of the former two, and the dynamic features in smiling process, i.e., the bending processes of the boundary lines between the upper and lower lips. On that basis, LipAuth can accurately identify legal users by actively extracting the spatial-temporal features on the lips’ profile changes, while also remaining fast and easy to use. We have implemented the prototype of LipAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results show that LipAuth can achieve an overall 99.24% accuracy for user authentication and can resist potential intrusion from video replaying and mimic attacks.

The location context can benefit a broad range of context-aware applications, where recognizing and locating contextual objects, such as hair dryers, coffee machines, or water faucets, which are not equipped with any smart modules and thus unable to emit modulated signals, provide fine-grained contextual information. While there have been extensive researches on localizing smart mobile devices, little has been done for locatingcontextual objects, let alone for recognizing and locating them together. In this article, we aim to study the problem of simultaneously recognizing and locating such contextual objects and present CORAL, a contextual object recognition and locating scheme by the usage of unmodulated acoustic signals from the working contextual objects recorded by the commercial off-the-shelf smartphones of users. Specifically, CORAL exploits the frequency and power features of these signals to build a mel-frequency cepstral coefficients (MFCCs) data set for contextual objects, and constructs a classifier for contextual object recognition by using bidirectional LSTM (BiLSTM) and a regression model for object-to-device distance computation by using LightGBM, which is then used for object locating with the help of the user’s trace. We implement a prototype of CORAL and extensive experiments show that the CORAL achieves high recognition accuracy and locating accuracy, even when there are concurrent working contextual objects or ambient noises.

To balance venue safety with public photography rights, this article presents CamShield—a novel system for selective defense against unauthorized photography. Amid dense electromagnetic environments, CamShield reliably identifies cameras by analyzing their unintended electromagnetic emissions. By tracing frequency drift patterns and harmonic spectral movements unique to each device, CamShield can accurately detect cameras despite environmental noise or model similarities. An integrated antenna amplitude ratio module and Kalman filter further localize threats through resilient positioning. Directional ultrasonic beams then focus tuned acoustic interference toward devices, temporarily disrupting visualization in restricted locations while preserving ambient imaging freedoms. Comprehensive evaluations across three state-of-the-art object detectors quantify real-world reliability. With 30 intruding cameras, CamShield exhibited obstruction latencies below 346 ms. Furthermore, CamShield achieves three times the coverage using the same power as traditional Omnidirectional transmission. Together, the breakthroughs in pervasive camera sensing and context-aware actuation contribute toward advancing policy-centric access controls at the edge of cyber-physical convergence. CamShield sets an important precedent on enforcing venue custom protections in bounded secure zones without undermining positive public photography assumptions elsewhere.

Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).

Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. HeadTrack uses smartphones as ‘sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.

This letter proposes a closed-form design method for an infinite impulse response (IIR) digital approximation of the fractional-order operator$s^{\nu }$,$\nu \in (-1,1)$, with improved performance around a predefined low frequency$\beta$,$0 < \beta < 1$. The proposed method utilizes a modified indirect discretization strategy, where it generates a rational$s$-domain expression using the continued fraction expansion (CFE) of a scaled operator$(s/\beta)^{\nu }$to minimize the truncation error around$\beta$. The$s$-domain expression is then discretized using a well-matched$s$-to-$z$transformation and scaled back by$\beta ^{\nu }$to fit$s^{\nu }$. The new approximations are compared with recently-proposed models by simulation in terms of frequency response as well as QRS complex detection. The proposed method shows a great potential to attain low-order accurate models that can be immediately used for hardware realizations in a wide range of applications.

Large-scale cloud applications today are often deployed using multiple containers, and a container overlay network is the de facto method to provide connectivity among these containers. However, the existing tunneling-based overlay network incurs significant performance overhead due to the need of transformation for every packet. Recent workSlim, through manipulating connection-level meta-data, allows containers to use host OS sockets directly thus they can achieve good performance without extra packet tunneling. Nevertheless, the connection setup is significantly slowed down, which requires an extra round-trip communication between both sides to pass the mapping information of the host OS socket and the container socket. This greatly hurts the performance of many cloud applications that must process short connections at high speed. We proposeSlimFast, a low-overhead container overlay network which provides a fast connection setup.SlimFastdirectly uses the host OS socket for container communication asSlim. However,SlimFastneeds no extra communication during connection setup. We reserve a dedicated host port for the container network and use socket mapping table to locally find the right container socket during connection setup. We implementSlimFastwhich is compatible with existing container applications. Experiments show that,SlimFastcan improve the connection setup time by about 2.1x compared withSlim, meanwhile maintaining low-overhead during data transmission asSlim. This brings significant performance improvement to real applications. Particularly, testbed results show thatSlimFastimproves the throughput of Nginx proxy and Memcached by about 0.9x and 2.2x, respectively.

In this article, we explore intercity mobility in urban agglomerations by surveying people traveling across cities based on private car trajectory data. Specifically, we first adopt the statistical analysis method to mine the intercity mobility in terms of various metrics of travel trips, so as to gain a preliminary understanding of intercity mobility in urban agglomeration. Then, we utilize the tensor decomposition method to conduct in-depth study on the intercity mobility pattern from the perspectives of complexity and multidimensionality. We construct a 4-D tensor based on private car trajectory and point-of-interest (POI) datasets and define the functional similarity and geographic adjacency between regions. Finally, we design an alternating proximal gradient (APG)-based method to resolve the core tensor and factor matrix, leading to the fine-grained discovery of intercity mobility patterns on administrative divisions in the urban agglomeration. Extensive experiments are conducted to evaluate the analysis of intercity mobility, using a real-world dataset containing one-year private car trajectories from five cities in the selected urban agglomeration. The experiments show that the proposed method successfully captures 20 intercity mobility patterns, in which the factor matrices retrieve the patterns from different dimensions with core tensors characterizing correlations between patterns in factor matrices. Besides, the extracted intercity mobility patterns not only cover administrative areas with frequent intercity interactions, but also contain areas with less intercity interactions. It validates that the intercity mobility is consistent with the regional functions in urban agglomeration.

The huge values created by Big Data and the recent advances in cloud computing have been driving data from different sources into cloud repositories for comprehensive query services. However, cloud-based data fusion makes it challenging to verify if an untrusted server faithfully integrates data and executes queries or not. This is even harder for range-aggregate queries that apply aggregate operations on data within given ranges. In this paper, we propose a query authentication scheme, named${\sf MARS}$, enabling a user to efficiently authenticate range-aggregate queries on multi-source data. Specifically,${\sf MARS}$creates a VG-tree by subtly integrating Expressive Set Accumulator into a multi-dimensional G-tree while signing the root digest with a multi-source aggregate signature scheme. Compared with previous solutions,${\sf MARS}$has the following merits: (1)Practicality.Instead of treating range and aggregate queries separately, the user can directly verify the statistical result of selected data. (2)Scalability.Instead of authenticating the individual result from each source, the user can perform an aggregative validation on the integrated result from multiple sources. The experimental results demonstrate the effectiveness of MARS. For large-scale data fusion, the user-side verification time increases by only 103 ms as the amount of data sources increases by five times.

The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability of the method and the ability to extract modulation characteristics. This paper proposes an automated machine learning framework, AutoSMC, for signal modulation classification. An adaptive signal augmentation method is proposed to adapt to the network changes during the search process. In order to extract the modulation features effectively, an scalable convolutional random fourier feature block is proposed. Moreover, the initial search space of the framework is given. The Bayesian Optimization is used to drive hyperparameter optimization to achieve AutoSMC and obtain the optimal method state. Great experiments were carried out on RADIOML 2016.10A and RADIOML 2016.10B. Experimental evaluations on these datasets show that our approach AutoSMC achieves state-of-the-art results compared to the most relevant signal modulation classification methods.

The location information of cars contains great value, but the uncontrollable characteristics of public data and the difficulty in distributing benefits derived from the potential value of the data greatly reduces the enthusiasm for data owners to share their data. In addition, the current selective disclosure schemes based on merkle tree still require large costs when there are many data items. To solve these problems, a blockchain-based framework for sharing cars’ location information applicable to the online car hailing industry is proposed in this paper, enabling the sharing of cars’ location information while protecting passengers’ privacy through selective disclosure. The combination of homomorphic encryption and probabilistic verification enables a faster batch data verification compared to other blockchain-based data sharing schemes, as well as ensures the authenticity of the data uploaded to the blockchain. The experimental results show that the proposed selective disclosure mechanism based on hash exclusive or tree has lower costs than the baseline for cases with many data items. Moreover, the proposed framework meets both security and feasibility requirements. Specifically speaking, under the constraint of 128-bits security level, the costs of time and space on the location information during one drive are at microsecond level and kilobyte level, respectively. Finally, the scheme is suitable for scenarios with higher throughput.

Trajectory recovery aims to restore missing data for reconstructing high-quality human mobility trajectory, which benefits a wide range of intelligent transportation system applications ranging from urban planning to travel recommendation. Inspired by the inherent regularity of human mobility, existing approaches capture spatial-temporal transition regularities in historical trajectory for data recovery. Although promising, existing solutions suffer from two limitations.i)These methods fail to recover occasionally-visited points (OVP) due to the lack of semantic information when learning spatial-temporal transition regularities.ii)The information before and after missing data is not be fully utilized for trajectory recovery. To overcome the limitations, we propose a novel semantic-aware trajectory recovery framework. First, we leverage heterogeneous information network (HIN) to encode various semantic correlations for obtaining rich semantic embeddings, which are fused with temporal information to form spatial-temporal semantic context. Then, we develop a behavior attention mechanism to capture semantic behavior transition regularities for trajectory recovery based on the bidirectional spatial-temporal semantic context before and after missing data. Extensive experiments on four real-world datasets show that our proposed method outperforms the state-of-the-arts by 7%-11% in term of recall, F1-score and mean average precision.

The increasing misuse of commercial drones for illicit activities poses significant challenges in their detection and identification. Existing methods, such as acoustic-based, radio frequency-based, and computer vision approaches, face limitations due to factors like miniaturization, stealth, and background noise. In this paper, we propose E-Argus, a system that leverages the electromagnetic radiation (EMR) emitted by the memory of drones. It is a basic fact that, with all types of drones, the implementation of arbitrary behavior must be digested in the built-in memory, and electromagnetic radiation is thus generated. Specifically, the memory clock drives the switching regulator causing current fluctuations that generate EMR signals at the clock frequency. E-Argus combines the relationship between the flight pattern of the drone and the memory EMR signal, analyzes the unique side-channel signatures, and utilizes advanced neural network-based identification; E-Argus can accurately detect and identify various types of illegal drones. We designed a system prototype based on USRP B210 and conducted experiments in a wide range of scenarios. The evaluation shows that E-Argus has low latency, high accuracy, and robustness in real environments.

Accurate perception of intelligent vehicles is critical for the safe operation of autonomous vehicles. However, current perception methods often struggle to effectively detect intelligent vehicles when obstacles block their field of view. Collaborative perception, although attracting considerable attention, presents challenges in terms of privacy and data trust. In this study, we present a novel design for Enhancing Intelligent Vehicle (), a cost-effective and comprehensive perception system for intelligent vehicles. We discovered that during the process of memory caching raw sensing data in the intelligent vehicle’s system-on-chip (SOC), continuous fluctuating currents inside the memory result in the emission of Electromagnetic Radiation (EMR). As a result, intelligent vehicles actively expose themselves on the electromagnetic spectrum. is based on a set of specially designed antenna arrays that scan the spectrum and utilize a joint Kalman filtering algorithm to enhance EMR signals. The micro-Doppler signature of each EMR signal is then analyzed to identify signals from intelligent vehicles and construct a vehicle database. A multi-antenna joint estimation algorithm is also designed to further estimate the position, distance, and direction of the target vehicle. Our experiments demonstrate that offers advantages in terms of timeliness, robustness, and accuracy.

With the wide adoption of blockchains in data-intensive applications, enabling verifiable queries over a blockchain database is urgently required. Aiming at reducing costs, previous solutions embed a small-sized authenticated data structure (ADS) in each block header, so that a user can verify search results without maintaining a full copy of blockchain databases. However, existing studies focus on exact queries with difficulty to guarantee the freshness of search results. In this article, we propose two frameworks, called$\mathsf{veffChain}$and$\mathsf{veffChain++}$, to realize freshness authentication of rich queries over blockchain databases. Specifically,$\mathsf{veffChain}$concerns about verifiable latest-$K$exact queries and employs RSA accumulator to generate constant-size ADSs;$\mathsf{veffChain++}$integrates RSA accumulator into the Trie tree to further authenticate latest-$K$fuzzy queries. For improved scalability, an adaptive keyword splitting (AKS) solution is proposed to enable ADSs to be incrementally updated. Compared with the state-of-the-art work, our frameworks have the following merits: (1)Freshness Guarantee. The user can efficiently retrieve the freshest data from a blockchain database in a verifiable way. (2)Flexibility. The user can specify different query patterns on demand to retrieve data as accurately as possible. The detailed security analysis and extensive experiments validate the practicality of our frameworks.

Due to the large-scale data streams produced by distributed terminals, hybrid-storage blockchain (HSB) that combines on-chain and off-chain storages has emerged as a promising solution for secure data storage in decentralized applications. Because all the raw data is outsourced to an untrusted service provider (SP), existing solutions suggest to utilize an on-chain authenticated data structure (ADS) to verify query results retrieved off-chain. However, existing solutions support onlycoarse-grained authenticationmaking a user abandon all the query results once the validation fails. In this paper, we focus on realizingfine-grained authenticationfor range queries, enabling a user to distinguish authentic data from falsified results. Considering the heavy gas consumption of on-chain storage, we propose two multi-dimensional parity-based verification (MPV) schemes with a trade-off between off-chain and on-chain efficiencies. Our main idea is to design an accumulator-based ADS to summarize well-designed verifiable hypercubes, so that fake results can be quickly located by combining multi-dimensional faces failed validation. Compared with previous solutions, our MPV schemes allow a user to make efficient use of query results by filtering out errors, and thus have higher data utility. The detailed security analysis and extensive experiments demonstrate the security and effectiveness of our MPV schemes, respectively.

Door lock is regarded as a critical line of defending the privacy and security of personal areas. However, for inner doors in environments like factories, existing locking mechanisms can be poor in user-friendliness and high in cost. For instance, mechanical locks require carrying keys that inevitably compromise user experiences, while smart locks always require non-trivial sensors. Therefore, inner doors urgently require a lightweight unlocking scheme that can properly balance user-friendliness, cost, and security. To this end, we propose HandKey as a keyless unlocking scheme to supplement existing lock systems. HandKey relies on two principles: the simplicity of hand knocking doors and the uniqueness of vibration triggered by the knocking force. In other words, a door and a hand knocking it jointly form a unique physical system that generates hand-dependent and user-specific vibration signatures uniquely representing a user identity. In designing HandKey, we first analyze the vibration mechanism behind it and the impacts of gestures and door materials on vibration signatures. Then we innovatively construct a signal processing and deep learning-based pipeline to extract signatures robust to variable knocking behaviors for representing user identity. Finally, we implement a HandKey prototype and use extensive evaluation to demonstrate its security and effectiveness.

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short ofimplementabilitythanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we proposeMagSignthat leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components’ workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.

Vehicular edge computing (VEC) provides an effective task offloading paradigm by pushing cloud resources to the vehicular network edges, e.g., road side units (RSUs). However, overloaded RSUs are likely to occur especially in urban aggregation areas, possibly leading to greatly compromised offloading performance. Inspired by this, this article explores this situation by introducing an unmanned aerial vehicle (UAV) to address the VEC overload problem. Specifically, we formulate a novel online UAV-assisted vehicular task offloading problem to minimize vehicular task delay under the long-term UAV energy constraint. To solve the formulated problem, we first decouple the long-term energy constraint based on the Lyapunov optimization technique. In this way, the problem can be solved in a real-time manner without requiring future information. Then, we construct a Markov chain based on Markov approximation optimization to find out the close-to-optimal UAV-assisted offloading strategies. Furthermore, we derive a mathematical analysis to rigorously demonstrate the offloading performance of the proposed algorithm. Additionally, the simulation results show that the proposed method outperforms the baselines by significantly reducing the vehicular task delay constrained by the long-term UAV energy budget under various system parameters, such as the energy budget and computation workloads.

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, respectively. To address these issues, we propose IA-Track, a novel head motion tracking system that combines inertial measurement units (IMU) and acoustic sensing. Our wireless earphone-based method balances flexibility, computational complexity, and tracking accuracy, requiring only an earphone with an IMU and a smartphone. However, we still face two challenges. First, wireless earphones have limited hardware resources, making acoustic Doppler effect-based method unsuitable for acoustic tracking. Second, traditional Kalman filter-based trajectory restoration methods may introduce significant cumulative errors. To tackle these challenges, we rely on IMU sensor data to recover the trajectory and use smartphones to emit ”inaudible” acoustic signals that the earphone receives to adjust the IMU drift track. We conducted extensive experiments involving 50 volunteers in various potential IA-Track usage scenarios, demonstrating that our well-designed system achieves satisfactory head motion tracking performance.

Blink detection is essential for various human-computer interaction scenarios, such as virtual reality and driving state detection. It has gained significant attention from industry and academia alike in recent years. Existing non-contact detection systems (cameras, acoustics, etc.) have made significant progress, but various issues have prevented their widespread adoption, including privacy concerns, line-of-sight requirements, and cost issues. Therefore, there is a critical need for a simple and robust system that can detect eye blinks using common commercial equipment. In this paper, we propose BlinkRadar, which uses a low-cost customized impulse-radio ultra- wideband (IR-UWB) radar for non-contact and fine-grained blink detection. BlinkRadar can reliably detect driver blinks in driving conditions, making it possible to infer drowsy driving. To effectively extract the eye blink signal, we analyzed real experimental data to study the characteristics of the eye blink pattern and successfully used the multi-sequence variational mode decomposition (MS-VMD) algorithm to separate the blink signal from the noise signal. We conducted extensive experiments in two different environments (a quiet room and moving vehicles) and found that BlinkRadar had an average blink detection accuracy of over 96.2%. Our results demonstrate the feasibility of using UWB radar for non-contact eye blink detection.

Numerous mobile devices are equipped with voice assistants to facilitate contactless user-device interaction. However, the widespread availability of voice assistants also raises security and privacy concerns, as they can be maliciously triggered to perform voice eavesdropping. Although diverse attacks have been taken to manipulate voice assistants for eavesdropping, they exhibit deficiencies of limited attack scopes and conspicuous attack behaviors because they target specific voice assistants or require extra voice commands to activate them. To manipulate arbitrary voice assistants for covert eavesdropping attack, we conduct a comprehensive analysis of voice assistant implementation in the Android system and refine a universal workflow. Through meticulous analysis and experimental verification, we uncover an inherent vulnerability that in voice assistants across device types that can be awakened by an artificial faking Intent. Building on this significant discovery, we propose an attack termed VoiceEar. It leverages a malicious event generation file and a first-in-first-out Intent generation algorithm to trigger voice assistants within the normal workflow for eavesdropping, without voice commands. Finally, we deploy the VoiceEar attacks on 25 mainstream mobile devices, and invite 95 volunteers for eavesdropping activity perception testing. The results unequivocally demonstrate the seamless execution of VoiceEar attacks, with neither users nor devices awareness.

Microphone has been widely integrated into mobile devices to provide physical basis for human-device voice interaction. However, the microphone may be spitefully invoked by maliciousmobile applications(apps) with arousing security and privacy concerns. In this work, to explore the issue of illegal microphone access, we develop spiteful apps through native and injection development to access the microphone viciously on a series of mobile devices. The results demonstrate that baleful apps could enable the microphone arbitrarily without any hint. To combat the unauthorized microphone access behavior, we design amicrophone illegal access detection(MicDet) scheme by constructing a request-response time model using the Unix time stamps of voice icon touched and microphone invoked. Through conducting numerical analysis and hypothesis testing to effectively verify the request-response pattern of app's normal access, we detect illegal access by analyzing whether the touch operation matches the normal pattern. For friendly user experience, we design an intuitive floating window to alert users by displaying the name of the app that illegally accessed the microphone once the illegal behavior is detected. Finally, we apply our scheme to different mobile devices and test several apps, the experimental results show that the MicDet scheme achieves a high detection accuracy.

Millions of mobile devices are currently equipped withvoice assistant(VA) for robust identity authentication. Regrettably, VA authentication remains susceptible to voice spoofing attacks, encompassing playback, synthesis, and conversion attacks. Despite numerous proposed defense schemes, these solutions exhibit deficiencies such as limited versatility and cumbersome implementation. Many are specialized in detecting only one specific type of attack, necessitate additional equipment, or mandate placing the device in specific locations. In this study, we introduce a versatile and user-friendly scheme designed to counteract voice spoofing attacks by analyzing common nonlinear features inherent in vocalization systems. Initially, we demonstrate the nonlinear nature of both human and mobile device vocalization by scrutinizing the mechanisms and processes of voice generation. Subsequently, we develop a comprehensive nonlinear model and extract a universal acoustic nonlinear property to discern sounds produced by humans from those generated by loudspeakers, thereby enhancing resistance against spoofing attacks. Finally, we conduct extensive experiments utilizing a real-world collected dataset and the supplementary ASVspoof2017 dataset. Evaluation results reveal that the proposed scheme significantly improves accuracy and computation cost by nearly 40% and 15%, respectively.

Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose thePa-Count, an effective real-timePassengerCounting system deployed inside the vehicle via using Wi-FiCSI(Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers’ subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92$\%$.

Keyless entry systems (KES) have become popular due to their high user-friendliness, while fingerprint and digital password authentication are two of the most widely used unlocking ways. However, current KES are vulnerable to security threats, such as fingerprint films that deceive fingerprint sensors and stolen passcodes. To address these issues, this paper presents${\sf Fingerbeat}$, a two-factor authentication system to defend the security risks of the current widely deployed KES devices.${\sf Fingerbeat}$combines original credentials, such as fingerprints and passcodes, with unique and persistent finger-induced vibrations to create a two-factor secure authentication model, while ensuring user-friendliness.${\sf Fingerbeat}$leverages the fact that each person's finger structure is distinct and can be represented in distinct vibration patterns. During authentication, FIV is triggered and embodied in the mechanical vibration of the force-bearing body (i.e., KES panel), which can be captured by a low-cost accelerometer. We develop a proof-of-concept prototype of${\sf Fingerbeat}$, extracting FIV features from mixed vibration recordings and eliminating the impacts of variable behaviors and external disturbance. Finally, we conduct extensive experiments to demonstrate its security and effectiveness.

With the increasing awareness of secure data outsourcing, dynamic searchable symmetric encryption (DSSE) that enables searches and updates over encrypted data has begun to receive growing attention. Despite promising, existing DSSE schemes with forward and backward privacy are still hard to achieve authorized keyword searches on mobile devices while supporting secure and flexible updates. In this article, we propose a DSSE scheme, named$\mathsf{FLY_{++}}$based on a flexible index structure$\mathsf{Hybrid}$that incorporates the merits of inverted indexes and forward indexes while compacting the index size. Specifically,$\mathsf{FLY_{++}}$encrypts the newly added data with a fresh key and disperses previous keys into$\mathsf{Hybrid}$for forward privacy, while applying symmetric puncturable encryption (SPE) and a dual-key mechanism to realize backward privacy further. Compared with the state-of-the-art work,$\mathsf{FLY_{++}}$has the following advantages: (1)Authorized search. It dispenses with caching or re-encrypting search results, enabling a mobile device to search only designated keywords over the data outsourced before authorization. (2)Flexibility.It not only allows for sublinear search time, but also simultaneously supports fine-grained and coarse-grained updates of outsourced data. The detailed security analysis and extensive experiments conducted on a real dataset demonstrate the security and practicality of$\mathsf{FLY_{++}}$, respectively.

The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches such askeystroke inference(KI). However, the use ofchannel state information(CSI) in existing attacks is highly questionable due to its signal instability and hardness to acquire. Moreover, such Wi-Fi-based attacks are confined to only one victim because Wi-Fi sensing offers insufficient range resolution to physically differentiate multiple victims. To this end, we propose MuKI-Fi to enable, for the first time,multi-personKI, leveragingbeamforming feedback information(BFI), a new feature offered by latest Wi-Fi hardware, transmitted in clear-text by smartphones. BFI's characteristics, clear-text communication and signal stability, make it readily acquirable and usable by any other Wi-Fi devices switching to monitor mode without the need forlow-levelhacking on hardware. Moreover, to improve upon existing KI methods offering very limited generalizability across diversified application scenarios, MuKI-Fi innovates in an adversarial learning scheme to enable its inference generalizable towards unseen scenarios. Finally, we discover that, as a smartphone is in close proximity to a victim, the variations of BFI caused by that victim's keystrokes in suchnear-fieldsubstantially outweigh those caused by other distant victims; this phenomenon naturally allows for multi-person KI. Our extensive evaluations clearly demonstrate that MuKI-Fi can effectively eavesdrop on the keystrokes of multiple subjects, achieving 87.1% accuracy for individual keystrokes and up to 81% top-100 accuracy for stealing passwords from mobile applications(e.g., WeChat) on average.

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF-based mechanisms. However, existing approaches for portable devices solely track a single target, incapable of the ubiquitous and highly challenging multi-target situations such as double-hand multimedia controlling and multi-player gaming. In this paper, we proposeAMT$^+$, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. The challenge of multi-target occlusion is effectively addressed by employing multiple speaker-microphone pairs. However, the unique challenge raised by MIMO is the superposition of multi-source signals due to the cross-correlation among speakers. Initially, we tackle this challenge by designing a weak cross-correlation signal to reduce interference passively. InAMT$^+$, we’ve further integrated self-interference cancellation for active minimize interference. The most distinguishing advantage ofAMT$^+$lies in the elimination of the raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles.AMT$^+$employs Doppler filtering over delay subtraction for echo suppression. Further, by non-particle target reflections modeling results, we introduce a distance-projection-based method for continuous target identification and tracking. Implemented on commercial smartphones,AMT$^+$achieves on average 0.54 cm, 1.37 cm, and 2.13 cm errors for single, double, and triple target tracking respectively, and on average 97.0% classification accuracy for 14 controlling gestures.

Abundant valuable knowledge can be obtained by learning frequencies of relations in a decentralized network, which benefits various further complex tasks, such as range query and commodity recommendation. Nonetheless, counting frequencies in original networks can reveal sensitive data and pose a risk to individual privacy, specifically relation privacy. Current privacy notions do not fully preserve both the privacy of relations’ values and existence. In this paper, we introduce an enhanced privacy notion, relation local differential privacy (relation LDP), which provides comprehensive preservation in relation privacy. However, a significant amount of noise in perturbed networks often leads to severe errors in the accuracy of relation frequencies. To obtain accurate frequencies, we propose a framework called frequency estimation based on combination (Fest-C), which decomposes relation frequencies into two independent parts, the total frequency of relations and relative proportions. Binning relations into hyper-relations, Fest-C reduces errors of total frequency and relative proportions, respectively, and then estimates frequencies by logically multiplying them. Finally, we rigorously prove that Fest-C satisfies relation LDP. Our experiments on various datasets confirm the high accuracy of Fest-C in estimating relation frequencies with a much lower time overhead compared to competitors.

Spatial Crowdsourcing (SC) has been an indispensable Location-based Service where the SC server assigns tasks to workers based on the locations of task requesters and workers, raising strong privacy concerns. Limited by the computational and time complexity, existing works prefer differential privacy-based methods to protect location privacy. However, most differential privacy-based works ignore the road network, perturbing locations on two-dimensional plane, resulting in more failures in tasks and moreover extensive privacy disclosure in practice. This paper aims to implement a multi-task assignment with both high utility and efficiency while protecting the location privacy of both task requesters and workers on road networks. Specifically, we design a Road Network-aware Exponential Mechanism and propose an Obfuscated Locations Selection algorithm to guarantee location privacy of all participants and extensive privacy. Then, we propose region distance. Based on this, we further formulate multi-task assignment as a Binary Linear Programming problem and a utility-aware optimization problem. We solve the first problem to obtain optimal efficiency and then propose a utility-aware optimization algorithm for the second problem to improve the utility. Our experiments demonstrate sufficient and stable privacy guarantee and the well-performance on both utility and efficiency of our framework.

With the wide spread of data-driven deep learning applications, a growing number of users outsource compute-intensive inference processes to the cloud. To protect inference privacy, Liu (INFOCOM 2022) proposed two steganography-based solutions, named GHOST and GHOST+, relying on the mobile-cloud collaborative framework, where the mobile device hides sensitive images into public cover images before feature extraction, while launching adversarial attacks on the cloud-side deep neural network (DNN) to obtain desired results. Although both solutions demonstrate significant advantages in private deep learning, they suffer from limited practicality; since the inference accuracy decreases sharply as the hiding ratio increases. To address this, we propose two improved solutions, IGHO and IGHO+, which ensure high inference accuracy even when abundant sensitive images need to be hidden. Specifically, IGHO as the improved version of GHOST proposes two feature fusion methods, feature synthesis and pixel synthesis, to preprocess cover images, making the poisoned DNN learn hidden sensitive features better, while IGHO+as the improved version of GHOST+designs a novel feature mining generative adversarial network (FMGAN) to craft adversarial perturbations highly robust against variable sensitive types. Experimental results show that the proposed solutions highly improve the practicality of GHOST and GHOST+.

Point-of-interest (POI) recommendation has drawn much attention with the widespread popularity of location-based social networks (LBSNs). Previous works define long- and short-term trajectories via long short-term memory (LSTM) to capture user's stable and current preference, and incorporate context factors to improve recommendation effectiveness. However, these factors have different impacts on POI recommendation, and meanwhile, they are mutually influenced. Existing studies either model all the factors separately, or feed them into the same LSTM model, which are less meticulous for modeling the LBSNs trajectories. To address such issues, we revisit the long- and short-term preference learning for next POI recommendation by presenting a novel framework that can model both POI level and semantic level check-in trajectories. We develop a hierarchical LSTM to learn the two-level representations and consider the interplay of the two-level features by adding factors to the gates of LSTMs for each trajectory. We further construct a semantic filter to improve the recommendation efficacy. Experimental results using two real-world check-in datasets indicate that the proposed framework outperforms four state-of-the-art baselines regarding two commonly used metrics.

As smartphones proliferate, secure and user-friendly authentication methods are increasingly critical. Existing behavioral biometrics, however, are often compromised by behavior variability, leading to poor authentication accuracy and an unsatisfactory user experience. To fill this gap, we proposeBioHold, a new robust and reliable user authentication method, fusing finger behavior and hand geometry, captured via a smartphone's multitouch screen during natural holding gestures. It synergistically fuses behavioral and physiological biometrics. In contrast to traditional methods that require restrictive, unnatural user patterns, our approach utilizes a stable, natural gesture for authentication, effectively mitigating behavior variability. It enables one-handed authentication through familiar smartphone-holding and unlocking gestures. During this interaction, hand geometry and behavioral characteristics are recorded for subsequent authentication. We evaluate our method using a dataset collected from 20 subjects, demonstrating its resilience against behavioral variability over time while maintaining a high level of distinctiveness. With only 10 training samples, our method achieves an equal error rate of 3.59%, which improves to 1.25% with 40 training samples. Importantly, our method is resistant to common security threats such as zero-effort attacks, smudge attacks, and shoulder surfing attacks. A usability study confirms the method's high user acceptance, as measured by the system usability score.

Dynamic Adaptive Streaming over HTTP (DASH) stands as a leading streaming technology embraced by major video platforms and smart TV manufacturers worldwide. Despite its widespread use, the inherent diversity in both the video content and the client devices poses challenges, hindering DASH from consistently delivering top-notch playback quality for all users. This oversight often leads to network congestion, compromising the playback quality for users. To tackle these issues, we propose a Two-stage Stackelberg Game (TSBG) algorithm for personalized video streaming transmission in Edge Computing (EC) environments. The TSBG algorithm aims to optimize the Quality of Experience (QoE) of users by tailoring video streaming services between EC servers and clients. Initially, we establish the system model and define the video stream transmission problem as a multi-objective optimization problem, balancing server downlink resource scheduling and client adaptive bit rate. Subsequently, we design the TSBG algorithm, where an edge server allocation mechanism is adopted in the first stage to maximize overall user QoE, while users adjust their video bit rates based on the edge server's distribution plan to enhance their individual QoE in the second stage. We prove the existence and uniqueness of the equilibrium solution of the two-stage Starkelberg game and design an optimal pricing algorithm to maximize the benefits of edge servers. Extensive simulation experiments validate the effectiveness of the TSBG algorithm, showcasing its superiority in achieving enhanced QoE, network efficiency, and fairness compared to alternative approaches.

In connected autonomous vehicles (CAVs), the driving safety can be greatly deteriorated, in the presence of occlusions which are adverse to CAVs' perception of region-of-interest (RoI). Collaborative perception on the basis the information sharing of occlusions among CAVs, in a real-time and accurate manner, provides a means of the occlusion-free RoI perception for safe driving. In this paper, we propose a novel framework ofCollaborativeOcclusion-freePerception (COFP) in CAVs, to regain the real-time and accurate occlusion awareness. The innovative COFP targets two goals: well-balanced computation resource allocation, as well as fast and high-quality RoI information fusion. Specifically, the resource allocation problem, with the objective of minimizing CAVs' completion delay, is formulated as a multi-player continuous potential game and solved by a better response dynamics (BRD) algorithm. The RoI information fusion, with the objective of maximizing the overall object depiction quality, is formulated as a combinatorial optimization problem, and solved by a modified discrete salp swarm (MDSSA) algorithm. Experimental results show that the proposed COFP with 5GHz computing power can achieve full occlusion awareness for CAVs with 69.61% completion time reduction and 19.03% fusion quality improvement, compared to the existing methods.

Acoustic-based respiration sensing is promising due to its ubiquitous device support and great freedom in signal design. However, existing proposals often either fail to function properly when a target is non-static or is under multipath interference, or address it in an algorithmic manner. To this end, in this paper, we propose CORA, a COntinuous RespirAtion monitoring system using purely analytical signal processing methods. CORA is the first approach that achieves physical separation between motion artifacts and respiration, other than existing algorithmic solutions, and hence can obtain results that are closer to ground truth. CORA leverages the edges of Orthogonal Time Frequency Space signals in monitoring motion states and addressing multipath interference. The ability to tackle these challenges can help to compensate motion-induced artifacts for FMCW-based sensing techniques, enabling continuous respiration monitoring even in non-static scenarios. To achieve high-quality compensation, a pipeline of signal processing techniques is proposed, including robust moving target tracking, accurate frequency bin selection, and effective phase denoising. Unlike existing deep learning-based approaches, CORA is explainable and is readily deployable, without sophisticated adaptation or exhausted training processes. We have implemented a system prototype and evaluated its performance. Experiment results demonstrate a median error of 0.86 respiration per minute.

Sensing data acquired with crowdsensing are generally stored at central cloud servers, since massive data are involved and sensing devices do not have enough space to store them. Although each sensing device only has limited storage capacity, the total size of storage across thousands of devices can be considerable. In view of this, this paper addresses decentralized storage problem in mobile crowdsensing system, providing an alternative to cloud-based data storage. By investigating a virtual sensor model, the movement of a participant in the target sensing area is formulated as a random sampling over the data field related to this area. With a particular encoding algorithm, the data field is compressed into only one measurement along with a random sampling process. Each participant stores its own measurements as if various compressed snapshots of the data field are separately stored by different participants. We further investigate a recovery algorithm, reconstructing the original data field by carefully decoding enough measurements. Extensive experiments validate the proposed storage scheme under various crowdsensing scenarios, and our scheme achieves excellent performance in terms of recruitment overhead, decoding time, and decoding accuracy.

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

Recent years have witnessed the booming growth of 5G and 6G technology, which has brought unprecedented massive data transmission, causing severe privacy issues. However, traditional information encryption and multimedia covert communication fail to protect the identities of communication parties and the originality of messages. The emergence of blockchain provides a promising solution to solve these problems. Its anonymity manages to hide the identities of communication parties, and immutability ensures the message is undestroyable. However, the existing blockchain-based covert communication schemes suffer the issues of low embedding capacity and high time cost. In this paper, an amount-based covert communication scheme over the blockchain is proposed, in which a unique coding method is devised for hiding messages into transaction amounts to improve the embedding capacity. Compared with existing address-based methods, the proposed scheme can apply any address and reduce the time of obtaining special addresses. Besides, we innovate the way to prove the concealment by calculating the relative entropy of the transaction amount between Bitcoin and the proposed scheme. The security of our method is demonstrated by comparing the probability of attackers acquiring secret messages under different adversary capabilities. The experimental results verify that the proposed approach outperforms the existing schemes regarding embedding capacity, time costs, number of transactions, concealment, and security.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches, yet existing attacks relying on Wi-Fi CSI (channel state information) demand hacking Wi-Fi hardware to obtain desired CSIs. Since such hacking has proven prohibitively hard due to compact hardware, its feasibility in keeping up with fast-developing Wi-Fi technology becomes very questionable. To this end, we propose WiKI-Eve to eavesdrop keystrokes on smartphones without the need for hacking. WiKI-Eve exploits a new feature, BFI (beamforming feedback information), offered by latest Wi-Fi hardware: since BFI is transmitted from a smartphone to an AP in clear-text, it can be overheard (hence eavesdropped) by any other Wi-Fi devices switching to monitor mode. As existing keystroke inference methods offer very limited generalizability, WiKI-Eve further innovates in an adversarial learning scheme to enable its inference generalizable towards unseen scenarios. We implement WiKI-Eve and conduct extensive evaluation on it; the results demonstrate that WiKI-Eve achieves 88.9% inference accuracy for individual keystrokes and up to 65.8% top-10 accuracy for stealing passwords of mobile applications (e.g., WeChat).

Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4–6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.

This paper explores parallel computing systems for efficient subgraph query processing in large graphs. We investigate how to take advantage of the inherent parallelism of parallel computing systems for both intraquery and interquery optimization during subgraph query processing. Rather than relying on widely-used hash-based methods, we utilize and extend locality sensitive hashing methods. For intraquery optimization, we use the structures of both the data graph and subgraph query to design a query-constraint locality sensitive hashing method named QCMH, which can be used to merge multiple tasks during a single subgraph query processing. For interquery optimization, we propose a query locality sensitive hashing method named QMH, which can be used to detect common subgraphs among different subgraph queries, thereby merging multiple subgraph queries. Our proposed methods can reduce the redundant computation among multiple tasks duringa single subgraph query processing or multiple queries. Extensive experimental studies on large real and synthetic graphs show that our proposed methods can improve query performance compared to state-of-the-art methods by 10% to 50%.

Voice assistants support contactless smart device control and thus act as a holy grail of human–computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this article, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then, we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach does not interfere with normal voice interaction and need not to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.

Vehicle-to-vehicle (V2V) computation offloading has emerged as a promising solution to facilitate computing-intensive vehicular task processing, where task vehicles (i.e., TaVs) will be requested to offload computing-intensive tasks to server vehicles (i.e., SeVs) in order to keep task delay low. However, it is challenging for TaVs to obtain the optimal V2V computation offloading decisions (i.e., realizing the minimal task delay) due to the constraints, including: 1) incomplete offloading information; 2) degraded Quality-of-Service (QoS) of SeVs; and 3) privacy leakage risks. In this article, we develop a learning-based V2V computation offloading algorithm enhanced by SeV’s ability & trustfulness awareness to solve these problems. We emphasize that the proposed algorithm learns the offloading performance of candidate SeVs based on history offloading selections, without requiring the complete offloading information in advance. Additionally, both the QoS of SeVs and safe V2V computation offloading are enhanced in the proposed learning-based algorithm. Furthermore, we conduct extensive simulation experiments to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces the average task delay by 35% and 40%, and at the same time decreases the learning regret by 39% and 41%, compared to the algorithms without SeV’s ability and trustfulness awareness.

Heart disease has now become a very common and impactful disease, which can actually be easily avoided if treatment is intervened at an early stage. Thus, daily monitoring of heart health has become increasingly important. Existing mobile heart monitoring systems are mainly based on seismocardiography (SCG) or photoplethysmography (PPG). However, these methods suffer from inconvenience and additional equipment requirements, preventing people from monitoring their hearts in any place at any time. Inspired by our observation of the correlation between pupil size and heart rate variability (HRV), we consider using the pupillary response when a user unlocks his/her phone using facial recognition to infer the user’s HRV during this time, thus enabling heart monitoring. To this end, we propose a computer vision-based mobile HRV monitoring framework-PupilHeart, designed with a mobile terminal and a server side. On the mobile terminal, PupilHeart collects pupil size change information from users when unlocking their phones through the front-facing camera. Then, the raw pupil size data is preprocessed on the server side. Specifically, PupilHeart uses a 1-D convolutional neural network (1-D CNN) to identify time series features associated with HRV. In addition, PupilHeart trains a recurrent neural network (RNN) with three hidden layers to model pupil and HRV. Employing this model, PupilHeart infers users’ HRV to obtain their heart condition each time they unlock their phones. We prototype PupilHeart and conduct both experiments and field studies to fully evaluate effectiveness of PupilHeart by recruiting 60 volunteers. The overall results show that PupilHeart can accurately predict the user’s HRV.

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.

Autonomous driving has so far received numerous attention from academia and industry. However, the inevitable occlusion is a great menace to safety and reliable driving. Existing works have primarily focused on improving the perception ability of a single autonomous vehicle (AV), but the safety problem brought by occlusions remains unanswered. In this paper, we propose a multi-tier perception task offloading framework with a collaborative computing approach, where an AV is able to achieve a comprehensive perception of the concerned region-of-interest (RoI) by leveraging collaborative computation with nearby AVs and road side units (RSUs). Besides, the collaborative computation provides offloading service for computationally intensive tasks so as to reduce processing delay. Specifically, we formulate a joint problem of perception task assignment, offloading and resource allocation, by fully considering the AV’s mobility, task dependency, and delay requirement. The collaborative offloading is modeled as a mixed-integer nonlinear programming (MINLP) problem. We design a two-layer binary intelligent firefly (TL-BIFA) algorithm to solve MINLP, with the goal of minimizing execution delay. The proposed TL-BIFA synthesizes the advantages of heuristic methods and deterministic methods. Through extensive simulations, the proposed collaborative offloading approach and the TL-BIFA show superiority in enhancing the autonomous driving system’s safety, efficiency and resource utilization.

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically: 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALU outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback-Leibler divergence (KL), and R2.

Due to the explosive increase of enterprise network traffic, middleboxes that inspect packets through customized rules have been widely outsourced for cost-saving. Despite promising, redirecting enterprise traffic to remote middleboxes raises privacy concerns about the exposure of corporate secrets. To address this, existing solutions mainly apply searchable encryption (SE) to encrypt traffic and rules, enabling middlebox to perform pattern matching over ciphertexts without learning any sensitive information. However, SE is designed for searching pre-chosen keywords, and may cause extensive costs when applied directly to inspecting traffic in which the keywords cannot be determined in advance. The inefficiency of existing SE-based approaches motivates us to investigate a privacy-preserving and lightweight middlebox. To this end, this paper designs$\mathsf{SlimBox}$, which rapidly screens out potentially malicious packets in constant time while incurring only moderate communication overhead. Our main idea is to fragment a traffic/rule string into sub-patterns to achieve conjunctive sub-pattern matching over ciphertexts, while incorporating the position information into the secure matching process to avoid false positives. Experiment results on real datasets show that$\mathsf{SlimBox}$can achieve a good tradeoff between matching latency and communication cost compared to prior work.

Demand forecasting (DF) plays an essential role in supply chain management, as it provides an estimate of the goods that customers are expected to purchase in the foreseeable future. While machine learning techniques are widely used for building DF models, they also become more susceptible to data poisoning attacks. In this article, we study the vulnerability of targeted poisoning attacks for linear regression DF models, where the attacker controls the behavior of forecasting models on a specific target sample without compromising the overall forecasting performance. We devise a gradient-optimization framework for targeted regression poisoning in white-box settings, and further design a regression value manipulation strategy for targeted poisoning in black-box settings. We also discuss some possible countermeasures to defend against our attacks. Extensive experiments are conducted on two real-world datasets with four linear regression models. The results demonstrate that our attacks are very effective, and can achieve a high prediction deviation with control of less than 1% of the training samples.

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on noncooperative game theory. Then, we adopt the 0–1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. In addition, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms.

Urban region heat refers to the extent of which people congregate in various regions when they travel to and stay in a specified place. Predicting urban region heat facilitates broad applications ranging from location-based services to intelligent transportation management. The region heat is essentially characterized by the ‘arrive-stay-leave (ASL)’ behaviors, while it is a challenging task to well capture the spatial-temporal evolution of region heat since the following issues remain: i) ASL behaviors of private cars is usually heterogeneous resulting in a hierarchical distribution of region heat. ii) Urban region heat contains complex spatial-temporal correlations hidden in ASL behaviors and how to collaboratively integrate them is challenging. To address these challenges, we propose a Hierarchical Spatial-Temporal Network (HierSTNet) to forecast urban region heat, which contains two representations, namely, grid region from micro perspective and node region from macro perspective. For the grids, three-dimension spatial and temporal convolutional network (3D-STCNN) is proposed to model multi-scale properties in temporal dimension of ASL behaviors. For the nodes, multi-head graph attention networks are utilized to model the periodicity and spatial heterogeneity among macro region. Hierarchical structures are designed for multi-view modeling spatial-temporal distribution of ASL behaviors, by which they capture small-scale features in micro regions and embeds the global representation into graph propagation. Finally, we design an interaction decoder layer to integrate the external factors and aggregate spatial-temporal information across hierarchical structures. Extensive experiments based on real-world private car trajectory dataset demonstrate the superiority and effectiveness of proposed framework.

Though measuring ambient temperature is often deemed as an easy job, collecting large-scale temperature readings in real-time is still a formidable task. The recent boom of network-ready (mobile) devices and the subsequent mobile crowdsourcing applications do offer an opportunity to accomplish this task, yet equipping commodity devices with ambient temperature sensing capability is highly non-trivial and hence has never been achieved. In this paper, we proposeAcousticThermometer (AcuTe+) as an interference-resilient ambient temperature sensor empowered by a single commodity smartphone. AcuTe+ utilizes on-board dual microphones to estimate air-borne sound propagation speed, thereby deriving ambient temperature. To accurately estimate sound propagation speed, we leverage the phase of chirp signals to circumvent the low sample rate on commodity hardware. In addition, we propose to use both structure-borne and air-borne propagations to address the multipath problem. Most importantly, we equip AcuTe+ with a mask-based desnoising algorithm to handle intensive acoustic interference. As a mobile, economical, highly accurate sensor, AcuTe+ may potentially enable many relevant applications, in particular large-scale indoor/outdoor temperature monitoring in real-time. We have conducted extensive experiments on AcuTe+; the results demonstrate a median error of 0.6$^\circ$C even under severe acoustic interference (overall median 0.3$^\circ$C), and they also showcase the practical ability of AcuTe+ in real-time distributed temperature sensing.

We consider the problem of task offloading and resource allocation in mobile edge computing (MEC). To maintain satisfactory quality of experience (QoE) of end-users, mobile devices (MDs) may offload their tasks to edge servers based on the allocated computation (e.g., CPU/GPU cycles and storage) and wireless resources (e.g., bandwidth). However, these resources could not be effectively utilized unless an encouraging resource allocation scheme can be proposed. What’s worse, task offloading incurs additional MEC energy consumption, which inevitably violate the long-term MEC energy budget. Considering these two challenges, we propose an online joint offloading and resource allocation (JORA) framework under the long-term MEC energy constraint, aiming at guaranteeing the end-users’ QoE. To achieve this, we leverage Lyapunov optimization to exploit the optimality of the long-term QoE maximization problem. By constructing an energy deficit queue to guide energy consumption, the problem can be solved in a real-time manner. On this basis, we propose online JORA methods in both centralized and distributed manners. Furthermore, we prove that our proposed methods enable the achievement of the close-to-optimal performance while satisfying the long-term MEC energy constraint. In addition, we conduct extensive simulations and the results show superiority in performance over other methods.

We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-user distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise users within the same group. We first validate the performance of between-user distance estimation via conducting extensive experiments in a three-floor campus building and a shopping center, and the results show that WiDE outperforms other machine learning based approaches for between-user distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99 percent. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

A number of image compressive sensing (CS) algorithms were proposed in the past two decades, aiming at yielding recovered images with the best possible visual effect. However, it is quite difficult to further improve the image quality for human eyes. For example, in the low-rate sampling scenarios, CS algorithms always suffer degraded performance and can only recover less visually appealing images. We notice that what human beings concern with is the visual quality of an image, while machine users care much more about its latent metrics, such as recognition accuracy, rather than the subjective visual effect. Inspired by this point, we develop a machine recognition-oriented image CS with an adversarial learning strategy. Some adversarial models are investigated to make the recognition accuracy as an additional optimization goal of the CS reconstruction network. Through end-to-end training, CS reconstruction network automatically learns an image recognition pattern, and produce recovered images owning extra recognition metric, which makes them become more suited for machine users. Experimental results indicate that the images recovered with the proposed adversarial learning strategy can be recognized with significantly higher accuracy compared to that with the existing CS algorithms.

This paper presents a fast and robust centralized data center network (DCN) routing solution, called . For fast routing calculation, uses centralized controllers to collect/disseminate the network’s link-states (LS), and offload the actual routing calculation onto each switch. Observing that the routing changes can be classified into a few fixed patterns in DCNs which have regular topologies, we simplify each switch’s routing calculation into a table-lookup manner, i.e., comparing LS changes with pre-installed base topology and updating routing paths according to predefined rules. As such, the routing calculation time at each switch only needs 10s of us even in a large network topology containing 10K+ switches. For efficient controller fault-tolerance, purposely uses reporter switch to ensure the LS updates successfully delivered to all affected switches. As such, can use multiple stateless controllers and little redundant traffic to tolerate failures, which incurs little overhead under normal case, and keeps 10s of ms fast routing reaction time even under complex data-/control-plane failures. We design, implement and evaluate with extensive experiments on Linux-machine controllers and white-box switches. provides$\sim$1200x and$\sim$100x shorter convergence time than current distributed protocol BGP and the state-of-the-art centralized routing solution, respectively. Furthermore, Primus maintains good routing controllability/manageability thanks to its centralized architecture, which enables us to build several advanced routing features in our testbed, including routing failure visualization and weighted-cost-multi-path routing.

Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent L ight I ntensity P attern to Auth enticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth , we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

Detecting anomalous traffic is a crucial task for network management. Although many anomaly detection algorithms have been proposed recently, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper uses the tensor model for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low-rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). Besides only detecting random anomalies, our algorithm can also effectively detect structured anomalies that appear as bursts. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve higher Accuracy and Recall.

We consider the problem of dependent task offloading in edge computing with unknown system-side information (e.g., edge transmission rate and computation resources). In this problem, tasks have complicated dependency relationships and have no prior knowledge of system-side information to assist offloading decision-making. Although existing learning-based approaches can help to address unknown system-side information, the impact of inherent task dependency on such approaches has not been formally explored. To bridge the gap, we first use a breadth-first-search (BFS) method to decouple task dependency, and then leverage the Lyapunov optimization technique to transfer the long-term offloading problem to an online optimization problem. Furthermore, we employ the multi-armed bandit (MAB) theory to develop theonlinelearning-baseddependenttaskoffloading algorithm, called OL-DTO. The algorithm can address the unknown system-side information and is augmented with task dependency awareness. We present a rigorous theoretical analysis to evaluate the performance of this algorithm in terms of application delay and UD energy consumption. Our extensive experimental results demonstrate that the OL-DTO algorithm significantly reduces application delay while satisfying the long-term energy budget constraint of the UD.

The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eye-blink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and non-periodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context, and their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO, a novel user-Perception-based video Experience Optimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between device’s state of motion, BS factor, video bitrate and the resulting user-perceived quality. Specifically, by capturing the motion of on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

We study the problem of cross-modality person reidentification (ReID) and tracking with dual visible-infrared (VI) cameras, while most existing efforts on tracking-by-detection have been paid on single-modality visible ReID which is inapplicable for poor-light environments. The major difficulties for cross-modality (e.g., VI) ReID stem from the large modality gap between three-channel visible images and one-channel infrared images and such unknown environmental factors as background clutter, occlusions, etc. To tackle these issues, we propose to enrich the diversities of visible and infrared images for intra- and cross-modality matching by using both the channel-aware data augmentation (DA) techniques (e.g., channel exchanged augmentation and random occlusions) and standard DA techniques. On top of these DA techniques, we incorporate ResNet50 and vision transformer (ViT) into the feature extraction backbone network and apply the dynamic weight average (DWA) strategy for learning loss weights by regarding the minimization of identity loss and triplet loss as a multitask learning problem. We then apply the proposed ReID approach for person tracking in the field of interests. The experiments on two public data sets, i.e., RegDB and SYSU-MM01, show that our approach can improve the performance of state-of-the-art rank-1, mAP, and mINP for cross-modality matching. In addition, the experiments on our data set show that tracking by VI-ReID using dual VI cameras can achieve an accuracy of around 0.24 m.

In this article, we study the problem of passive human localization using an infrared (IR) thermal imaging camera which detects IR radiation emitted by human without carry-on devices and thereby generates a heat map of human body. Rather than directly using the heat map, we propose to exploit temperature of human body and design a lightweight approach for human localization using machine learning techniques. We observe that person-to-camera distance is closely related with the position and the size of a person’s head in the heat map, and several other features, such as variance, skewness, and kurtosis of temperatures in the head region are also good indicators of person-to-camera distance estimation. Accordingly, we propose a set of features and construct a model for inferring person-to-camera distance using machine learning techniques. With the estimated distance, we further compute human localization based on the relative position of the person in the heat map, the estimated person-to-camera distance, and the location and the DFoV of the IR thermal camera. Our experiments in real environments show that the proposed approach can accurately estimate person-to-camera distance and human localization with submeter errors.

The physical layer secret key generation scheme is a preferred solution designed for resource-constrained Internet of Things (IoT) devices. But it suffers from a severe attack, the signal manipulating attack, which aims at controlling the generated key. The existing solutions either cannot prevent all kinds of signal manipulation attacks or require working in full-duplex mode, which is not suitable for resource-constrained IoT devices. In this article, we introduce a secret key generation scheme with the help of an untrusted relay to address this dilemma. Also, our method can protect the privacy of legitimate users from the untrusted relay. We conclude a general signal manipulation attack model from existing practical signal manipulation attacks and analyze the security strength and privacy preserving ability of our scheme based on this model. Finally, we compare our method with existing signal manipulation attack solutions. The result shows that our method is the best solution for resource-constrained IoT systems.

Bothvoice communicationand automatic speech verification (ASV) over smart devices are vulnerable to the voice impersonation (VI) attack, which is often launched via imitating a target’s voice characteristics to deceive human auditory sense or fool the ASV system. Researchers have designed a number of defense schemes yet without the consideration of universality due to the lack of comprehensive data sets. In this article, we propose a universal defense scheme based on the VI data set collected from a famous TV show named “The Sound.” First, we deliver a thorough study on the VI attacks in both auditory and ASV systems to verify the collected simulated voice could spoof the auditory and the ASV system with a notable probability. Second, we propose a quasi-Gaussian distribution (QGD)-based defense scheme with the discovery about specific voice characteristics that are distinct between attackers and targets. Finally, we conduct extensive experimental results on our collected VI data set as well as the auxiliary ASVspoof2017 data set, to indicate the proposed QGD scheme outperforms the state-of-the-art schemes: backpropagation neural network, support vector machine, and Gaussian mixture model, in terms of accuracy.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.

With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones (FZs) on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors, such as weather and holidays on traffic is an unsolved problem. This article proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing FZs, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into FZs, calculate the interaction between FZs and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show the better prediction performance of the proposed model compared with other existing methods in a complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in a complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.

Privacy concerns over face recognition systems have attracted extensive attention in various fields. For gender privacy-preserving work, there are two key challenges: 1)privacy, i.e., confusing gender classifiers and 2)utility, i.e., maintaining its face verification performance. To address both issues, this article develops a novel gender-adversarial network, referred to as Gender-AN, to impart gender privacy to face images. Gender-AN employs an attribute-independent encoder–decoder GAN-based network to perturb the input face image, training with the assistance of the proper facial attributes. The perturbed image is then able to obfuscate gender classifiers while maintaining identity discriminability. To optimize the generator, a multitask-based loss function is utilized, which includes attribute manipulation loss, face matcher loss, adversarial loss, and reconstruction loss functions. This optimization facilitates our model to achieve the generalization, verification preserve, and natural appearance, simultaneously. Extensive experiments confirm the effectiveness of the proposed model in enhancing gender privacy and preserving face verification utility.

With the rapid development of various applications in the Internet of Things (IoT), we have witnessed much progress with very wide differences in characteristics and requirements. In this article, we propose a novel dynamic channel assembling (DChA) strategy for channel access of heterogeneous secondary user (SU) flows in IoT-oriented cognitive radio networks (CRNs), making use of the priority queues based on fine-grained flow classification. Specifically, three categories of SU flows are considered, so-called the real-time SU (RSU) flows, the elastic large SU flows, and the elastic small SU flows. On top of this, channel access opportunities are distributed to the SU flows in three specially designed queues performing the channel access algorithm. The highlight of our main idea is that the RSU flows with higher priority are only supposed to assemble as few channels as possible, so long as their minimum requirements are fulfilled, thereby minimizing the impact on elastic SU (ESU) traffic. For the sake of performance evaluation, we utilize the continuous-time Markov chain to model our proposed strategy and conduct theoretical analyses. With the detailed theoretical analyses and extensive simulations, the proposed DChA strategy is demonstrated to be able to fulfill the deadline of SU flows, while significantly reducing the blocking probability as well as the completion time of the ESU flows.

In this article, we analyze the performance of a mixed dual-hop power line communication/free-space optical communication (PLC-FSO) transmission system supporting both decode-and-forward (DF) and fixed-gain amplify-and-forward (AF) relaying protocols. Further, it is assumed that the PLC link experiences log-normal fading under the effects of additive background and impulsive noise, while the free-space optical (FSO) channel obeys an exponentiated Weibull distribution with pointing errors. On this basis, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end (e2e) signal-to-noise ratio (SNR). To evaluate the system performance, the closed-form expressions for the outage probability (OP) and average bit-error rate (BER) are derived. Additionally, to gain more insights, the asymptotic analysis of OP and average BER, and the upper bound of the average capacity are presented.

Convolutional neural networks (CNNs) have achieved remarkable performance in driver drowsiness detection based on the extraction of deep features of drivers' faces. However, the performance of driver drowsiness detection methods decreases sharply when complications, such as illumination changes in the cab, occlusions and shadows on the driver's face, and variations in the driver's head pose, occur. In addition, current driver drowsiness detection methods are not capable of distinguishing between driver states, such as talking versus yawning or blinking versus closing eyes. Therefore, technical challenges remain in driver drowsiness detection. In this article, we propose a novel and robust two-stream spatial-temporal graph convolutional network (2s-STGCN) for driver drowsiness detection to solve the above-mentioned challenges. To take advantage of the spatial and temporal features of the input data, we use a facial landmark detection method to extract the driver's facial landmarks from real-time videos and then obtain the driver drowsiness detection result by 2s-STGCN. Unlike existing methods, our proposed method uses videos rather than consecutive video frames as processing units. This is the first effort to exploit these processing units in the field of driver drowsiness detection. Moreover, the two-stream framework not only models both the spatial and temporal features but also models both the first-order and second-order information simultaneously, thereby notably improving driver drowsiness detection. Extensive experiments have been performed on the yawn detection dataset (YawDD) and the National TsingHua University drowsy driver detection (NTHU-DDD) dataset. The experimental results validate the feasibility of the proposed method. This method achieves an average accuracy of 93.4% on the YawDD dataset and an average accuracy of 92.7% on the evaluation set of the NTHU-DDD dataset.

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification.

Although users can obtain various services by sharing their location information online with location-based service providers, it reveals sensitive information about users. However, existing privacy-preserving techniques in the online scenario suffer from the following shortcomings. First, they model the correlations between the real trajectory and the distorted trajectory as undirected, which makes them unable to accurately quantify the data privacy leakage caused by sharing the distorted trajectory. Second, they are unable to protect semantic privacy, i.e., attackers can obtain the victims’ visit purpose by using the Point of Interest information without knowing the real location data. Additionally, they fail to balance semantic-aware data utility and privacy protection. To make the case even worse, compared to the offline scenario, sharing trajectory online in real time does not have access to the overall location trajectory. In this paper, we propose a novel semantic-aware privacy-preserving online location trajectory sharing mechanism, called SEmantic-aware Information-Theoretic Privacy (SEITP), to protect both data privacy and semantic privacy while the semantic-aware data utility can be preserved. In particular, we put forward two new metrics of privacy to capture data privacy leakage and semantic privacy leakage, respectively. Besides, to quantify the semantic-aware trajectory data utility, we propose a semantic-aware utility metric. With those metrics, the shortcoming of failing to guarantee the data utility is avoided naturally through structuring a multi-objective optimization problem. Then, we theoretically prove that the new construction can protect both data and semantic privacy. Finally, the experimental evaluations based on the real-world private vehicle trajectory dataset demonstrate that SEITP outperforms existing mechanisms.

For academic research and business intelligence, trajectory data has been widely collected and analyzed. Releasing trajectory data to a third party may lead to serious privacy leakage, which has spawned considerable researches on trajectory privacy protection technology. However, existing work suffers from several shortcomings. They either focus on point-based location privacy, ignoring the spatio-temporal correlations among locations within a trajectory, or they protect the privacy of each user separately without considering privacy leakage of the social relationship between trajectories of different users. Besides, they fail to balance privacy protection and data utility. Motivated by these limitations, in this article, we propose S 3 T -Trajectory, which is a utility-aware and privacy-preserving trajectory synthesis model that Resists social relationship privacy attacks. Specifically, we first develop a time-dependent Markov chain based on an adaptive spatio-temporal discrete grid to efficiently and accurately capture human mobility behavior. Then, we propose three mobility feature metrics from spatio-temporal, semantic, and social dimensions. On the basis of the metrics, we construct a bi-level optimization problem to accomplish the utility-aware and privacy-preserving trajectory synthesizing. The upper-level objective guarantees data utility and the lower-level optimization problems (or upper-level constraints) provides two-layer privacy protection for S 3 T -Trajectory, i.e., resisting location inference attacks and social relationship privacy attacks. We conduct extensive experiments on large-scale real-world datasets loc-Gowalla and loc-Brightkite. The experimental results demonstrate the effectiveness and robustness of S 3 T Trajectory. Compared with the baseline models, S 3 T Trajectory achieves between 7.8% and 23.8% performance improvement in resisting social relationship privacy attacks and achieves at least 5.19% improvement regarding data utility.

With the fast development of urbanization and motorization, an increasing number of people choose to buy private cars to fulfill their daily travel needs. In particular, many people from various positions of the city drive their cars to specified areas, and then they will stop and stay for a certain period of time, leading to a spatiotemporal evolution of urban area attractiveness (AA). In this paper, we aim at understanding urban AA based on analyses of private car trajectory datasets. Specifically, by extracting point-of-stop (PoS) data from the private car trajectories, we design the variational Bayesian Gaussian mixture models (VBGMM) to deduce the probability density distribution of PoSs and connect it to the variation of AA. We establish a deep learning model based on long short-term memory (LSTM) to capture the evolution of the AA. Furthermore, we integrate dropout in the LSTM method to address challenging issues such as overfitting and time-consuming training of complex neural networks in the AA prediction. We conduct experiments by using real-world private car trajectory data to evaluate the performance of the proposed method. The results validate that our proposed method outperforms existing ones in terms of various metrics. To the authors’ knowledge, our work is the first one to utilize private car trajectory data to study urban area attractiveness, thereby facilitating a new perspective regarding an understanding of human travel behavior and the evolution of urban mobility.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.

In network-wide monitoring, finding the large monitoring data entries is a fundamental network management function. However, the retrieval of large entries is extremely difficult and challenging as a result of incompleteness of network measurement data. Enlightened by tensor model’s strong capability of information representation and extraction, we model the network-wide monitoring data as a 3-way tensor. With tensor completion, the retrieval can be performed after recovering all missing entries. However, this not only incurs an extremely high cost when the tensor is large, but is also unnecessary. Instead, to quickly retrieve large entries at low cost, we transform the large entry retrieving problem to a cosine similarity searching problem, and propose two algorithms: 1) Quickly reordering the factor vectors based on Locality Sensitive Hashing (LSH) hash table so that vectors with small cosine distances are placed in the same hash bucket; 2) Quickly finding the similar vector of a queried one that the two together determine a large entry without incurring the high cost of recovering all entries through the dot products. In the process of LSH table building and similarity query, several novel techniques are proposed, including LSH table representation with the LSH forest, good hash table building to support the flexible search of cosine similarity, and bit-shifting-based quick similarity query. Our experimental studies on 4 real world datasets indicate that our technique is at least up to 60 times faster than the approach based on direct tensor completion.

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at https://github.com/siwangzhou/MCS-Storage .

Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including 1) the allocation of dynamic communication and computational resources, 2) delay constraints of heterogeneous tasks, and 3) requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is formulated as a delay-constrained long-term stochastic optimization problem under unknown prior statistical knowledge. To solve this problem, we first provide a technical path to transform and decompose it into several slot-level sub-problems. Then, we devise a distributed online algorithm, namely TODG, to efficiently allocate resources and schedule offloading tasks. Further, we present a comprehensive analysis for TODG in terms of the optimality gap, the worst-case delay, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.

Continuous user authentication is of great importance to maintain security for a mobile system and protect user's privacy throughout a login session. In this paper, we propose HandPass, a continuous user authentication system that employs the vibration responses of concealed hand biometrics, which are passively activated by the natural user-device interactions on the touchscreen. Hand vibration responses are instantly triggered and embodied in the mechanical vibration of the force-bearing body (i.e., the mobile device and the holding hand). Therefore, a built-in accelerometer can effectively capture the intrinsic features of hand vibration responses. The hand vibration response is determined by the trigger force and the complex hand structure, which is unique to each user and is difficult (if not impossible) to counterfeit. HandPass is a passive hand vibration response-based continuous user authentication system hosted on smartphones, with advantages of non-intrusiveness, high efficiency, and user-friendliness. We prototyped HandPass on Android smartphones and comprehensively evaluated its performance by recruiting 43 volunteers. Experiment results show that HandPass can achieve 97.3 % overall authentication accuracy and only 1.8 % false acceptance rate in diverse scenarios.

Modeling user preferences is a challenging problem in the wide application of recommendation services. Existing methods mainly exploit multiple activities irrelevant to user's inner feeling to build user preference model, which may raise model uncertainty and bring about prediction error. In this paper, we present PupilMeter - the first system that moves one step forward towards exploring the correlation between user preference and the instant pupillary response. Specifically, we conduct extensive experiments to dig into the generic physiological process of pupillary response while viewing specific content on smart devices, and further figure out six key time-series features relevant to users' preference degree by using Random Forest. However, the diversity of pupillary responses caused by inherent individual difference poses significant challenges to the generality of learned model. To solve this problem, we use Multilayer Perceptron to automatically train and adjust the importance of key features for each individual and then generate a personalized user preference model associated with user's pupillary response. We have prototyped PupilMeter and conducted both test experiments and in-the-wild studies to comprehensively evaluate the effectiveness of PupilMeter by recruiting 30 volunteers. Experimental results demonstrate that PupilMeter can accurately identify users' preference.

This paper presents a fast and robust centralized data center network (DCN) routing solution called Primus. For fast routing calculation, Primus uses centralized controller to collect/disseminates the network's link-states (LS), and offload the actual routing calculation onto each switch. Observing that the routing changes can be classified into a few fixed patterns in DCNs which have regular topologies, we simplify each switch's routing calculation into a table-lookup manner, i.e., comparing LS changes with pre-installed base topology and updating routing paths according to predefined rules. As such, the routing calculation time at each switch only needs 10s of us even in a large network topology containing 10K+ switches. For efficient controller fault-tolerance, Primus purposely uses reporter switch to ensure the LS updates successfully delivered to all affected switches. As such, Primus can use multiple stateless controllers and little redundant traffic to tolerate failures, which incurs little overhead under normal case, and keeps 10s of ms fast routing reaction time even under complex data-/control-plane failures. We design, implement and evaluate Primus with extensive experiments on Linux-machine controllers and white-box switches. Primus provides ~1200x and ~100x shorter convergence time than current distributed protocol BGP and the state-of-the-art centralized routing solution, respectively.

Enabling contactless and device-free hands tracking on mobile device leads to new user interaction experiences. In this article, we propose CTrack, a device-free and collaborative hands motion tracking solution for above-device interaction by using acoustic signals. CTrack does not require instrumenting hands with sensors. We achieve this by transforming the device into an active sonar system that transmits inaudible sound signals and tracks the echoes of the hand at its microphones. To guarantee subcentimeter-level tracking accuracies, we present an adaptive approach that uses the chirp’s time of flight to accurately measure the distance from the hand to an in-built speaker array. Then, the hand, speaker array, and microphone array yield a set of different ellipses. The hand position can be pinpointed exactly by solving and optimizing the intersection of these ellipses. Our evaluation shows that CTrack can achieve 2-D motion tracking with an average accuracy of 14 mm using the in-built microphones and speakers of a Nexus 6P.

Existing studies on indoor navigation often require such a pre-deployment as floor map, localization system and/or additional (customized) hardwares, or human motion traces, making them prohibitive when the situation deviates from these requirements (e.g., navigating a crowd of panicking people where no localization system or motion traces are available). The main observation inspiring our work without reliance on such pre-deployment is that when there are sufficient participants (e.g., a crowd of panicking people), the WiFi signatures collected by participants can serve as the fingerprints (referred to as location fingerprints) of their unknown locations. By computing relative positions of these location fingerprints we can connect them to form a global map. Such a map reflects the topology of the underlying walkable space and thus holds the potential of offering a navigation path for any intended users. Based on this observation, we design Fly-Navi, a crowdsourcing based indoor navigation system via on-the-fly map generation, and primarily designed for indoor environments with rectilinear and narrow corridors. Specifically, each participant uploads sensory data, and the server then generates a global map (on-the-fly map) through a series of operations such as local map generation, local map stitch and edge computation. On top of the global map, Fly-Navi computes a navigation path to the given destination and tracks the progress. We implement the prototype of Fly-Navi and our experiments show that Fly-Navi can quickly generate a correct global map with the 80-percentile of between-fingerprint distance error less than 3 meters, which is important for computing turning points of the map and hereon offering turn-by-turn instructions, and correctly navigate the intended users to their destinations.

Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94 percent material identification accuracies for 10 liquids and differentiates even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users’ location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.

The number of vehicles has increased year by year, especially individual vehicles. In addition to meeting basic transportation needs, vehicles are expected to serve varied location-based services and applications for humans. However, it can constitute severe risks for privacy. In this article, we concentrate on one of the most sensitive information, namely, social relationships, that can be inferred from the vehicle mobility data. We propose a social relationship inference model, which provides a new perspective for privacy preservation in human mobility data. In particular, we extract discriminative features from both the spatial and temporal dimensions. Then, the heterogeneous features are being merged with a fusion model to improve the performance of inference. Extensive experiments on the real-world data set validate the effectiveness of the extracted features in estimating social connections and demonstrate that our method significantly outperforms the baseline models.

Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users' quality-of-experience guaranteed.

With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) for years. Often the HetNets include a macrocell base station (MBS), several sets of macrocell users (MUs), a large number of femtocell base stations (FBSs), and femtocell users (secondary users), where the femtocells help the macrocell system relay the uplink or downlink traffic between the MUs and the MBS. In this article, we propose a novel joint information and energy cooperation method, with the aim of enhancing the spectrum and energy efficiency (EE) for cognitive HetNets. Specifically, the MUs and the femtocells harvest wireless energy from the radio frequency signal transmitted by MBS. By using the harvested energy, femtocells obtain the transmission opportunity to forward the signals of their serving users. We theoretically derive the theoretical expressions of the outage probabilities of the primary link as well as the secondary link. Then, we focus on investigating how to maximize EE by jointly considering time allocation and power control. Furthermore, we formulate the EE maximization problem, which contains the fractional form objective function and the linear inequality constraints and hence is nonconvex. To resolve this, we integrate the Dinkelbach method with convex optimization to derive the tractable and optimal solution. The numerical results demonstrate the simulations well match our theoretical analysis. Moreover, the results validate the feasibility of the proposed method for high-quality transmission without incurring extra energy consumption.

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.

This Special Section aims at providing a platform for sharing the state-of-the-art research and development on end-edge-cloud orchestration and publishing original research and peer-reviewed articles targeted to all readers of IEEE Transactions on Industrial Informatics. The content of the special issue focus on several topics that are recently concerned in the community, including the architectures and implementations, communication and networking protocols, computation offloading strategies, advanced machine learning and data analytical methods, performance modeling and optimization, and other enabling technologies for end-edge-cloud orchestrated systems and its industrial applications.

Understanding the travel behavior of private cars will generate promising solutions on addressing urban problems such as alleviating traffic congestion and improving transport services. In this paper, we focus on investigating the individual travel patterns of private car users based on a large-scale private car trajectory dataset. To achieve this goal, we first analyze the stop-and-wait information from the private car trajectory data and utilize DBSCAN method to implement clustering with the aim at identifying the frequently-visit places (FVPs). After that, we leverage Markov chain to study the spatial-temporal transition characteristics when private cars travel among their FVPs. Finally yet importantly, we design the concept of spatial-temporal entropy rate and conduct a quantitative study for measuring the regularity of each individual private car's mobility. We validate the proposed methodology based on a real-world dataset including 25,564 private cars driving during one month in China. Extensive experiments demonstrate that the proposed method outperforms the existing methods in terms of the accuracy on measuring the mobility behavior. Moreover, we observe that, on one side, the travel pattern is easier to mine from the private car users with fewer FVPs, on the other side, there are also a small number of users whose FVPs are large, while their mobility are relatively regular. Our work is the first effort to explore individual travel patterns of private car users via studying private car trajectory big data, thereby being able to provide new insight into the research of human travel activities, traffic management and urban planning.

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

A vaccine is a biological production that is related to people's lives. Currently, vaccine production supervision is very rough. The vaccine production records are completely controlled by the enterprise. Enterprises only submit production records to review agency for review when they need to sell vaccines. Production records are easy to forge and modify. In order to solve the shortcomings of traditional centralized management. We propose a supervision method for vaccine production based on double-level blockchain. We have designed a double-level blockchain structure. The first level is private data of vaccine production enterprise, including production records and corresponding hash. The next level is public data, including production records hash and vaccine information. In this way, we make vaccine enterprise to submit production records in a timely manner without fear of privacy leaks. We avoid enterprise tampering or falsification of production records through the non-tampering features and time stamps of the blockchain. Through these methods, we have realized efficiency supervision of vaccine production.

SAR remote sensing has shown its excellence in providing important information for damage mapping in a large area. SAR change detection is a fast and efficient way to identify damaged buildings by comparing post-seismic to pre-seismic images. When a building collapses after an earthquake, the corresponding SAR signal could be stronger or weaker. So it is hard to decide whether a target is damaged by simple intensity difference or ratio. In this paper SAR correlation and texture are combined to explore the function between building damage degree and SAR texture change.

The earthquake disaster not only caused great damage to the original ecological environment system, but also even formed a new ecological system. Remote sensing technology has played an important role in post-earthquake emergency and disaster assessment with its rapid, high revisit cycle and macro-micro comprehensive observation. The effective use of multi-period remote sensing data combined with the theory of eco-environmental impact assessment can build an effective model to evaluate the dynamic changes of ecological environment. In this paper, taking Wenchuan County in Wenchuan Earthquake as a case study, we proposed a comprehensive evaluation index method and the eco-environmental quality evaluation of Wenchuan County after 10-year earthquake were constructed and the changes of eco-environmental quality were analyzed. The result of the analysis showed that ten years after the earthquake (2018), the eco-environmental quality of poor regions in the county had also exhibited an obvious improvement. The eco-environmental quality in some regions in the county had fully recovered. The results are of great significance to scientific disaster relief and reasonable restoration and reconstruction.

The wavelet multi-scale decomposition method was used on the DEM data to generate a landform data with low frequency topographic signal which can indicate the tectonic zone information. According to the result, the third order approximation map of DEM can indicate the large scale tectonic characteristics which represented by the low frequency signals in landform. Based on the third order approximation map, we calculate the slope to identify, reclassify and extract the tectonic blocks and its margin tectonic belt around the Qinghai-Tibet Plateau. Compared with the traditional tectonic divisions, the automatic zonation based on DEM can characterize most of the important tectonic blocks and its margin belt, and it is found that the scale of the boundary between the Lhasa Block and the Qiangtang Block is larger than the boundary between the Qiangtang Block and the Bayan Har Block.

Trajectory clustering aims at grouping similar trajectories into one cluster. It is an efficient way of finding the representative path or common trend shared by different moving objects, and also provides a foundation for movement pattern mining, anomaly detection and other applications. Existing trajectory clustering studies mainly rely on feature selection and similarity measurement based on their geographical and spatial properties. However, one obstacle hindering their wide usage is the problem of clustering accuracy in the presence of noisy or incomplete sensing data, due to limited sensory device quantity, communication errors, sensor failures, and sensor vacancy. This paper proposes an error-tolerant trajectory clustering approach by incorporating denoising methods.We propose the Robust Deep Attention Auto-encoders model (called Robust DAA) to learn the representations of low-dimensional denoising trajectories with three novel features. First, we present the deep attention auto-encoders by integrating the attention mechanism into the classical deep auto-encoder, which is capable of enhancing feature propagation and feature selection. Second, we train the deep attention auto-encoder by applying proximal method, back propagation and the Alternating Direction of Method of Multipliers (ADMM). As a result, our Robust DAA can reduce the negative influence of the noise on trajectory data. Finally, we perform clustering over the low-dimensional denoising representations using traditional clustering algorithms and demonstrates the quality of the clustering results by comparing our approach with existing representative methods. Extensive experiments are conducted on both synthetic datasets and real datasets. The results show that our approach outperforms the existing models in terms of accuracy, precision, recall and f1-score.

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents' communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars, and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during the vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian particle filter for vehicle state estimation (nGPF-VSE) algorithm wherein we utilize the genetic operator resampling (GOR) technique to enhance the efficiency of particle filter (PF) relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that nGPF-VSE outperforms existing methods in terms of vehicle state accuracy.

Internet of Things (IoT) applications bring in a great convenience for human’s life, but users’ data privacy concern is the major barrier toward the development of IoT.${k}$-anonymity is a method to protect users’ data privacy, but it is presently known to suffer from inference attacks. Thus far, existing work only relies on a number of experimental examples to validate${k}$-anonymity’s performance against inference attacks, and thereby lacks of a theoretical guarantee. To tackle this issue, in this paper we propose the first theoretical foundation that gives a nonasymptotic bound on the performance of${k}$-anonymity against inference attacks, taking into consideration of adversaries’ background information. The main idea is to first quantify adversaries’ background information, and from the point of the view of adversaries, classify users’ data into four kinds: 1) independent with unknown data values; 2) local dependent with unknown data values; 3) independent with certain known data values; and 4) local dependent with certain known data values. We then move one step further, theoretically proving the bound on the performance of${k}$-anonymity corresponding to each of the four kinds of users’ data through cooperating with the noiseless privacy. We argue that such a theoretical foundation links${k}$-anonymity with noiseless privacy, theoretically proving${k}$-anonymity provides noiseless privacy. Additionally, this paper theoretically explains why${k}$-anonymity is vulnerable to inference attacks using the modified Stein method. Simulations on real check-in dataset from the location-based social network have validated our results. We believe that this paper can bridge the gap between design and evaluation, enabling a designer to construct a more practical${k}$-anonymity technique in real-life scenarios to resist inference attacks.

The energy efficiency and delivery robustness are two critical issues for low duty-cycled wireless sensor networks. The asynchronous receiver-initiated duty-cycling media access control (MAC) protocols have shown their effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this article, we present CD-MAC, an energy-efficient and robust contention-detectable mechanism for addressing the collision-catching problem in receiver-initiated MACs. By exploring the temporal diversity of the acknowledgments, a receiver recognizes the potential senders and subsequently polls individual senders one by one. On that basis, CD-MAC can successfully avoid packet collision even though multiple senders have data packets to transmit to the same receiver. We implement CD-MAC in TinyOS and evaluate its performance on an indoor testbed with single-hop and multi-hop network scenarios. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

Private cars, a class of small motor vehicles usually registered by an individual for personal use, constitute the vast majority of city automobiles and hence significantly affect urban traffic. In particular, private cars tend to stop-and-wait (SAW) in specific regions during daily driving. This SAW behavior produces a spatiotemporal aggregation effect, which facilitates the formation of urban hot zones. In this paper, we investigate the SAW behavior and aggregation effect based on large-scale private car trajectory data. Specifically, motivated by the first law of geography, we leverage the kernel density estimation (KDE) method and extend it to three dimensions to capture the density distribution of the SAW data. Furthermore, according to the inherent relationship between the present SAW density and future SAW aggregation, we propose a 3D-KDE-based prediction model to characterize the dynamic spatiotemporal aggregation effect. In addition, we design a modified inertia weight particle swarm optimization (MIW-PSO) algorithm to determine the optimal weight coefficients and to avoid local optima during SAW prediction. Extensive experiments based on real-world private car SAW data validate the effectiveness of our method for discovering dynamic aggregation effects, therein outperforming the current methods in terms of the Kullback-Leibler (KL) divergence, mean absolute error (MAE), and root mean square error (RMSE). To the best of the authors’ knowledge, our work is the first to utilize private car trajectory data to study the aggregation effect in urban environments, thereby being able to provide new insight into the study of traffic management and the evolution of urban traffic.

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

Due to the popularity of mobile computing and mobile sensing, users' traces can now be readily collected to enhance applications' performance. However, users' location privacy may be disclosed to the untrusted data aggregator that collects users' traces. Cloaking users' traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose W3-tess that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of W3-tess is to credibly imitate the temporal, spatial, and social behavior of users' mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, W3-tess can provide “differential privacy” on location privacy preservation. In addition, compared to the existing work, W3-tess offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of W3-tess.

Due to limited energy supply of Internet of Things (Zhao et al. 2018) (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Since the sleep schedules of nodes are unsynchronized, a sender has to repeatedly send frames to coordinate with its receiver or keep sleeping until the receiver’s wake-up time will come according to receiver’s sleep-wake schedule. In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource for duty-cycled IoT networks. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in opportunistic forwarding, potentially enhancing the overall performance of duty-cycled multi-hop networks. However, since the current channel contention mechanisms mainly focus on collision avoidance, it is too conservative to exploit concurrency. To address this problem, in this article, we propose COF to fully exploit the potential Concurrency for Opportunistic Forwarding in duty-cycled IoT networks. COF achieves concurrent transmission by: (i) measuring conditional link quality under the interference of on-going transmissions, and then (ii) further modeling the benefit of potential concurrency opportunities. According to the expected benefit of concurrency, COF decides whether or not to transmit in concurrent way. COF also adopts concurrency flag and signal features to avoid data collision caused by disordered concurrent transmissions and enhance the accuracy of conditional link quality estimation. COF can be easily integrated into the conventional unsynchronized and duty-cycled protocols. We have implemented COF and evaluated its performance on a 40-node testbed. The results show that COF can effectively exploit potential concurrency in opportunistic forwarding and COF outperforms the state-of-art protocols under diverse traffic load and network density.

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

Accurately estimating the distance between persons with COTS mobile devices can benefit many applications (e.g., group activity analysis, indoor navigation, etc.). In this paper we present WiDE, a deep learning-based system for estimating person-to-person distance based on surrounding WiFi signals. Specifically, WiDE has two phases: offline learning and online prediction. In offline learning phase, we apply a stacked autoencoder (SAE) for pre-training the weights of a deep neural network (DNN), and establish a DNN-based classifier for predicting between-person discretized distance and corridor identity using WiFi signals. During online prediction phase, based on the trained DNN with the SAE and newly uploaded WiFi information, we estimate the corridor identities and the distance between pairwise persons. We validate our system by conducting extensive experiments in a three-floor campus building, and the results show that WiDE achieves the corridor identification accuracy over 98 % and the median ranging error of 0.9m and 3.0m for two persons on the same corridor and on different corridors, respectively, which outperforms the state-of-the-art proximity inferring system [1].

Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users' mobility into D2D communication underlaying cellular networks. Maximizing the total end-to-end data rate involves complex tasks, such as resource allocation and routing. By leveraging on the square tessellation technique, we propose an efficient square-division-based resource allocation scheme. Furthermore, we design a relay-assisted D2D communication protocol that addresses the challenges in enabling multihop D2D communications, namely, spectrum resource allocation, users' mobility, and relay incentive. Through extensive simulations, we show that our relay-assisted D2D communication protocol improves the system throughput up to 55% and the user access rate up to four times in typical scenarios, as compared with state-of-the-art schemes.

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one's location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one's location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users' mobility in continuous LBS queries. With a thorough understanding of the users' mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.

We consider the navigation application of 3D sensor networks that can proactively guide the movement of internal users from potential dangers to a safe exit, where a 3D sensor network serves as a reactive system, instead of a monitoring tool or a medium of data acquisition. Most if not all existing efforts in this line concentrate on 2D cases only, and none of them can be readily applied to 3D sensor networks, posing it a non-trivial challenge to design an effective and light-weight navigation protocol in 3D sensor networks. In this paper, we propose the first location-free, distributed, and scalable navigation protocol that can provide a navigation route for users inside the 3D sensor network with guaranteed safety. More specifically, we formulate the navigation problem as the minimum cumulative exposure problem, and design SNP, a navigation protocol based on the so-called 1-manifold skeleton, which offers a safe path with a near-optimal cumulative exposure to dangers. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.

Indoor localization plays an important role as the basis for a variety of mobile applications, such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and the existing privacy-preserving solutions are algorithm-driven, each designed for specific localization algorithms, which hinders their wide-scale adoption. Furthermore, they mainly focus on users' location privacy, while the LS's data privacy cannot be guaranteed. In this paper, we propose a Privacy-Preserving Paradigm-driven framework for indoor LOCalization (P3-LOC). P3-LOC takes the advantage that most indoor localization systems share a common two-stage localization paradigm: information measurement and location estimation. Based on this, P3-LOC carefully perturbs and cloaks the transmitted data in these two stages and employs specially designed “k -anonymity” and “differential privacy” techniques to achieve the provable privacy preservation. The key advantage is that P3-LOC does not rely on any prior knowledge of the underlying localization algorithms, and it guarantees both users' location privacy and the LS's data privacy. Our extensive experiments from the measured data have validated that P3-LOC provides privacy preservation for general indoor localization techniques. In addition, P3-LOC is comparable with the state-of-the-art algorithm-driven techniques in terms of localization error, computation, and communication overhead.

Temporal regularity in trajectory data is an important basis for traffic management, public service and marketing. Although many efforts have been made to study temporal regularity, yet almost all existing works select time granularity intuitively. User-specified time granularity and other parameters may lead to biased results. Moreover, as the size of datasets grows, the costs of parameters tuning also increases. To solve these problems, we propose the Automatic Multi-granularity Temporal Regularity Detection algorithm (auto-MTRD) for trajectory data. Our approach clusters time series from the trajectory data using automatic parameter selection and generates a temporal regularity tree to indicate multi-granularity temporal regularity. It cannot only avoid the negative effect of human intervention, but also evaluate the relative importance of multiple time granularities at the same time. Two real-life datasets are used to validate the effectiveness of our method.

The wall cracks of damaged building caused by earthquake are important to predict the extent of building damage and to reveal the process of building damage in an earthquake. As a new technology of non-contact measurement method, gound-based LiDAR can provide a new way to extract the quantitative information of wall cracks of earthquake damage buildings. In this article, one of damaged buildings of Beichuan County, which suffered Wenchuan earthquake on May 12th, 2008, were took as an example to study the quantitative extraction method of wall cracks information of earthquake damaged buildings based on gound-based lidar. And, according to the extraction results, the influence of extraction accuracy by point cloud density is analyzed as well.

Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an energy-efficient framework for high-precision multi-target-adaptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

When emergencies happen, navigation services that guide people to exits while keeping them away from emergencies are critical in saving lives. To achieve timely emergency navigation, early and automatic detection of potential dangers, and quick response with safe paths to exits are the core requirements, both of which rely on continuous environment monitoring and reliable data transmission. Wireless sensor networks (WSNs) are a natural choice of the infrastructure to support emergency navigation services, given their relatively easy deployment and affordable costs, and the ability of ubiquitous sensing and communication. Although many efforts have been made to WSN-assisted emergency navigation, almost all existing works neglect to consider the hazard levels of emergencies and the evacuation capabilities of exits. Without considering such aspects, existing navigation approaches may fail to keep people farther away from emergencies of high hazard levels and would probably encounter congestions at exits with lower evacuation capabilities. In this paper, we propose SEND, a situation-aware emergency navigation algorithm, which takes the hazard levels of emergencies and the evacuation capabilities of exits into account and provides the mobile users the safest navigation paths accordingly. We formally model the situation-aware emergency navigation problem and establish a hazard potential field in the network, which is theoretically free of local minima. By guiding users following the descend gradient of the hazard potential field, SEND can thereby achieve guaranteed success of navigation and provide optimal safety. The effectiveness of SEND is validated by both experiments and extensive simulations in 2D and 3D scenarios.

Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, afullresourcesharing scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters).

Device-free localization, which does not require any device attached to the target, is playing a critical role in many applications, such as intrusion detection, elderly monitoring and so on. This paper introduces D-Watch, a device-free system built on the top of low cost commodity-off-the-shelf RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the “bad” multipaths to provide a decimeter-level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately detected by the proposed novel P-MUSIC algorithm. The proposed wireless phase calibration scheme does not interrupt the ongoing data communication and thus reduces the deployment burden. We implement and evaluate D-Watch with extensive experiments in three different environments. D-Watch achieves a median accuracy of 16.5 cm for library, 25.5 cm for laboratory, and 31.2 cm for hall environment, outperforming the state-of-the-art systems. In a table area of 2 $\text{m}\times 2$ m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is also capable of localizing multiple targets which is well known to be challenging in passive localization.

Device-free localization, which does not require any device attached to the target is playing a critical role in many applications such as intrusion detection, elderly monitoring, etc. This paper introduces D-Watch, a device-free system built on top of low cost commodity-off-the-shelf (COTS) RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the "bad" multipaths to provide a decimeter level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival (AoA) information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately captured by the proposed novel P-MUSIC algorithm. The wireless phase calibration scheme proposed does not interrupt the ongoing communication. Real-world experiments demonstrate the effectiveness of D-Watch. In a rich-multipath library environment, D-Watch can localize a human target at a median accuracy of 16.5 cm. In a table area of 2 m×2 m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is capable of localizing multiple targets which is well known to be challenging in passive localization

Offering the services of real-time tracking and arrival time prediction is a common welfare for bus riders and transit agencies, especially in urban environments. On the down side, the traditional GPS-based solutions work poorly in urban areas due to urban canyons, while the location systems based on cellular signal also suffer from inherent limitations. In this paper, we present a powerful tool named Signal Voronoi Diagram (SVD) to partition the radio-frequency (RF) signal space of WiFi Access Points (APs), distributed where a bus travels, into Signal Cells, and then into fine-grained Signal Tiles, tackling the problem of noisy received signal strength (RSS) readings and possible AP dynamics. On top of SVD, we present a novel framework so-called WiLocator, to track and predict the arrival time of an urban bus based on the surrounding WiFi information collected by the commodity off-the-shelf (COTS) smartphones of bus riders, the mobility constraint of a bus and the temporal consistency of travel time of buses on the overlapped road segments. We also show the WiLocator's power of generating an accurate and real-time traffic map with the predicted travel time on each road segment. We implement the prototype of WiLocator and conduct the in-situ experiment to demonstrate its accuracy.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization.

Mobile data traffic has been experiencing a phenomenal rise in the past decade. This ever-increasing data traffic puts significant pressure on the infrastructure of state-of-the-art cellular networks. Recently, device-to-device (D2D) communication that smartly explores local wireless resources has been suggested as a complement of great potential, particularly for the popular proximity-based applications with instant data exchange between nearby users. Significant studies have been conducted on coordinating the D2D and the cellular communication paradigms that share the same licensed spectrum, commonly with an objective of maximizing the aggregated data rate. The new generation of cellular networks, however, have long supported heterogeneous networked applications, which have highly diverse quality-of-service (QoS) specifications. In this paper, we jointly consider resource allocation and power control with heterogeneous QoS requirements from the applications. We closely analyze two representative classes of applications, namely streaming-like and file-sharing-like, and develop optimized solutions to coordinate the cellular and D2D communications with the best resource sharing mode. We further extend our solution to accommodate more general application scenarios and larger system scales. Extensive simulations under realistic configurations demonstrate that our solution enables better resource utilization for heterogeneous applications with less possibility of underprovisioning or overprovisioning.

Auction-style pricing policies can effectively reflect the underlying trends in demand and supply for the cloud resources, and thereby attracted a research interest recently. In particular, a desirable cloud auction design should be (1) online to timely reflect the fluctuation of supply-demand relations, (2) expressive to support the heterogeneous user demands, and (3) truthful to discourage users from cheating behaviors. Meeting these requirements simultaneously is non-trivial, and most existing auction mechanism designs do not directly apply. To meet these goals, this paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous requirement on their desired allocation time, application type, and even how they value among different possible allocations can be flexibly and concisely expressed. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism. To ensure truthfulness with heterogenous and online user demand, the design of COCA is driven by a monotonic payment rule and a utility-maximizing allocation rule. Moreover, our theoretical analysis shows that the worst-case performance of COCA can be well-bounded, and our further discussion shows that COCA performs well when some other important factors in online auction design are taken into consideration. Finally, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [19].

One of the major applications of wireless sensor networks (WSNs) is the navigation service for emergency evacuation, the goal of which is to assist people in escaping from a hazardous region safely and quickly when an emergency occurs. Most existing solutions focus on finding the safest path for each person, while ignoring possible large detours and congestions caused by plenty of people rushing to the exit. In this paper, we present CANS, a C ongestion-Adaptive and small stretch emergency Navigation algorithm with WSNs. Specifically, CANS leverages the idea of level set method to track the evolution of the exit and the boundary of the hazardous area, so that people nearby the hazardous area achieve a mild congestion at the cost of a slight detour, while people distant from the danger avoid unnecessary detours. CANS also considers the situation in the event of emergency dynamics by incorporating a local yet simple status updating scheme. To the best of our knowledge, CANS is the first WSN-assisted emergency navigation algorithm achieving both mild congestion and small stretch, where all operations are in-situ carried out by cyber-physical interactions among people and sensor nodes. CANS does not require location information, nor the reliance on any particular communication model. It is also distributed and scalable to the size of the network with limited storage on each node. Both experiments and simulations validate the effectiveness and efficiency of CANS.

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table GHT is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

Efficient in-network information storage and retrieval is of paramount importance to sensor networks and has attracted a large number of studies while most of them focus on 2D fields. In this paper, we propose novel Reeb graph based information storage and retrieval schemes for 3D sensor networks. The key is to extract the line-like skeleton from the Reeb graph of a network, based on which two distance-sensitive information storage and retrieval schemes are developed: one devoted to shorter retrieval path and the other devoted to more balanced load. Desirably, the proposed algorithms have no reliance on the geographic location or boundary information, and have no constraint on the network shape or communication graph. The extensive simulations also show their efficiency in terms of sensor storage load and retrieval path length.

Scalable routing for large-scale wireless networks needs to find near shortest paths with low state on each node, preferably sublinear with the network size. Two approaches are considered promising toward this goal: compact routing and geometric routing (geo-routing). To date, the two lines of research have been largely independent, perhaps because of the distinct principles they follow. In particular, it remains unclear how they compare to each other in the worst case, despite extensive experimental results showing the superiority of one or another in particular cases. We develop a novel Trap Array topology model that provides a unified framework to uncover the limiting behavior of 10 representative geo-routing algorithms. We present a series of new theoretical results, in comparison to the performance of compact routing as a baseline. In light of their pros and cons, we further design a Compact Geometric Routing (CGR) algorithm that attempts to leverage the benefits of both approaches. Theoretical analysis and simulations show the advantages of the topology model and the algorithm.

In this paper, we propose a highly efficient scheme, SLICE (a scalable and low stretch routing scheme), enabling greedy routing for wireless sensor networks (WSNs) deployed in complex-connected 3-D settings, whose topologies are often theoretically modeled as high genus 3-D WSNs. Compared to previous 3-D greedy embedding techniques, SLICE improves both the robustness and applicability. 1) It achieves a smaller distance distortion and a lower routing stretch with guaranteed delivery. While it follows the basic idea to embed the surface network to a planar topology to enable greedy routing, the embedding method proposed in SLICE is novel. We first slice the surface network to a genus-0 open surface with exactly one boundary. Then, to achieve a lower distance distortion, we purposely propose a variation of the Ricci flow algorithm, by which this open surface is flattened not to a planar annulus, but to a planar convex polygon, resulting in a lower routing stretch. 2) This is the first work, to the best of our knowledge, that enables greedy routing in high genus 3-D WSNs with general topologies. SLICE not only works for high genus 3-D surface WSNs, but also can be easily adapted to more general cases: high genus 3-D surface networks with holes, and high genus 3-D volume networks. For a high genus 3-D surface network with holes, SLICE embeds it to a planar convex polygon with circular holes, where our proposed greedy routing variation can be applied. For a high genus 3-D volume network, SLICE embeds the inner nodes to a height structure attached to the convex polygon, and a variation of greedy routing scheme with guaranteed delivery is proposed in this structure. The effectiveness of SLICE is validated by extensive simulations.

Many applications in wireless sensor networks (WSNs) require that sensor observations in a given monitoring area are aggregated in a serial fashion. This demands a routing path to be constructed traversing all sensors in that area, which is also needed to linearize the network. In this article, we present SURF, a Space filling cURve construction scheme for high genus three-dimensional (3D) surFace WSNs, yielding a traversal path provably aperiodic (that is, any node is covered at most a constant number of times). SURF first utilizes the hop-count distance function to construct the iso-contour in discrete settings, and then it uses the concept of the Reeb graph and the maximum cut set to divide the network into different regions. Finally, it conducts a novel serial traversal scheme, enabling the traversal within and between regions. To the best of our knowledge, SURF is the first high genus 3D surface WSN targeted and pure connectivity-based solution for linearizing the networks. It is fully distributed and highly scalable, requiring a nearly constant storage and communication cost per node in the network. To incorporate adaptive density of the constructed space filling curve, we also design a second algorithm, called SURF + , which makes use of parameterized spiral-like curves to cover the 3D surface and thus can yield a multiresolution SFC adapting to different requirements on travel budget or fusion delay. The application combining both algorithms for in-network data storage and retrieval in high genus 3D surface WSNs is also presented. Extensive simulations on several representative networks demonstrate that both algorithms work well on high genus 3D surface WSNs.

In wireless sensor networks (WSNs), a space filling curve (SFC) refers to a path passing through all nodes in the network, with each node visited at least once. By enforcing a linear order of the sensor nodes through an SFC, many applications in WSNs concerning serial operations on both sensor nodes and sensor data can be performed, with examples including serial data fusion and path planning of mobile nodes. Although a few studies have made efforts to find such SFCs in WSNs, they primarily target 2D planar or 3D surface settings and cannot be directly applied to 3D volumetric WSNs due to considerably more complex geometric features and topology shapes that the 3D volumetric settings introduce. This article presents BLOW-UP, a distributed, scalable, and connectivity-based algorithm to construct an SFC for a 3D volumetric WSN (or alternatively to linearize the 3D volumetric network). The main idea of BLOW-UP is to decompose the given 3D volumetric network into a series of connected and closed layers, and the nodes are traversed layer by layer, incrementally from the innermost to the outermost, yielding an SFC covering the entire network, provably at least once and at most a constant number of times. To the best of our knowledge, BLOW-UP is the first algorithm that realizes linearization in 3D volumetric WSNs. It does not require advance knowledge of location or distance information. It is also scalable with a nearly constant per-node storage cost and message cost. Extensive simulations under various networks demonstrate its effectiveness on nodes’ covered times, coverage rate, and covering speed.

Video-on-demand (VoD) services historically rely on commercial content distribution networks (CDNs) for on-demand capacity provisioning. Content providers gradually prefer a self-managed content infrastructure because of its full control and customization. However, such a dedicated physical infrastructure could be costly in initial capital investment, and complex in management. It has become a promising alternative to host VoD services on pay-as-you-go cloud platforms, on which using dynamic server provisioning to reduce server rental cost is the key objective of content providers. In this paper we address two major challenges to reducing cost: to minimize content reorganization and to tolerate imperfect workload prediction. We first present a practical VoD servicing system design based on a pay-as-you-go cloud. We prove that previous works, focusing exclusively on cost savings, cause significant content reorganization and are vulnerable to imperfect workload prediction. To address such issues, we propose a novel idea called workload absorber, and design a provisioning algorithm called Absorb Window based on the idea. Workload absorbers eliminate the bandwidth wastage and significantly reduce content reorganization. We conduct extensive evaluations with real VoD access traces, and demonstrate the superior scalability of the proposed algorithm by producing highly optimized provisioning in seconds for thousands of servers.

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application - the estimated trajectory is close to the ground truth.

Many applications in wireless sensor networks (WSNs) require that sensor observations in a given monitoring area be aggregated in a serial fashion. This demands a routing path to be constructed traversing all sensors in that area, which is also called to linearize the network. In this paper, we present SURF, a Space filling cURve construction scheme for high genus 3D surFace WSNs, yielding a traversal path provably aperiodic (that is, any node is covered at most a constant number of times). SURF first utilizes the hop-count distance function to construct the iso-contour in discrete settings, then it uses the concept of the Reeb graph and the maximum cut set to divide the network into different regions. Finally it conducts a novel serial traversal scheme, enabling the traversal within and between regions. To the best of our knowledge, SURF is the first high genus 3D surface WSNs targeted and pure connectivity-based solution for linearizing the networks. It is fully distributed and highly scalable, requiring a nearly constant storage and communication cost per node in the network. Extensive simulations on several representative networks demonstrate that SURF works well on high genus 3D surface WSNs.

This poster introduces JRD, a novel device-free localization system which can achieve high accuracy with low cost and little human effort, and is even robust to different scenarios. Unlike the previous Radio Signal Strength (RSS)-based systems which depend on the dense deployment to provide high accuracy, JRD extracts the fine-grained RSS distributions of a single link and presents a voting algorithm based on multi-link to identify the object location accurately while maintaining a low-cost deployment. Furthermore, JRD is flexible to different scenarios by using the transferring technique with less time-consuming and human effort. Experimental results show that JRD can improve the localization accuracy by up to 50% with less cost as compared with the existing RSS approaches.

In sensor networks, skeleton extraction has emerged as an appealing approach to support many applications such as load-balanced routing and location-free segmentation. While significant advances have been made for 2D cases, so far skeleton extraction for 3D sensor networks has not been thoroughly studied. In this paper, we conduct the first work of a unified framework providing a connectivity-based and distributed solution forline-likeskeleton extraction in both 2D and 3D sensor networks. We highlight its practice as: 1) it has linear time/message complexity; 2) it provides reasonable skeleton results when the network has low node density; 3) the obtained skeletons are robust to shape variations, node densities, boundary noise and communication radio model. In addition, to confirm the effectiveness of the line-like skeleton, a 3D routing scheme is derived based on the extracted skeleton, which achieves balanced traffic load, guaranteed delivery, as well as low stretch factor.

Efficient sensor network design requires a full understanding of the geometric environment in which sensor nodes are deployed. In practice, a large-scale sensor network often has a complex and irregular topology, possibly containing obstacles/holes. Convex network partitioning, also known as convex segmentation, is a technique to divide a network into convex regions in which traditional algorithms designed for a simple network geometry can be applied. Existing segmentation algorithms heavily depend on concave node detection, or sink extraction from the median axis/skeleton, resulting in sensitivity of performance to network boundary noise. Furthermore, since they rely on the network's 2-D geometric properties, they do not work for 3-D cases. This paper presents a novel segmentation approach based on Morse function, bringing together the notions of convex components and the Reeb graph of a network. The segmentation is realized by a distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2-D/3-D sensor networks. In CONSEL, several boundary nodes first flood the network to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, generating a coarse segmentation. Next, neighboring regions that are not mutex pairs are merged together. Finally, by ignoring mutex pairs that lead to small concavity, we provide an approximate convex decomposition. CONSEL has a number of advantages over previous solutions: 1) it works for both 2-D and 3-D sensor networks; 2) it uses merely network connectivity information; 3) it guarantees a bound for the generated regions' deviation from convexity. We further propose to integrate network segmentation with existing applications that are oriented to simple network geometry. Extensive simulations show the efficacy of CONSEL in segmenting networks and in improving the performance of two applications: geographic routing and connectivity-based localization.

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. In this paper, we develop a new localization protocol based on approximate convex decomposition (ACDL), with reliance on network connectivity information only. ACDL can calculate the node virtual locations for a large-scale sensor network with a complex shape. We first examine one representative localization algorithm and study the influential factors on the localization accuracy, including the sharpness of the angle at the concave point and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define the concavity according to the angle at a concave point, which reflects the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex section of the network, an improved MDS algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Besides, by slight modification on the third step, we propose a variant of ACDL, denoted by ACDL-Tri, which is fully distributed and scalable while the localization accuracy is still comparable. We finally show the efficiency of ACDL by extensive simulations.

Geometric routing or geo-routing has been shown as a promising approach to scalable routing in sensor networks. Despite its success in 2-D networks, very few designs are available for 3-D networks that can ensure short routes using only small per-node state, without incurring high load imbalance on the nodes. In this paper, we propose a novel addressing and routing scheme, i.e., OnionMap, for 3-D sensor networks that achieve the above goals, using solely connectivity information and at a linear message cost. The key idea is to decompose a 3-D network into a set of connected layers, which are then mapped to a set of concentric sphere structures (similar to an onion). On each sphere, a discrete Ricci flow method is used to assign each node a set of coordinates that permits purely greedy routing within that sphere; across the different spheres, a layer alignment algorithm helps rotate and scale the spheres, to form a coherent global coordinate system that guides global routing. Theoretical analysis and simulation show OnionMap's advantages over state-of-the-art solutions in path stretch, per-node storage, and load balance.

Network Coordinate System (NCS) has drawn much attention over the past years thanks to the increasing number of large-scale distributed systems that require the distance prediction service for each pair of network hosts. The existing schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system based on Robust Principal Component Analysis, RNC, that uses a few local distance measurements to calculate high-precision coordinates without convergence process. To guarantee the non-negativity of predicted distances, we propose Robust Nonnegative Principal Component Analysis (RUN-PACE) which only involves convex optimization, consequently resulting in low computation complexity. Our experimental results indicate that RNC outperforms the state-of-the-art NCS schemes.

Existing algorithms for topology extraction focus on only one topology feature, either skeleton or segmentation, in 2D or 3D sensor networks, most of which requiring complete boundary information. As boundary information is not easily obtained, especially in sparse 3D sensor networks, and extracting these two features separately is very expensive, in this study, we propose to simultaneously extract the line-like skeleton of 2D/3D sensor networks and decompose the network into nice pieces, by constructing the Reeb graph. The Reeb graph has been envisioned as a powerful tool for encoding the topology of an object in computer graphics and computational geometry, where the key is to select the right feature function f. Without using boundary information, we first construct a cut graph, and then regard the distance of a node to the nearest cut as the function f such that the corresponding Reeb graph is pose independent, based on which the skeleton extraction and network decomposition are simultaneously conducted. Some simulation results are presented to show the efficiency of the algorithm.

In-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually delivers a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves.

When a sensor network grows large, or when its topology becomes complex (e.g., containing many holes), network algorithms designed with a smaller or simpler setting in mind may be rendered rather inefficient. We propose to address this problem using a divide and conquer approach: the network is divided into convex pieces by a distributed convex partitioning protocol, using connectivity information only. A convex network partition exhibits some desirable properties that allow traditional algorithms to work to their full advantage. Based on this, we can achieve relatively high performance for an algorithm by combining algorithmic actions within individual partitions. We consider two important applications: virtual-coordinate-based geographic routing and connectivity-based localization. The former benefits from convex partition's friendliness to network embedding, which is crucial to generating accurate virtual coordinates for the nodes, while the latter leverages the fact that shortest paths are largely straight for node pairs within a convex partition. Experimental results show that the convex partition approach can significantly improve the performance of both applications in comparison with state-of-the-art solutions.

Sensor networks are invariably coupled tightly with the geometric environment in which the sensor nodes are deployed. Network boundary is one of the key features that characterize such environments. While significant advances have been made for 2D cases, so far boundary extraction for 3D sensor networks has not been thoroughly studied. We present CABET, a novel Connectivity-Based Boundary Extraction scheme for large-scale 3D sensor networks. To the best of our knowledge, CABET is the first 3D-capable and pure connectivity-based solution for detecting sensor network boundaries. It is fully distributed, and is highly scalable, requiring overall message cost linear with the network size. A highlight of CABET is its non-uniform critical node sampling , called r'-sampling , that selects landmarks to form boundary surfaces with bias toward nodes embodying salient topological features. Simulations show that CABET is able to extract a well-connected boundary in the presence of holes and shape variation, with performance superior to that of some state-of-the-art alternatives. In addition, we show how CABET benefits a range of sensor network applications including 3D skeleton extraction, 3D segmentation, and 3D localization.

In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-folded: 1) We maximize the operating revenue when serving heterogeneous SaaS applications with unpredictable user requests. 2) We minimize the power consumption when processing the user requests. To achieve these objectives, we construct a unified profit-maximizing objective to jointly consider revenue and cost in an economic view. An offline solution to maximize the supreme bound of the objective is first developed, to 1) justify the validity of our theoretical model, and 2) establish a benchmark to examine the effectiveness of other control solutions. As a highlight of our contributions, we take advantage of the Lyapunov optimization techniques to design and analyze an optimal yet practical control framework, which makes online decisions on request admission control, routing, and virtual machine (VMs) scheduling. Our control framework can accommodate a variety of design choices and operational requirements in a datacenter. Specifically, buffering facilities can be introduced to alleviate the bursty admitted requests and to improve the robustness of the system, and a power budget can be enforced to improve the datacenter performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of the cost-effective power-performance tradeoff) and stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.

Reducing transmission redundancy is key to efficient broadcast in wireless networks. A standard approach to achieving this goal is to create a network backbone consisting of a subset of nodes that are responsible for data forwarding, while other nodes act as passive receivers. On top of this, network coding (NC) is often used to further reduce unnecessary transmissions. The main problem with existing backbone and NC combinations is that the backbone construction process is blind of what is needed by NC, thus may produce a structure that limits the power of NC algorithms. To address this problem, we propose Coding Opportunity Aware Backbone (COAB) metrics, which seek to maximize coding opportunities when selecting backbone forwarders. We show that the backbone construction process guided by our metrics leads to significantly increased coding frequency, at the cost of minimal localized information exchange. The highlight of our work is COAB's broad applicability and effectiveness. We integrate the COAB metrics with ten state-of-the-art broadcast algorithms specified in eight publications [1]-[8], and evaluate COAB with a running testbed of 30 MICAz nodes and extensively simulations. The experimental results show that our design outperforms the existing schemes substantially.

As a sensor network grows large, it may become increasingly complex in topology due to its close ties to the surrounding environment. Previous work has shown that proper geometric processing of the network (e.g., boundary detection and localization) can provide very helpful information for applications to optimize their performance. To that end, numerous algorithms have been developed, providing a variety of inspiring solutions, yet exhibiting an ad hoc style in principle and implementation. In this paper we show that the crux of solving many of the problems caused by complex topology is to identify the concave nodes, nodes that are located at concave network corners, where the boundary has an inner angle greater than π. The knowledge of such nodes makes several important tasks, namely geometric embedding, full localization, convex segmentation, and boundary detection, relatively easier or perform significantly better, as confirmed by simulations. These findings suggest that concave nodes can serve as a basic supporting structure for general geometric processing tasks and geometry-related applications in sensor networks.

In sensor networks, skeleton extraction has emerged as an appealing approach to support many applications such as load-balanced routing and location-free segmentation. While significant advances have been made for 2D cases, so far skeleton extraction for 3D sensor networks has not been thoroughly studied. In this paper, we conduct the first work on the skeleton extraction in 3D sensor networks, and propose a unified framework for line-like skeleton extraction in both 2D and 3D sensor networks. Our algorithm has the following three steps: first, each node identifies itself as a skeleton node if the geodesic shortest paths between its nearest boundary nodes (referred to as feature nodes) decompose the boundary of the network into more than one connected component; second, each skeleton node is assigned a monotonically increasing importance measure according to the maximum Lebesgue measure of the connected components of the boundary such that the identified skeleton nodes are self-connected; and finally, the skeleton is pruned based on the proposed metric branch similarity. The proposed algorithm is connectivity-based, distributed and of low complexity. Extensive simulations show that it is robust to shape variations and boundary noise.

Abundant field experiments have showed that the super low frequency (SLF) electromagnetic detector is sensitive to Coalbed Methane. The signal curves collected by the SLF electromagnetic detector show high amplitude anomalies in the Coalbed Methane enrichment areas. Based on this finding, we choose the Qinshui basin as study area, and take advantage of the field data to seeking the coupleing relationship between the SLF electromagnetic data and Coalbed Methane gas content. The results show that the passive super-low frequency electromagnetic detection technology can effectively monitor the longer time span dynamic of Coalbed Methane gas content.

Reducing transmission redundancy is key to the efficiency of wireless network broadcast. A standard technique to achieve this is to create a network backbone consisting of a subset of nodes that are responsible for data forwarding, while other nodes act as passive receivers. On top of this, network coding (NC) is often used to further reduce unnecessary transmissions. The main problem with this backbone+NC approach is that the backbone construction process is blind of what is needed by NC, thus may produce a structure with little benefit to the NC algorithms. To address this problem, we propose a Coding Opportunity Aware Backbone (COAB) construction scheme, which seeks to maximally exploit coding opportunities when selecting backbone forwarders. We show that the better informed backbone construction process leads to significantly increased coding frequency, at minimal cost of localized information exchange. The highlight of our work is COAB's broad applicability and effectiveness. We integrate COAB with ten state-of-the-art broadcast algorithms, specified in eight publications [1]-[8], and evaluate it with prototype implementations with 30 MICAz nodes. The experimental results show that our design outperforms the existing schemes substantially.

In this paper, we put forward a novel scalable and distributed routing algorithm, called SINUS, for sensor networks deployed on the surface of complex-connected 3D settings such as tunnels, whose topologies are often theoretically modeled as high genus 3D surfaces. SINUS is carried out by first slicing the genus-n surface along a maximum cut set based on Morse theory and Reeb graph, in order to form a genus-0 surface with 2n boundaries. Then, it groups these 2n boundaries into two groups each of which is next connected together. By doing so, a genus-0 surface with exactly two boundaries emerges, which can be flattened into a strip, using the Ricci flow algorithm and next mapped to a planar annulus by Möbius Transform. By assigning nodes virtual coordinates on the planar annulus, SINUS finally realizes a variation of greedy routing to enable individual nodes to make local muting decisions. Our simulation results show that SINUS can achieve low-stretch routing with guaranteed delivery, as well as balanced traffic load.

The paradigm of cloud computing has spontaneously prompted a wide interest in market-based resource allocation mechanisms by which a cloud provider aims at efficiently allocating cloud resources among potential users. Among these mechanisms, auction-style pricing policies, as they can effectively reflect the underlying trends in demand and supply for the computing resources, have attracted a research interest recently. This paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Our framework desirably supports a variety of design requirements, including (1) dynamic design for timely reflecting fluctuation of supply-demand relations, (2) joint design for supporting the heterogeneous user demands, and (3) truthful design for discouraging bidders from cheating behaviors. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous demands are generalized to regulated and consistent forms. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism based on two proposed guidelines. Our theoretical analysis shows that the worst-case performance of COCA can be well-bounded. Further, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [11].

In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-fold: (1) We maximize the operating profit when serving heterogeneous SaaS applications with unpredictable user requests, and (2) we minimize the power consumption when processing user requests. To achieve these objectives, we take advantage of Lyapunov Optimization techniques to design and analyze an optimal control framework to make online decisions on request admission control, routing, and virtual machine (VMs) scheduling. In particular, our control framework can be flexibly extended to incorporate various design choices and practical requirements of a data-center in the cloud, such as enforcing a certain power budget for improving the performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of a cost-effective power-performance tradeoff) and system stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.

In sensor networks community, the skeleton (or medial axis), as an important infrastructure which can correctly capture the topological and geometrical features of the underlying network, has been widely used for facilitating routing, navigation, segmentation, etc. Even though there are a handful of skeleton extraction solutions, the measurement of the goodness of the derived skeleton is often application-oriented, and there is no quantitative metric for this task. In this paper, we study the problem of skeleton extraction and conduct the first work on quantitative evaluation of skeleton in sensor networks. Different from traditional schemes which assume complete or incomplete boundaries, the proposed skeleton extraction algorithm is based on mere connectivity information, without reliance on any boundary information. More specifically, for each node we compute its variability factor based on the neighborhood sizes of the node and its neighbors, which can reflect how central a sensor node is to the network, and a sensor node identifies itself as a skeleton node if its variability factor is locally maximal. Next, we present a light-weight scheme to connect these skeleton nodes. Finally, we proposed a metric, named visibility coefficient, to quantitatively evaluate the derived skeleton.

Scalable routing for large-scale wireless networks needs to find near shortest paths with low state on each node, preferably sub-linear with the network size. Two approaches are considered promising toward this goal: compact routing and geometric routing (geo-routing). To date the two lines of research have been largely independent, perhaps because of the distinct principles they follow. In particular, it remains unclear how they compare with each other in the worst case, despite extensive experimental results showing the superiority of one or another in particular cases. We develop a novel Trap Array topology model that provides a unified framework to uncover the limiting behavior of ten representative geo-routing algorithms. We present a series of new theoretical results, in comparison with the performance of compact routing as a baseline. In light of their pros and cons, we further design a Compact Geometric Routing (CGR) algorithm that attempts to leverage the benefits of both approaches. Theoretic analysis and simulations show the advantages of the topology model and the algorithm.

A connectivity-based and anchor-free three-dimensional localization (CATL) scheme is presented for large-scale sensor networks with concave regions. It distinguishes itself from previous work with a combination of three features: (1) it works for networks in both 2D and 3D spaces, possibly containing holes or concave regions; (2) it is anchor-free and uses only connectivity information to faithfully recover the original network topology, up to scaling and rotation; (3) it does not depend on the knowledge of network boundaries, which suits it well to situations where boundaries are difficult to identify. The key idea of CATL is to discover the notch nodes , where shortest paths bend and hop-count-based distance starts to significantly deviate from the true Euclidean distance. An iterative protocol is developed that uses a notch-avoiding multilateration mechanism to localize the network. Simulations show that CATL achieves accurate localization results with a moderate per-node message cost.

We study the problem of skeleton extraction for large-scale sensor networks with reliance purely on connectivity information. Existing efforts in this line highly depend on the boundary detection algorithms, which are used to extract accurate boundary nodes. One challenge is that in practical this could limit the applicability of the boundary detection algorithms. For instance, in low node density networks where boundary detection algorithms do not work well, the extracted boundary nodes are often incomplete. This paper brings a new view to skeleton extraction from a distance transform perspective, bridging the distance transform of the network and the incomplete boundaries. As such, we propose a distributed and scalable algorithm for skeleton extraction, called DIST, based on DIStance Transform, while incurring low communication overhead. The proposed algorithm does not require that the boundaries are complete or accurate, which makes the proposed algorithm more practical in applications. First, we compute the distance transform of the network. Specifically, the distance (hop count) of each node to the boundaries of a sensor network is estimated. The node map consisting of the distance values is considered as the distance transform (the distance map). The distance map is then used to identify skeleton nodes. Next, skeleton arcs are generated by controlled flooding within the identified skeleton nodes, thereby connecting these skeleton arcs, to extract a coarse skeleton. Finally, we refine the coarse skeleton by building shortest path trees followed by a prune phase. The obtained skeleton is robust to boundary noise or shape variations. Besides, we present two specific applications that benefit from the extracted skeleton: identifying complete boundaries and shape segmentation. First, with the extracted skeleton using DIST, we propose to identify more boundary nodes to form a meaningful boundary curve. Second, the utilization of the derived skeleton to segment the network into approximately convex pieces has been shown to be effective.

Data gathering in sensor networks is required to be efficient, adaptable and robust. Recently, compressive sensing (CS) based data gathering shows promise in meeting these requirements. Existing CS-based data gathering solutions require that a transform that best sparsifies the sensor readings should be used in order to reduce the amount of data traffic in the network as much as possible. As a result, it is very likely that different transforms have to be determined for varied sensor networks, which seriously affects the adaptability of CS-based schemes. In addition, the existing schemes result in significant errors when the sampling rate of sensor data is low (equivalent to the case of high packet loss rate) because CS inherently requires that the number of measurements should exceed a certain threshold. This paper presents STCDG, an efficient data gathering scheme based on matrix completion. STCDG takes advantage of the low-rank feature instead of sparsity, thereby avoiding the problem of having to be customized for specific sensor networks. Besides, we exploit the presence of the short-term stability feature in sensor data, which further narrows down the set of feasible readings and reduces the recovery errors significantly. Furthermore, STCDG avoids the optimization problem involving empty columns by first removing the empty columns and only recovering the non-empty columns, then filling the empty columns using an optimization technique based on temporal stability. Our experimental results indicate that STCDG outperforms the state-of-the-art data gathering algorithms in terms of recovery error, power consumption, lifespan, and network capacity.

Wireless sensor networks are expected to be used in many different applications such as disaster relief, environmental control, and intelligent buildings. In this paper, we focus on a sensor network that collects environment data from all sensor nodes periodically. To gather the sensing data quickly and reliably, the scheduling algorithm should be able to coordinate the data transmissions in the network and react to node/link failures effectively. In this paper, we present an innovative scheduling algorithm, Fault-Tolerant Scheduling for data collection (FTS), that leads to short data collection time and high fault tolerance. Our experimental results show that FTS outperforms the DCSB algorithm and exhibits strong fault-tolerant capabilities.

MAP is a connectivity-based routing protocol aimed at improving the load balance performance of traditional geographical routing methods. It attempts to find parallel routing paths by taking advantage of the concept of skeleton in the continuous domain. However, MAP suffers seriously from overloading the sensor nodes that are close to the skeleton. In this paper, we propose a contour-based routing protocol, CAR, that does not require geographical information, produces short routing paths, and achieves outstanding load balancing. Our experimental results show that CAR outperforms MAP in terms of both load balancing and routing path length.

This paper proposes a novel approach, named DIST, to skeleton extraction from incomplete boundaries using the idea of {\em distance transform}, a concept in the computer graphics area. The main contribution is a distributed and low-cost algorithm that produces accurate network skeletons without requiring that the boundaries be complete or tight. The algorithm first establishes the network's distance transform -- the hop distance of each node to the network's boundaries. Based on this, some {\em critical skeleton nodes} are identified. Next, a set of {\em skeleton arcs} are generated by controlled flooding, connecting these skeleton arcs then gives us a coarse skeleton. The algorithm finally refines the coarse skeleton by building shortest path trees, followed by a prune phase. The obtained skeletons are robust to boundary noise and shape variations.

In sensor networks, skeleton (also known as medial axis) extraction is recognized as an appealing approach to support many applications such as load-balanced routing and location free segmentation. Existing solutions in the literature rely heavily on the identified boundaries, which puts limitations on the applicability of the skeleton extraction algorithm. In this paper, we conduct the first work of a connectivity-based and boundary free skeleton extraction scheme, in sensor networks. In detail, we propose a simple, distributed and scalable algorithm that correctly identifies a few skeleton nodes and connects them into a meaningful representation of the network, without reliance on any constraint on communication radio model or boundary information. The key idea of our algorithm is to exploit the necessary (but not sufficient) condition of skeleton points: the intersection area of the disk centered at a skeleton point x should be the largest one as compared to other points on the chord generated by x, where the chord is referred to as the line segment connecting x and the tangent point in the boundary. To that end, we present the concept of \epsilon-centrality of a point, quantitatively measuring how "central" a point is. Accordingly, a skeleton point should have the largest value of \epsilon-centrality as compared to other points on the chord generated by this point. Our simulation results show that the proposed algorithm works well even for networks with low node density or skewed nodal distribution, etc. In addition, we obtain two by-products, the boundaries and the segmentation result of the network.

Remote sensing technology is considered a fast and effective method to prospect ore. Now, this method is used in Gejiu tin deposit of YunNan in order to extract more accurate mineralization abnormal information. In this study, first through the band math method and principal component analysis method, the mineralization alternation can be extracted in ETM data. Then using ASTER data the limonitization, the chloritization and the dolomitization are extracted by the spectral angle method. At last, the trace elements of the vegetation are statistically analyzed and the vegetation mineralization alteration information is extracted by two different methods in ASTER data. The result shows that the alternation information distributions are consistent in the east-south study area and match with the field exploration. Consequently the extracted results are effective.

This work focuses on using remote sensing and geographic information systems (GIS) to identify promising geothermal areas in southwest Tengchong, China. Thematic information, including surface temperature, urban area, and surface slope are derived from Enhanced Thematic Mapper Plus (ETM+) data and digital elevation models (DEM). GIS is applied as a decision-support tool to integrate the thematic information for suitability analysis. The results indicate that combining remote sensing with GIS is an overall effective and accurate method for geothermal exploration. Three developed geothermal fields are successfully extracted in Tengchong, and promising areas are found to the north of study area and warrant further exploration.

A cardinal prerequisite for the system design of a sensor network, is to understand the geometric environment where sensor nodes are deployed. The global topology of a large-scale sensor network is often complex and irregular, possibly containing obstacles/holes. A convex network partition, so-called segmentation, is to divide a network into convex regions, such that traditional algorithms designed for a simple geometric region can be applied. Existing segmentation algorithms highly depend on concave node detection on the boundary or sink extraction on the medial axis, thus leading to quite sensitive performance to the boundary noise. More severely, since they exploit the network's 2D geometric properties, either explicitly or implicitly, so far there has been no general 3D segmentation solution. In this paper, we bring a new view to segmentation from a Morse function perspective, bridging the convex regions and the Reeb graph of a network. Accordingly, we propose a novel distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2D/3D sensor networks. Specifically, several boundary nodes first perform flooding to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, thereby generating the coarse segmentation. Next the neighbor regions which are not mutex pair are merged together. Finally, by ignoring mutex pairs which leads to small concavity, we provide the constraints for approximately convex decomposition. CONSEL is more desirable compared with previous studies: (1) it works for both 2D and 3D sensor networks; (2) it only relies on network connectivity information; (3) it guarantees a bound for the regions' deviation from convexity. Extensive simulations show that CONSEL works well in the presence of holes and shape variation, always yielding appropriate segmentation results.

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define concavity according to the angle at a concave point, which can reflect the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex subsection of the network, an improved Multi-Dimensional Scaling (MDS) algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Our simulation on several representative networks demonstrated that ACDL has localization error that is 60%-90% smaller as compared with the typical MDS-MAP algorithm and 20%-30% smaller as compared to a recent state-of-the-art localization algorithm CATL.

Youtube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Facebook-like online social networks (OSNs), have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSSes are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users' viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends are more likely to have common interests and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for VSS video recommendation. An auto-encoder model is developed to learn the social similarity of different videos in terms of their sharing in OSN. We therefore propose a similarity-based strategy to enhance recommended video lists for VSSes. Evaluation results demonstrate that this strategy can remarkably improve the precision in VSSes, as compared to state-of-the-art strategies without social information.

Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective.

This paper introduces several novel load-balancing algorithms for distributing Session Initiation Protocol (SIP) requests to a cluster of SIP servers. Our load balancer improves both throughput and response time versus a single node while exposing a single interface to external clients. We present the design, implementation, and evaluation of our system using a cluster of Intel x86 machines running Linux. We compare our algorithms to several well-known approaches and present scalability results for up to 10 nodes. Our best algorithm, Transaction Least-Work-Left (TLWL), achieves its performance by integrating several features: knowledge of the SIP protocol, dynamic estimates of back-end server load, distinguishing transactions from calls, recognizing variability in call length, and exploiting differences in processing costs for different SIP transactions. By combining these features, our algorithm provides finer-grained load balancing than standard approaches, resulting in throughput improvements of up to 24% and response-time improvements of up to two orders of magnitude. We present a detailed analysis of occupancy to show how our algorithms significantly reduce response time.

Top-k query has long been a crucial problem in multiple fields of computer science, such as data processing and information retrieval. In emerging cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, users may request different sets of information, with different priorities and at different times. Thus, top-k search should not only be multidimensional, but also be across time domain. From the system's perspective, data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resource constrained and system efficiency is of paramount importance. In this paper, we develop a framework that can effectively satisfy demands from the two aspects. The sensor network maintains an efficient dominant graph data structure for data readings. A simple top-k extraction algorithm is used for user query processing and two schemes are proposed to further reduce communication cost. Our methods can be used for top-k query with any linear convex query function. The framework is adaptive enough to incorporate some advanced features; for example, we show how approximate queries and data aging can be applied. To the best of our knowledge, this is the first work for continuous multidimensional top-k query processing in sensor networks. Simulation results show that our schemes can reduce the total communication cost by up to 90 percent, compared with a centralized scheme or a straightforward extension from previous top-k algorithm on 1D sensor data.

In unstructured peer-to-peer networks, the average response latency and traffic cost of a query are two main performance metrics. Controlled-flooding resource query algorithms are widely used in unstructured networks such as peer-to-peer networks. In this paper, we propose a novel algorithm named Selective Dynamic Query (SDQ). Based on mathematical programming, SDQ calculates the optimal combination of an integer TTL value and a set of neighbors to control the scope of the next query. Our results demonstrate that SDQ provides finer grained control than other algorithms: its response latency is close to the well-known minimum one via Expanding Ring; in the mean time, its traffic cost is also close to the minimum. To our best knowledge, this is the first work capable of achieving a best trade-off between response latency and traffic cost.

This paper presents SHARP: a scalable framework for Dynamic Joint Replica Placement and Request Routing (DJRPRR) scheduling in content delivery networks. After grouping similar proxies and modeling them by a single section, we propose a hierarchical scheduling framework to greatly reduce the dimensions of the mathematical formulation. In every phase the obtained shaped formulation has an easy-solvable form and the complete optimization process is highly scalable. To verify the scalability and effectiveness of our approach, SHARP is evaluated by comprehensive experiment settings which are derived from realistic data/topology of an operational commercial CDN.

Energy efficient broadcasting is of paramount importance for many broadcast applications in wireless ad hoc networks. With respects network coding, it has been proved that the energy gain is upper bounded by 3. However, the coding opportunity is often highly dependent on the established routing paths, resulting in that a lot of coding opportunities could be lost in practice. By combining network coding with the Connected Dominating Set (CDS)-based broadcasting, we take full use of network coding. The intuition behind our algorithm is to intersect information flows at nodes in CDS to increase the coding opportunities. We propose a novel scheme named NCAB, a Network Coding Aware based Broadcast routing mechanism, integrating the network coding and the dynamic implementation of connected dominating set. Our experimental results show that NCAB provides up to 169% gains compared to flooding, and 41% gains compared to CDS-based broadcasting.

Abstract-Underwater Wireless Sensor Networks (UWSNs) can enable a broad range of applications; data aggregation is a fundamental task in such multi-hop wireless sensor networks. To the best of our knowledge, none of existing research works have addressed the interference-free data aggregation scheduling problem in UWSNs. In this paper, we formally define the data aggregation model in UWSNs. We propose a realistic aggregation scheduling scheme together with its theoretical latency bound Rh(C(Δ - 1) + D), where Rhand Δ are the hop radius and the max degree of the network respectively while C and D is a constant. Specifically, we introduce the concept of Virtual Slot to efficiently exploit multiplexing opportunities of time domain. Compared with naively adapted terrestrial algorithms, the evaluation results show that our proposed algorithm achieve far better performance especially when the packet size is small or the node density is high.

User-Generated Content (UGC) is overwhelming the Internet with its interactivity and various contents. However, traditional UGC still have constrains on videos' length and size, which block out a wide variety of potential popular contents. In this paper, we present the first experimental measurements and analysis of an Unconstrained User-Generated Content (UUGC) system - a test site (so-called "T" site in this paper) of a leading VOD service provider in China. This test site is a video-sharing portal just like traditional UGC, while its contents are not constrained by either duration or size. As an UUGC system, its most distinguishing characteristics are the various types of contents uploaded (movie, TV episode, TV show, music, documentary, sports, etc.) and the wide range of uploaders, which make it an interesting case study. By matching relative key words in video's index, we classify the contents into several basic types and analyze the statistics of three major types - movie, TV episode and TV show (labeled MVI, TV-E and TV-S). For further study of various contents, we demonstrate the patterns of flash crowd triggering of MVI, TV-E and TV-S with several typical cases. To find out the viewers' consumption pattern, we investigate daily & weekly cycles, as well as grouping the videos by age and exhibiting the popularity evolution. By means of curve fitting with multiple known distributions to video view traces, we show that power law with exponential cutoff best fits the videos' popularity distribution for this UUGC system.

Soil spectral reflectance is affected by soil physicochemical characteristics and the physical basis of the soil remote sensing. Generally, the impact factors of the soil spectral features include water content, organic matter content, iron oxides content, physical composition and the parent material. In this study, a portable ASD FieldSpec Pro FR was used to collect the spectra of soil samples. The sensitive bands were selected by analyzing the relationship between nutrients and soil spectral features. Then the inversion models of soil nitrogen and organic were established by linear regression separately. The result showed that the content of soil nitrogen and soil organic can be well retrieved from remote sensing.

There are two buffer representation methods at present: vector buffer and raster buffer. When dealing with a large geographic dataset, both of them are unsatisfactory either in time efficiency or in space efficiency. This paper presents a new buffer representation method-equational buffer, which is not represented by physical entities like vector buffer and raster buffer but by a mathematical equation. Such a representation method frees equational buffer from complicated geometric calculations and makes it suitable for dealing with massive geographic data and dynamic geographic data. We also discuss the characteristics and potential application of equational buffer.

Top-k query has long been an important topic in many fields of computer science. Efficient implementation of the top-k queries is the key for information searching. With the new frontier such as the cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, different users may request different set of information, with different priorities and at different times. Thus, the top-k search not only should be multi-dimensional, but also across time domain. From the system's perspective, the data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resource-constrained and system efficiency is of paramount importance. In this paper, we develop a framework that can effectively satisfy the two ends. The sensor network maintains an efficient dominant graph data structure for data readings. A simple top-k extraction algorithm is used for the user query processing and two schemes are proposed to further reduce communication cost. Our proposed methods can be used for top-k query with any linear convex query function. To the best of our knowledge, this is the first work for continuous multi-dimensional top-k query processing in sensor networks; and our simulation results show that our schemes can reduce the total communication cost by up to 90%, compared with the centralized scheme or a straightforward extension from previous top-k algorithm on one-dimensional sensor data.

Sensor networks are invariably coupled tightly with the geometric environment in which the sensor nodes are deployed. Network boundary is one of the key features that characterize such environments. While significant advances have been made for 2D cases, so far boundary extraction for 3D sensor networks has not been thoroughly studied. We present CABET, a novel Connectivity-bAsed Boundary Extraction scheme for large-scale Three-dimensional sensor networks. To the best of our knowledge, CABET is the first 3D-capable and pure connectivity-based solution for detecting sensor network boundaries. It is fully distributed. A highlight of CABET is its non-uniform critical node sampling, called r r'-sampling, that selects landmarks to form boundary surfaces with bias toward nodes embodying salient topological features. Simulations show that CABET is able to extract a well-connected boundary in the presence of holes and shape variation, with performance superior to that of some state-of-the-art alternatives. In addition, we show how CABET benefits a range of sensor network applications including 3D skeleton extraction and 3D segmentation.

The problem of streaming packetized media has been intensively studied for a long time. In this paper, we revisit this problem in the high-speed railway context, where passengers encode and upload videos through increasingly powerful smartphones. The challenge is highlighted by the fast changing channel conditions in high-speed trains and the limited battery of cell phones. Inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient and rate-distortion optimized approach for video streaming. Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario. It then smartly adjusts the GOF budget, schedules the video transmission to achieve graceful rate-distortion performance and yet conserves the energy consumption. Performance evaluation based on simulated railway scenarios and H.264 video traces demonstrates the effectiveness of our solution and its superiority as compared to existing solutions.

Dynamic application placement for clustered web applications heavily influences system performance and quality of user experience. Existing approaches claim that they strive to maximize the throughput, keep resource utilization balanced across servers, and minimize the start/stop cost of application instances. However, they fail to minimize the worst case of server utilization; the load balancing performance is not optimal. What's more, some applications need to communicate with each other, which we called dependent applications; the network cost of them also should be taken into consideration. In this paper, we investigate how to minimize the resource utilization of servers in the worst case, aiming at improving load balancing among clustered servers. Our contribution is two-fold. First we propose and define a new optimization objectives: limiting the worst case of each individual server's utilization, formulated by a min-max problem. A novel framework based on binary search is proposed to detect an optimal load balancing solution. Second, we define system cost as the weighted combination of both placement change and inter-application communication cost. By maximizing the number of instances of dependent applications that reside in the same set of servers, the basic load-shifting and placement-change procedures are enhanced to minimize whole system cost. Extensive experiments have been conducted and effectively demonstrate that: 1) the proposed framework achieves a good allocation for clustered web applications. In other words, requests are evenly allocated among servers, and throughput is still maximized; 2) the total system cost maintains at a low level; 3) our algorithm has the capacity of approximating an optimal solution within polynomial time and is promising for practical implementation in real deployments.

For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult-it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques.

(A short version of this paper appears in IEEE INFOCOM 2009: http://www.research.ibm.com/people/i/iyengar/INFOCOM2009-kanon.pdf.) Peer-to-peer VoIP (voice over IP) networks, exemplified by Skype, are becoming increasingly popular due to their significant cost advantage and richer call forwarding features than traditional public switched telephone networks. One of the most important features of a VoIP network is privacy (for VoIP clients). Unfortunately, most peer-to-peer VoIP networks neither provide personalization nor guarantee a quantifiable privacy level. In this paper, we propose novel flow analysis attacks that demonstrate the vulnerabilities of peer-to-peer VoIP networks to privacy attacks. We then address two important challenges in designing privacy-aware VoIP networks: Can we provide personalized privacy guarantees for VoIP clients that allow them to select privacy requirements on a per-call basis? How to design VoIP protocols to support customizable privacy guarantee? This paper proposes practical solutions to address these challenges using a quantifiable k-anonymity metric and a privacy-aware VoIP route setup and route maintenance protocols. We present detailed experimental evaluation that demonstrates the performance and scalability of our protocol, while meeting customizable privacy guarantees.

Data collection is of paramount importance in many applications of wireless sensor networks (WSNs). Especially, to accommodate ever increasing demands of signal source coding applications, the capacity of processing multi-user data query is crucial in WSNs where the efficiency is one key consideration. To that end, this paper presents EDCA: an Efficient Data Collection Approach for data query in WSNs, which exploits recent matrix completion techniques. Specifically, for the efficiency of energy consumption, we randomly select a part of nodes from the sensor network to sample at each time instance and directly forward the data to the sink. Then, to recover the data precisely, we shift the rank minimization problem, which is NP-hard, to a convex optimization one. Compared with the centralized scheme, energy consumption using EDCA is significantly reduced due to lower sampling rate and fewer packets to transmit. The experimental results demonstrate that EDCA significantly outperforms the existing naive method in terms of energy consumption and the introduced errors are quite trivial.

Routing is one of the most challenging open problems in pocket-switched-networks (PSN). In this paper, we propose a novel co-route media content forwarding scheme (CRMF), in which new contact opportunities are created for occasionally disconnected mobile users. Our study is inspired by two observations: one is that many people tend to make regular journeys to the same place, so their trajectories show a high degree of temporal and spatial regularity. The other is that the number of repeated journeys for an individual commuter is greater than that of the repeated contacts with another commuter who possess similar seasonal movement patterns. Our main contributions include: we properly install store-and-forward routers based on vehicle mobility patterns and human regular movement behaviors; we also propose a router-centric prediction scheme that collects passenger historical trajectory information to determine the delivery scheme. The simulation results demonstrate that this approach improves delivery ratio and also reduces the delivery latency compared to memory (history)-less delivery scheme.

Leaf Area Index (LAI) is a very important parameter in the area of vegetation quantitative remote sensing. Large range of LAI can reflect the change of eco-system. This article has discussed whether the crop type is a factor to impact the leaf area index retrieval. We choose four types of crops in our research and Hyperspectral Data and leaf area index of these crops were measured. Then the LAI retrieval models were established, which demonstrate the relationships between SVI and LAI. Finally the conclusion can be made that the type of crop is a factor impacting the LAI retrieval. For different crops, the best models are not the same. But the little difference of R2can be omitted. The SR is the best spectral vegetation index for LAI retrieval.

Forest regulating temperature ecosystem services was mainly that forest had the cooling effect in summer and warming effect in winter. Land surface temperature (LST) and normalized difference vegetation index (NDVI) were important parameter of forest regulating the temperature. This paper studied forest temperature changes with time using the time-series of LST and NDVI. Five typical samples were respectively selected from urban areas, farm, coniferous forest, broadleaf forest. The conclusions were followed: farm regulating temperature was less than forest; the lowest value of NDVI of the vegetation occurred at Feb., which lagged the time of the minimum LST occurred at January; the LST of conifer sample point had obvious low ebb, and the low ebb of broad-leaved sample point was not obvious; LST and NDVI were the negative correlation in the day, this was, the better of the vegetation cover, the lower of LST.

On September 6, 2008, the satellite HJ-1B was launched into a sun-synchronous, near-polar orbit. In order to determine temporal changes of the absolute radiometric calibration of the HJ-1B satellite in flight, a program was carried out at DunHuang calibration field, Gansu province of China, from August 19 to 30, 2009. In this work, reflectance -based calibration method was employed to simulate the absolute calibration coefficients of the HJ-1B CCD sensors. Then the cotton field, cement court, water pool and test site was selected to validate the calibration coefficient. The validation results indicated that there had a good agreement between the imaged-based reflectance and the ground measurement of the type of cement court and test site. On the other hand, there had a disagreement of the type of cotton field and water because of the effect of mixed pixel.

This study wants to demonstrate that two different polarimetric target decomposition methods can improve SAR data accuracy for estimating the parameters of bare soil surface. To achieve this goal, two experiments are conducted: (1) both Freeman and Cloude decomposition methods are performed on JPL/AIRSAR L-band fully polarimetric data; and (2) Advanced Integral Equation Model (AIEM) is used to simulate backscatting coefficients. The root mean square errors (RMSEs) of σ0hh, σ0vvbetween original data and AIEM simulated data are 1.96 and 1.25 dB. However, if Cloude method is used to decompose original data, the RMSEs will be reduced to 1.45 and 1.14dB, respectively; for Freeman method, the RMSEs are 1.64 and 1.35 dB. Therefore, polarimetric target decomposition compensation, especially Cloude method, can help to improve the accuracy of SAR data for estimating the parameters of bare soil surface.

This paper presents a Connectivity-based and Anchor-free Three-dimensional Localization (CATL) scheme for large-scale sensor networks with concave regions. It distinguishes itself from previous work with a combination of three features: (1) it works for networks in both 2D and 3D spaces, possibly containing holes or concave regions; (2) it is anchor-free, and uses only connectivity information to faithfully recover the original network topology, up to scaling and rotation; (3) it does not depend on the knowledge of network boundaries, which suits it well to situations where boundaries are difficult to identify. The key idea of CATL is to discover the notch nodes, where shortest paths bend and hop-count-based distance starts to significantly deviate from the true Euclidean distance. An iterative protocol is developed that uses a em notch-avoiding multilateration mechanism to localize the network. Simulations show that CATL achieves accurate localization results with a moderate per-node message cost.

Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in wireless sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, are not immediately applicable for the discrete and distributed wireless sensor networks. In this paper, we present a novel Connectivity-bAsed Skeleton Extraction (CASE) algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, CASE is distributed as no centralized operation is required, and is scalable as both its time complexity and its message complexity are linearly proportional to the network size. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. We believe that CASE has broad applications and present a skeleton-assisted segmentation algorithm as an example. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms the state-of-the-art algorithms.

Existing terrestrial synchronization protocols including RBS, FTSP, TPSN, LTS and TSHL have already achieved high precision in radio networks, but none of them perform well in high latency networks like acoustic sensor networks. In this paper, we present tri-message: a lightweight time synchronization protocol for high latency and resource-constrained networks. As its name suggests, only three message exchanges are required in one synchronization process. Meanwhile, tri-message utilizes very simple mathematical operations to calculate the clock skew and offset. Specially, tri-message is feasible for many extremely long latency applications such as space exploration because it has an increasing synchronization precision with the increasement of distance.

A comparative research of snow cover is conducted, which is aimed to investigate the effect of spatial scale variation and the differences of different spatial resolution remote sensing images, by using moderate-resolution imaging spectroradiometer (MODIS) data and the satellite-B (HJ-1B) data of the small satellite constellation for environment and disaster monitoring and forecasting of China. Results show that: (1) the scaling-change within certain limits has little impact on snow cover mapping; (2) high spatial resolution image gives a better description of the detailed information of snow cover area; (3) the differences of snow cover between low spatial resolution and high resolution images are due to the different snow cover type.

It is found that the abandoned mine goaves, because of the different fillers, exist in below types: goaves filled with air, filled with water, and congested by falling coal bed roof after investigation. According to the goaf types and characteristics of different fillings in goaves by super low frequency (SLF) electromagnetic exploring, the SLF electromagnetic exploring curves may be classified into three major categories: the characteristic curve of air filled goaf, the characteristic curve of water filled goaf, and the characteristic curve of coal bed roof backfilled goaf. Based on the analysis above, we established the recognition characterization of passive super low frequency electromagnetic exploring spectrum curves with respect to goaves, and accordingly obtained the information of goaves.

This paper introduces several novel load balancing algorithms for distributing session initiation protocol (SIP) requests to a cluster of SIP servers. Our load balancer improves both throughput and response time versus a single node, while exposing a single interface to external clients. We present the design, implementation and evaluation of our system using a cluster of Intel x86 machines running Linux. We compare our algorithms with several well-known approaches and present scalability results for up to 10 nodes. Our best algorithm, transaction least-work-left (TLWL), achieves its performance by integrating several features: knowledge of the SIP protocol; dynamic estimates of back-end server load; distinguishing transactions from calls; recognizing variability in call length; and exploiting differences in processing costs for different SIP transactions. By combining these features, our algorithm provides finer-grained load balancing than standard approaches, resulting in throughput improvements of up to 24 percent and response time improvements of up to two orders of magnitude. We present a detailed analysis of occupancy to show how our algorithms significantly reduce response time.

Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction has shown great impact on the performance of such services as location, routing, and path planning in sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, is not immediately applicable for the discrete and distributed sensor networks. In this paper we present CASE: a novel connectivity-based skeleton extraction algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, no centralized operation is required. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms state-of-the-art algorithms.

Current implementations of BitTorrent-like P2P applications ignore the underlying Internet topology hence incur a large amount of traffic both inside an Internet service provider (ISP)' national backbone networks and over cross-ISP Internet working links. These traffics not only occupy costly bandwidth, but also increase user perceived response latency. ISP-biased neighbor selection proposes to exploit peers' topological locality by biased neighbor selection, in which a peer chooses the majority of its neighbors from peers within the same ISP. In this paper, we propose to further exploit peers' geographic locality. First we improved ISP-biased neighbor selection (ISP-Biased+) to take into consideration network locality (or, city locations) within the same ISP. When required neighbor number is relatively much less than seeds available, ISP-biased neighbor selection+ performs much better than original approach, proved by simulations. Next, we propose that a peer could also choose its neighbors from peers of different ISPs within the same city with priority: assist by a well-know Chinese operator's unique ISP-internetworking content distribution network (CDN), these local cross-ISP traffics can be routed through local CDN cite. Using simulations, we show that cross-ISP traffic burden can be completely shifted to CDN local links and backbone traffic. At the same time, user perceived delay can be significantly reduced.

Grid applications have been prone to encountering problems such as failures or malicious attacks during execution, due to their distributed and large-scale features. The application itself, however, has limited power to address these problems. This paper presents the design, and implementation of an adaptive framework - Dynasa, which strives to handle security problems using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of applications according to the status of the grid environments.

Controlled-flooding algorithms are widely used in unstructured networks. Expanding ring (ER) achieves low response delay, while its traffic cost is huge; dynamic querying (DQ) is known for its desirable behavior in traffic control, but it achieves lower search cost at the price of an undesirable latency performance; Enhanced dynamic querying (DQ+) can reduce the search latency too, while it is hard to determine a general optimum parameters set. In this paper, a novel algorithm named selective dynamic query (SDQ) is proposed. Unlike previous works that awkwardly processing floating TTL values, SDQ properly select an integer TTL value and a set of neighbors to narrow the scope of next query. Our experiments demonstrate that SDQ provides finer-grained control than other algorithms: its latency is close to the well-known minimum one via ER; in the mean time its traffic cost also close to the minimum. To our best knowledge, this is the first work capable of achieving best performance in terms of both response latency and traffic cost. In addition, our experiments also demonstrate that SDQ works well in various network topologies.

For many applications in wireless sensor networks, accurate data collection is a crucial problem. Users may want to continuously extract data from the networks for analysis after. Clustering and prediction techniques, which exploit spatial and temporal correlation among sensor data, provide opportunities for reducing the energy consumption of sensor data collection. We propose the LEAP (Localized Energy-Aware Prediction) approach. LEAP is clustering based. A cluster head represents all sensor nodes in the cluster, and collects data values from them. LEAP implements local prediction algorithms, and only data values not within a specified error bound are collected by a cluster head. By doing so, the cluster head maintains an accurate view of the sensor data, while the communication cost is reduced. In this paper, we present energy-aware prediction models used in LEAP, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of energy-aware prediction. We believe LEAP has broad applications. Our proposed models, analysis, and algorithms are validated via simulation.

This paper studies the conditions under which P2P sharing can increase the capacity of IPTV services over FTTN networks. For a typical FTTN network, our study shows a) P2P sharing is not beneficial when the total traffic in a local video office is low; b) P2P sharing increases the load on FTTN switches and routers in local video offices; c) P2P sharing is the most beneficial when the network bottleneck is experienced in the southbound segment of a local video office (equivalently a northbound segment of an FTTN switch); and d) sharing among all FTTN serving communities is not needed when network congestion problems are solved by using some other technologies such as program pre-caching or replication. Based on the analytical results, design for IPTV services which monitors FTTN network conditions and decides when and how to share videos among peers to maximize the service capacity. Simulations and bounds both validate the potential benefits of the MediaGrid IPTV service platform.

Wireless sensor networks have stringent constraints on system resources and data aggregation techniques are critically important. However, accurate data aggregation is difficult due to the variation of sensor readings and due to the frequent communication failures. To address these difficulties, we propose a scalable and robust data aggregation algorithm. The novelty of our work includes two aspects. First, our algorithm exploits the mixture model and the Expectation Maximization (EM) algorithm for parameter estimation. Hence, it captures the effects of aggregation over different scales while keeping the communication cost low. Second, our algorithm exploits loss-tolerant multi-path routing schemes. Hence, it obtains accurate statistical information even in the presence of high link and node failure rates. We demonstrate that our techniques reduce communication cost while retaining the precious statistical information otherwise neglected by other aggregation techniques. Our evaluation shows the proposed techniques are robust against link and node failures, and perform consistently well.

In peer-to-peer streaming applications such as IP television and live shows, a key problem is how to construct an overlay network to provide high-quality, almost real-time media relay in an efficient and scalable manner. Much work has focused on the construction of tree and graph network topology, often based on the inference of network characteristics such as delay and bandwidth. Less attention has been paid to improving the liveness of media delivery, and to exploiting the flexibility of applications to construct better overlay networks. We propose the NSYNC, an ongoing work on constructing low-latency overlay networks for live streaming. It aims at solving the following problems. In typical applications, peers must buffer a portion of a real-time event, e.g., for at least a few seconds, to limit the impact of adversary network conditions. Thus, it introduces both (1) delay, especially long delay for peers that are many hops away from the origin servers, and (2) partial ordering between the peers. With NSYNC, the application media players can slightly increase or decrease the speed of playing media. Thus, the peers in a network can be synchronized to achieve two effects. First, late peers can catch early peers and the origin server such that the entire peer networks improve liveness. Second, the client/server roles between a pair of neighboring peers can be reversed, allowing opportunities for constructing more efficient overlay networks. NSYNC can be used in various peer-to-peer streaming systems.

In unstructured peer-to-peer networks, controlled flooding aims at locating an item at the minimum message cost. Dynamic querying is a new controlled flooding technique. While it is implemented in some peer-to-peer networks, little is known about its undesirable behavior and little is known about its general usefulness in unstructured peer-to-peer networks. This paper describes the first evaluation and analysis of such techniques, and proposes novel techniques to improve them. We make three contributions. First, we find the current dynamic querying design is flawed. Although it is advantageous over the expanding ring algorithm in terms of search cost, it is much less attractive in terms of peer perceived latency, and its strict constraints on network connectivity prevent it from being widely adopted. Second, we propose an enhanced flooding technique which requires the search cost close to the minimum, reduces the search latency by more than four times, and loosens the constraints on the network connectivity. Thus, we make such techniques useful for the general unstructured peer-to-peer networks. Third, we show that our proposal requires only minor modifications to the existing search mechanisms and can be incrementally deployed in peer-to-peer networks.

Providing Internet access to Mobile Ad hoc Networks (MANETs) can greatly extend their applications, increase their scalability, and improve the quality of service. However, a critical problem is how the mobile hosts can locate Internet-based servers efficiently in a dynamic, unstructured network. Neither reactive strategies, where the hosts initiate on-demand server discovery, nor proactive strategies, where the servers periodically advertise their availability information, are optimal. To that end, this paper studies adaptive strategies that (1) combine both proactive advertising by the servers and on-demand discovery by the mobile hosts, and (2) determine the relative rate of proactive advertising and on-demand discovery adaptively according to the network characteristics including the host mobility level and the offered load. We propose and evaluate two novel, integrated algorithms. First, to determine the rate of proactive advertising, we propose an exponential backoff algorithm to probe the optimal operating point. Second, to reduce the network traffic due to reactive (on-demand) discovery, we propose a novel controlled flooding algorithm. Our simulation study reveals that, compared to the previous proactive strategies and reactive strategies, our adaptive strategies reduce network traffic for locating the servers by several times when the network has a moderate offered load and a low or high level of host mobility.