Recently, Wi-Fi signals have emerged as a powerful tool for contactless sensing. During the sensing process, obtaining target direction information can provide valuable contextual insights for various applications. Existing direction estimation methods typically rely on antenna arrays, which are costly and complex to deploy in real-world scenarios. In this paper, we present WiRainbow, a novel approach that enables single-antenna-based direction awareness for Wi-Fi sensing by leveraging the dispersion effect of frequency-scanning antennas (FSAs), which can naturally steer Wi-Fi subcarriers toward distinct angles during signal transmission. To address key challenges in antenna design and signal processing, we propose a coupled-resonator-based antenna architecture that significantly expands the narrow Field-of-View inherent in conventional FSAs, improving sensing coverage. Additionally, we develop a sensing signal-to-noise-ratio-based signal processing framework that reliably estimates target direction in multipath-rich environments. We prototype WiRainbow and evaluate its performance through benchmark experiments and real-world case studies, demonstrating its ability to achieve accurate, robust, and cost-effective direction awareness for diverse Wi-Fi sensing applications.

Integrated sensing and communication (ISAC) is expected to play a prominent role in 6G. Avoiding full duplex transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, thus considered an indispensable scenario of ISAC. However, bi-static sensing must resolve the non-ideal synchronization of the transceiver nodes. Such timing offset (TO) remains a challenging issue, especially in the non-line-of-sight (NLOS) condition with rich multipaths. This article makes the best use of all multipaths, deriving the cyclic shift relation between the delay spectrums measured at the two sides of transceivers to estimate the TO. Based on this theoretical analysis, an algorithm called enhanced round-trip measurement (eRTM) is developed to mitigate the TO. Specifically, the cyclic cross-correlation between the amplitudes of the two normalized delay spectrums measured at the two sides of the transceivers respectively is conducted to estimate the TO, which is then used for TO mitigation. Extensive simulations have verified the advantages of the eRTM algorithm, especially in NLOS conditions and with low signal-to-noise ratios (SNRs). In addition, field tests conducted with a bi-static ISAC prototype system and eRTM algorithm, have achievedcentimeter-level positioning accuracy even in NLOS conditions with rich multipaths, confirming the validities of our theoretical analysis as well as the proposed algorithm.

Detecting whether a target crosses a certain zone can enable various applications, such as intrusion detection, visitor counting, etc. Traditional methods usually use cameras or infrared sensors for detection. These methods may raise privacy concerns or limit the detection range. Unlike these methods, by exploiting Channel State Information (CSI) obtained from a commodity WiFi device, WiFi-based methods have great detection potential due to their non-intrusive, privacy-preserving nature and wider coverage area. Although many existing WiFi-based methods can detect whether a target crosses the Line of Sight (LOS) of the WiFi transceiver pair, these methods usually require a high sampling rate (such as 1000Hz), which can severely impact WiFi communication. Moreover, these methods cannot precisely detect whether the target crosses the First Fresnel Zone (FFZ). In this paper, we propose WiCrossing, which can accurately detect whether a target crosses the FFZ of a pair of WiFi transceivers, even under low sampling rate conditions. We divide the crossing FFZ behavior into three stages: (1) entering the FFZ, (2) crossing the LOS path, (3) exiting the FFZ. By using the frequency diversity of CSI, we design a metric to discriminate whether the target enters or exits the FFZ. In order to achieve accurate detection under low sampling rate conditions and mitigate the multipath effects, we propose an effective metric construction method that can extract features that are only relevant to the moving target, and we propose an algorithm to obtain the crossing LOS indicator which can robustly detect whether the target crosses the LOS in different environments even under low sampling rate conditions (such as 50Hz). We implement WiCrossing with commercial WiFi devices, and extensive experiments demonstrate that WiCrossing can achieve a high True Positive Rate (TPR) and a low False Positive Rate (FPR) under various evaluation conditions.

Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing.$\bf{NearLink}$is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink's communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.

Beyond communication, wireless signals have been extensively utilized for localization, tracking, and sensing in recent years. The key information extracted for these purposes includes distance and angle. While distance measurement accuracy is mainly limited by signal bandwidth, angle accuracy depends on the number of antennas and phase noise. Conventional approaches typically improve angle estimation by boosting signal strength and increasing the number of antennas. In this paper, we propose employing a quantum receiver to substantially improve angle estimation performance. Rather than amplifying signal strength, the quantum receiver reduces the inherent hardware noise. Furthermore, we exploit the unique properties of a quantum RF receiver to construct a multi-antenna quantum system. Using only two physical quantum antennas, we generate virtual antennas by leveraging the receiver's broad frequency range, effectively increasing the number of antennas and significantly improving angle measurement performance. Our experimental results demonstrate that, with only two quantum antennas, we achieve angle estimation performance surpassing that of a conventional RF receiver equipped with 40 antennas. Furthermore, quantum antennas are not constrained by the coupling effects that typically limit the spacing between conventional RF antennas, allowing for much closer placement. This represents a significant step toward reducing the size of antenna arrays while preserving localization and tracking performance.

Wireless sensing technologies have shown significant promise in various applications, but their spatial coverage is confined to the vicinity of the transmitters, limiting their applicability in broader environments. In this paper, we introduce an innovative wireless sensing approach based on the globally covered Global Navigation Satellite System (GNSS) signals. While GNSS signals have been widely used in remote sensing to monitor slow changes in the Earth’s surface, like sea level and snow depth, their ability to accurately detect highly dynamic target motions, such as human respiration, gestures, and intrusions, remains unclear. The main challenge arises from the interference brought by the large-scale satellite movement and severe GNSS signal errors. In this study, we present a novel GNSS signal enhancement system named FineSat to address these interference. Specifically, we first utilize polynomial representations to cancel satellite movement interference. Then, based on the analysis of GNSS signal errors, we propose a signal differential processing module to mitigate the errors. We implement our system on commercial devices and validate its performance in three sensing applications: respiration monitoring, gesture recognition, and intrusion detection. Results show that we achieve 0.42 bpm mean absolute error in respiration monitoring, 96.5% average accuracy in gesture recognition, and 98.6% accuracy in intrusion detection.

The global aging demographic underscores the imperative for continuous in-home monitoring of the empty-nest elderly, ensuring their safety and well-being. The widespread deployment of Wi-Fi infrastructure has paved the way to monitor the elderly in a non-intrusive and privacy-preserving manner. Numerous studies have explored the potential of utilizing Wi-Fi signals to address urgent life safety concerns such as fall detection and vital sign monitoring. However, apart from these acute safety issues, the early detection of potential disease symptoms and managing the progression of chronic diseases are also crucial for elderly care, which calls for long-term and continuous monitoring of the elderly’s daily routines. Unfortunately, challenges like continuous activity segmentation and location/orientation dependencies have hindered the implementation of a long-term, around-the-clock activity monitoring system for the elderly. This work introduces “WiLife,” a cutting-edge Wi-Fi-based framework for continuous monitoring of the elderly’s spatio-temporal daily status information. Specifically, WiLife adopts a strategy of partitioning living spaces into functional areas and categorizing daily activities into atomic states . By encapsulating daily life status into a unique series of triple unit format: \(\left\langle\textit{Time, Area, State}\right\rangle\) , WiLife is able to offer valuable insights into when, where, and how activities occur. Field implementations spanning 1,080 hours (45 days \(\times\) 24 hours) in real-world home environments highlight WiLife’s exceptional capability in understanding individual living habits and timely detection of irregularities.

International audience

Foreseen as a killer application in next-generation wireless networks, integrated sensing and communication (ISAC) has gained tremendous developments in recent years, and will contribute to realize Internet of Everything (IoE). Particularly, by enabling separable sensing transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, and thus has become an indispensable scenario of ISAC. However, bi-static sensing suffers from transceiver asynchronization, which induces timing offset (TO), timing drift (TD) and carrier frequency offset (CFO). In this paper, we first give the theoretical analyses on how TO, TD and CFO impact the sensing signal, under the practical configuration following the new radio (NR) protocol. Based on this, we systematically reveal the mechanism of TD and the correspondingly resulted delay-Doppler spectrum dispersion. Specifically, the delay spectrum shifts and the phase drifts induced by TD are analyzed, which are the two main factors eventually leading to the delay-Doppler spectrum dispersion and consequent severe errors in signal detection and parameter estimation. Based on the revealed mechanisms, we develop an asynchronous delay-Doppler (ADD) algorithm for bi-static sensing, including delay spectrum alignment and phase compensation, respectively to suppress the delay spectrum shifts and phase drifts. Thanks to the revealed mechanism, the ADD algorithm does not rely on specific prerequisites. Simulation results have confirmed the revealed mechanisms and verified the effectiveness of the ADD algorithm. Particularly, field tests are conducted on an ISAC prototype, and achieve a centimeter-level positioning accuracy, which further confirms the revealed mechanisms and validates the ADD algorithm.

With the rapid development of artificial intelligence (AI), integrating deep neural networks (DNNs) into mobile and embedded devices has become an important trend. This integration significantly enhances the ability of these devices to collect and analyze perceptual data. Traditionally, the integration paradigm relies on cloud based training and deployment on mobile devices. However, the dynamic characteristics and privacy issues related to real-world perceptual data require training on the device. Despite its advantages, the limited computing resources of mobile devices constitute a key bottleneck that hinders the efficiency of model training. To address this issue, parallel distributed training across mobile device clusters has become a feasible paradigm. However, the inherent mobility of these devices not only increases the possibility of training interruptions, but also exacerbates the inefficiency caused by data imbalance. These challenges make traditional cloud based model parallelization methods unsuitable for mobile environments. To overcome these limitations, this paper proposes a novel model parallel system CrowdMesh designed for mobile device clusters. CrowdMesh consists of three key modules: (1) alliance-game-based dynamic cluster startup and construction,(2) computation-cost-based DL model parallel, (3) device-mobility-aware parameter propagation module. These modules work together to address training interruptions and restarts caused by device mobility, as well as efficiency issues caused by data imbalance.The experimental results show that CrowdMesh performs better than existing cloud and device based model parallelization baselines in various training tasks and deep learning models, and can reduce training latency by more than 18%.

Traditional liquid detection approaches are often time-intensive and invasive, typically requiring the opening of containers for examination. While recent initiatives have proposed several innovative solutions, including camera-based and vibration sensor-based techniques, these approaches still face limitations in terms of convenience. The development of radio frequency (RF) technology, particularly millimeter-wave (mmWave) radar, offers a promising solution for non-invasive and contactless liquid detection. In particular, during the past few years, a number of radar-based sensing systems have been developed to detect or identify liquids. However, little work has been done on the simultaneous detection of multiple liquids. To fill this gap, we design a novel composite reflection model, which overcomes the detection challenges due to composite interference and environmental reflections, by utilizing the consistency and uniqueness of the reflection signals from multiple liquid targets. Based on the proposed model, we develop a system namedMultiScanner, which is able to detect different types of liquids in multi-target scenarios, exhibiting high location independence without the need for extensive data training. Extensive experiments validate the effectiveness ofMultiScanner, achieving up to 95.91% accuracy in detecting 10 hazardous-normal liquid combinations in 2-target scenarios. Moreover, even in more complex 5-target scenarios, an detection accuracy of 86.49% can be obtained. To the best of our knowledge, this is the first study that uses RF signals for multi-liquid detection.

The widespread deployment of WiFi infrastructure has facilitated the development of Time-of-Flight (ToF) based sensing applications. ToF estimation, however, is a challenging task due to the complexity of multipath effect. In this paper, we propose a phase difference based method for ToF estimation and uncover the potential of signal cancellation to mitigate the impact of multipath and noise on phase differences among subcarriers. To separate the moving target path from the complex multipath for ToF estimation, we suggest employing specific elimination methods tailored to the characteristics of different signal components. For dynamic multipath, we observe that when a given subcarrier propagates along two paths to the receiver, with path lengths differing by half a wavelength, the phase difference introduced by these two paths cancels each other out. Therefore, we propose two metrics to identify signals that satisfy this condition, utilizing both frequency diversity and spatial diversity. Additionally, we propose leveraging time diversity to eliminate the static multipath component and reduce the impact of noise. We implemented the methods with off-the-shelf WiFi devices and achieved mean errors of 15.36 cm and 21.05 cm for distance estimation in outdoor and indoor scenarios, outperforming state-of-the-art ToF estimation method by 50% error reduction.

This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component’s amplitude for precise separation. A system calledFinerSenseis developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.

Acoustic sensing for heartbeat monitoring has emerged as a prevailing research topic in wireless sensing. However, existing acoustic sensing systems face two limitations: a restricted sensing range and operation limited to a single user, impeding large-scale deployment of its applications. In this paper, we present DF-Sense, aDualForming based multi-user acousticSensingsystem for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namelyDualforming, which leverages constructive superposition across multiple subcarriers and microphones. To facilitateDualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method and a 2-D peak identification scheme to locate and identify multiple subjects with subtle motions. Additionally, we propose a phase change-based method to promptly identify body leaning and adaptively re-localize subjects, thereby avoiding the high computational cost. Finally, we propose an enhanced recursive least squares (RLS) filter to effectively reconstruct high-quality heartbeat waveforms from Channel Frequency Response (CFR) signals affected by limb movements. Experimental results show that DF-Sense achieves high precision measurement of instantaneous heart rates within a range of 10 m, sufficient for most daily space requirements, and can monitor heartbeat for up to 6 subjects in a 2-D space.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, highlighting the critical need for accurate and continuous heart health monitoring. Electrocardiograms (ECG), considered as the golden standard for diagnosing and monitoring heart-related conditions, offer precise measurements but require direct skin contact, limiting their practicality for long-term and everyday use. On the other hand, existing RF sensing techniques that analyze signals reflected off the skin struggle to distinguish micro cardiac motions of the heart due to weak motion amplitude and respiration interference at the chest wall. To overcome these limitations, we introduce WiCG, a novel contact-less cardiac motion monitoring system that employs 2.4 GHz Wi-Fi signals to penetrate the chest and detect subtle cardiac movements. A mix-medium Wi-Fi Fresnel zone model is developed to explain the enhanced phase sensitivity of in-body Wi-Fi signals, which is crucial for accurately detecting cardiac motions. By strategically positioning antennas near the heart, WiCG captures ventricular motions effectively. A novel cardiac Doppler method is proposed to suppress phase noise and interference from static paths and extract the time interval between the systole and diastole of the ventricular. Extensive experiments demonstrate that the proposed system can robustly estimate the R-R and Q-T intervals of human cardiac cycles across 21 subjects and different environments with an average accuracy of 99.22% and 92.8%, achieving performance comparable to ECG.

Indoor mmWave-based sensing technologies have garnered substantial interest from both the industrial and academic. Yet, the intrinsic challenge posed by the limited Field-of-View (FOV) of mmWave radars significantly restricts their coverage. This limitation necessitates careful selection of installation positions and orientations to optimize performance, thereby severely curtailing the versatility and widespread adoption of these systems. Traditionally, expanding coverage involved increasing the number of radar units. This paper introduces a novel approach to enhance the FOV by incorporating mobility, achieved by affixing the radar onto a pan-tilt unit capable of rotating along both the horizontal and azimuthal. Nevertheless, the disparity between the pan-tilt and the radar presents significant challenges for accurately rotating the radar's orientation. To mitigate this, we propose an automated calibration algorithm for radar and pan-tilt, ensuring precise calibration. Additionally, we have devised a radar orientation adjustment algorithm intended to automatically align the radar's FOV with the positions of detected objects to facilitate various applications. Through three case studies, we have demonstrated that mmRotation can greatly expand the sensing range, enabling support for multiple applications on a single radar, such as vital signs monitoring and fall detection. Comprehensive experimental results underscore that our system surpasses the current state-of-the-art (SOTA).

In recent years, wireless signals have been extensively investigated for contactless human respiration monitoring. However, most wireless sensing systems encounter challenges when the target exhibits body movements. In this demo, we present a solution to mitigate the impact of body motion on contactless respiration monitoring. By employing novel signal processing techniques, body motion can be first estimated and subsequently eliminated from the signal reflected signal by the chest. We prototype the proposed system using a MIMO mmWave radar. Evaluations in real-world environments demonstrate the effectiveness of the solution.

Wireless signals have been widely utilized for human sensing. However, wireless sensing systems face a fundamental limitation, i.e., the wireless device must keep static during the sensing process. Also, when sensing fine-grained human motions such as respiration, the human target is required to stay stationary. This is because wireless sensing relies on signal variations for sensing. When device is moving or human body is moving, the signal variation caused by the target area (e.g., chest for respiration sensing) is mixed with the signal variation induced by device or other body parts, failing wireless sensing. In this paper, we propose MSense, a general solution to deal with motion interference from wireless device and/or human body, moving wireless sensing one step forward towards real-life adoption. We establish the sensing model by taking both device motion and interfering body motion into consideration. By extracting the effect of body and device motions through pure signal processing, the motion interference can be removed to achieve accurate target sensing. Comprehensive experiments demonstrate the effectiveness of the proposed scheme. The achieved solution is general and can be applied to different sensing tasks involving both periodic and aperiodic motions.

Despite intensive research efforts in radio frequency noncontact sensing, capturing fine-grained geometric properties of objects, such as shape and size, remains an open problem using commodity WiFi devices. Prior attempts are incapable of characterizing object shape or size because they predominantly rely on weak signals reflected off objects in a very small number of directions. In this paper, motivated by the observation that the diffracted signals around an object between two WiFi devices carry the contour information of the object, we formulate the problem of reconstructing the 2D target profile and develop WiProfile, the first WiFi-based system that unlocks the diffraction effects for target profiling. We introduce a CSI-Profile model to characterize the relationship between the CSI measured at different target positions and the target profile in the diffraction zone. With suitable approximations, the inverse problem of deriving the target profile from CSI can be solved by the inverse Fresnel transform. To mitigate CSI measurement errors on commodity WiFi devices, we propose a novel antenna placement strategy. Comprehensive experiments demonstrate that WiProfile can accurately reconstruct profiles with median absolute errors of less than 1 cm under various conditions, and effectively estimate the profiles of everyday objects of diverse shapes, sizes, and materials. We believe this work opens up new directions for fine-grained target imaging using commodity WiFi devices.

While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow bandwidth of WiFi. Very recent studies attempted to measure object sizes using WiFi signals. However, these systems are still far from practical with a lot of limitations including requiring multiple transceiver pairs and can only measure one-dimensional size, hindering their real-life adoption. Also, these systems rely on Channel State Information (CSI) to work, which is only available on few commercial WiFi cards. In this work, we propose to employ a new channel data, i.e., Beamforming Feedback Information (BFI), widely available on almost all new generation WiFi cards for fine-grained size measurement. Through thoroughly analyzing the mathematical relationship between BFI and CSI, we show how to use BFI to achieve fine-grained size measurement. We propose a novel method to accurately measure the two-dimensional size of an object using a single transceiver pair by identifying the positions of singularities when the object passes through the diffraction zone of the transceiver pair. Experiment results show that Wi2DMeasure can accurately measure the two-dimensional size of objects under various conditions, achieving a small median error of only 3.7 mm.

Recent advancements in Wi-Fi-based sensing technologies have enabled effective hand gesture recognition. However, most studies focus on single gesture recognition and fail to recognize naturally performed continuous gestures without pauses in transitions. The main challenges include diverse and uncertain transitions in continuous gesture recognition, making it difficult to segment and identify gestures from a stream of continuous hand movements. In this paper, we introduce a new method to recognize continuously performed gestures from a set of predefined gestures (e.g., digits) without requiring a pause in transitions. Instead of segmenting gestures at the gesture-transition level, we segment the stream into basic fractions that depict exclusive moving patterns of gestures. We propose a novel feature called meta motion, which geometrically characterizes different basic hand movements. Leveraging this feature, we use a back-tracking searching-based algorithm to identify gestures from the sequence of meta motions. Based on this approach, we develop a prototype system, WiCGesture, on commodity Wi-Fi devices. WiCGesture is the first system engaging in continuous gesture recognition using Wi-Fi signals. Evaluation results show that WiCGesture effectively recognizes continuous gestures from two gesture sets, significantly outperforming state-of-the-art methods.

The existing commodity Wi-Fi-based human gait recognition systems mainly focus on a single subject due to the challenges of multisubject walking monitoring. To tackle the problem, we propose Wi-Diag, the first commodity Wi-Fi-based multisubject abnormal gait diagnosis system that leverages only one pair of off-the-shelf commercial Wi-Fi transceivers to separate each subject’s gait information and maintains an excellent performance when the scenario changes. It is an intelligent multisubject gait diagnosis system that can release an experienced doctor from heavy load work. Multisubject abnormal gait diagnosis is modeled as a blind source separation (BSS) issue, and multisubject walking mixed signals are efficiently separated by IC analysis (ICA) approach. This fact is verified by comprehensive theoretical derivation and experimental validation. In addition, CycleGAN is leveraged to mitigate the environmental dependency so that Wi-Diag can be robust when the scenario changes. The excellent performance of Wi-Diag is verified by extensive experiments. The average mean diagnosis accuracy with a maximum group size of four and various scenarios is 87.77%.

Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.

Traffic prediction is one of the fundamental spatio-temporal prediction tasks in urban computing, which is of great significance to a wide range of applications, e.g., traffic controlling, vehicle scheduling, etc. Recently, with the expansion of the city and the development of public transportation, long-range and long-term spatio-temporal correlations play a more important role in traffic prediction. However, it is challenging to model long-range spatial dependencies and long-term temporal dependencies simultaneously in two aspects: 1) complex influential factors, including spatial, temporal and external factors. 2) multiple spatio-temporal correlations, including long-range and short-range spatial correlations, as well as long-term and short-term temporal correlations. To solve these issues, we propose a spatio-temporal memory augmented multi-level attention network for fine-grained traffic prediction, entitled ST-MAN. Specifically, we design a spatio-temporal memory network to encode and memorize fine-grained spatial information and representative temporal patterns. Then, we propose a multi-level attention network to explicitly model both short-term local spatio-temporal dependencies and long-term global spatio-temporal dependencies at different spatial scales (i.e., grid and region levels) and temporal scales (i.e., daily and weekly levels). In addition, we design an external component that takes external factors and spatial embeddings as inputs to generate location-aware influence of the external factors much more efficiently. Finally, we design an end-to-end framework optimized with the contrastive objective and supervised objective to boost model performance. Empirical experiments over coarse-grained and fine-grained real-world datasets demonstrate the superiority of the ST-MAN model compared to several state-of-the-art baselines.

Dynamic prices are used in many scenarios, e.g., flight ticketing, hotel room booking and ride-on-demand (RoD) service such as Uber and DiDi, and while they are beneficial for service providers, practitioners or users, they lead to the concern of privacy leakage – the possibility of learning user information from dynamic prices. In this paper, we aim to study this possibility and choose trip purpose mining in RoD service as an attack example, based on real-world large datasets. We discuss the criteria of choosing datasets – ubiquitous, collective and easily accessible – from the perspective of an attacker, and extract features describing trip information, spatio-temporal and dynamic prices context. The trip purpose mining problem is then solved as a multi-class classification problem and multiple binary-class problems. In the multi-class problem, we verify that dynamic prices information results in a 17.1% improvement in classification accuracy; in the binary-class problems, we quantify feature contributions and explain the different extents of privacy leakage in identifying different trip purposes. Our hope is that the study not only serves as a case study demonstrating the privacy leakage problem in RoD service, but also sheds light on such privacy problem in other services using dynamic prices and triggers more research efforts.

This paper demonstrates that thefractal dimension of frame preambleserves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

During the last decade, there have been lots of efforts on wireless sensing using Wi-Fi signals, which can be divided into two categories, i.e., the pattern-based approach and the model-based approach. Recently, more and more attention has been paid on the model-based approach, mainly due to its superiority of no need for collecting a large dataset or retraining the model for new environments. However, existing models are mainly designed for Line-of-Sight (LoS) scenarios, which are not applicable to Non-Line-of-Sight (NLoS) scenarios, such as through-wall sensing. To bridge this gap, we put forward a through-wall wireless sensing model to reveal the sensing mechanism of Wi-Fi signals in NLoS scenarios. In particular, arefraction-awareFresnel zone model is developed by taking into account both the reflection propagation and the refraction propagation of Wi-Fi signals. For the first time, we discover that the geometric distribution of Fresnel zones becomes uneven, due to the difference in dielectric constants between the air and the wall. Specifically, some areas become denser and other areas become sparser, leading to thesqueeze effectandstretch effectof Fresnel zones. Inspired by the insight, we further put forward a new metric namedcompression-ratioto quantify the through-wall sensing capability of Wi-Fi signals. Meanwhile, a set of algorithms are developed to guide the deployment of Wi-Fi sensing systems. To validate the proposed model, we implement a through-wall respiration sensing prototype system. Experiments show that the respiration detection performance varies significantly when the user locates in different areas. Specifically, for two sensing locations (one in the compression area and the other in the expansion area) symmetrically distributed on both sides of the transceivers’ connection line, the difference in mean absolute errors (MAE) can exceed 3 times.

With the rise of Artificial Intelligence of Things (AIoT), integrating deep neural networks (DNNs) into mobile and embedded devices has become a significant trend, enhancing the data collection and analysis capabilities of IoT devices. Traditional integration paradigms rely on cloud-based training and terminal deployment, but they often suffer from delayed model updates, decreased accuracy, and increased communication overhead in dynamic real-world environments. Consequently, on-device training methods have garnered research focus. However, the limited local perception data and computational resources pose bottlenecks to training efficiency. To address these challenges, Federated Learning emerged but faces issues such as slow model convergence and reduced accuracy due to data privacy concerns that restrict sharing data or model details. In contrast, we propose the concept of trusted clusters in the real world (such as personal devices in smart spaces, trusted devices from the same organization/company, etc.), where devices in trusted clusters focus more on computational efficiency and can also share privacy. We propose CrowdLearning, a decentralized distributed training framework based on trusted AIoT device collectives. This framework comprises two collaborative modules: A heterogeneous resource-aware task offloading module aimed at alleviating training latency bottlenecks, and an efficient communication data reallocation module responsible for determining the timing, manner, and recipients of data transmission, thereby enhancing DNN training efficiency and effectiveness. Experimental results demonstrate that in various scenarios, CrowdLearning outperforms existing federated learning and distributed training baselines on devices, reducing training latency by 55.8% and lowering communication costs by 67.1%.

In recent years, we have seen efforts made to monitor respiration for multiple users. Existing approaches capture chest movement relying on signals directly reflected from chest or separate breath waves based on breath rate difference between subjects. However, several limitations exist: 1) they may fail when subjects face away from the transceiver or are blocked by obstacles or other subjects; 2) they may fail to separate subjects' breath waves with the same or similar rates (i.e., breath rate differenceMultiResp, a multi-user respiration monitoring system using acoustic signal. By fully leveraging the abundant acoustic signals reflected indirectly from subjects' chest,MultiRespcan robustly capture chest movement even when they face away from the transceiver or are blocked. By extracting fine-grained breath rate and phase difference between different subjects,MultiRespcan separate the breath waves with the same or similar rates and adapt to dynamic change of subject number during monitoring. Extensive experiments show thatMultiRespis able to accurately monitor the respiration of multiple users with a median error of 0.3 bpm in various indoor scenarios, however, it fails when the sound pressure is lower than 55 dB or body movement is happening.

Most existing RF-based vital sign monitoring systems either assume that a human subject is stationary or discard measurements when motion is detected in order to output reliable respiration rates and heart rates. Such an assumption greatly limits the usability of these systems in practice. Even during sleep, one can undergo various body states including turns and involuntary twitches in light sleep, motionlessness during deep sleep, or abnormal limb movements due to sleep disorders such as restless legs syndrome. In this work, we develop SlpRoF, a low-cost contact-free system using a commercial-off-the-shelf UWB radar that achieves high temporal coverage and high accuracy in vital sign monitoring during sleep. By classifying body states into the motionless state, limb movement state, and torso movement state, and extracting vital signs during the first two states, it directly increases effective reporting periods over nights. By analyzing high-order harmonics and leveraging spatial diversity in captured signals from multiple on-body areas, it improves the accuracy of heart rate estimations and thus indirectly increases temporal coverage through reliable assessments. Experiment results show that SlpRoF is able to achieve an average median absolute error (MAE) of 0.44 beats per minute (bpm) in respiration rates, 1.55s in respiration intervals, and 0.9 bpm for heart rates, respectively.

Contactless respiration monitoring using wireless signals has drawn much attention in recent years. Many approaches have been proposed, however, they may not work when there is a lack of signals directly reflected from target's chest, e.g., a target faces away from the transceiver or a target is blocked by furniture. In this paper, we design and implement a novel omnimonitoring system for human respiration,OmniRespMonitor, using a pair of speaker and microphone. Different from Radio Frequency (RF) signal, acoustic signals cannot penetrate through walls and furniture. The multipath reflection in an indoor environment will result in highly abundant acoustic signals. In this case, even though there are lack of acoustic signals directly reflected by a target's chest, indirectly-reflected acoustic signals can still be received by the microphone. We can therefore monitor the target's respiration by extracting this subtle variation of indirectly reflected signals. To achieve this, we model chest movement using truncated System Frequency Response (SFR). We then develop a global search method based on the autocorrelation function to extract minute chest movement from SFR sequences. Finally, we dynamically synthesize the chest movement information to recover the breathing wave in real time. We conduct extensive experiments with both humans and animals (goat), the results show thatOmniResMonitoris able to monitor single target's respiration within 5 meters in indoor environments in various challenging scenarios there are lack of directly-reflected acoustic signals.

Although WiFi-based contactless sensing has made significant progress in the past decade, most prior work still focus on the reflection zone far from WiFi transceivers, while few studies explore the diffraction zone near transceivers. Additionally, previous diffraction models only consider the CSI amplitude signal and ignore the impact of multipath. In this work, we develop an accurate diffraction model to characterize the relationship between both CSI amplitude and phase and target's movement in the diffraction zone. We further put forward the deformation forms of the model under static multipath conditions and find that the CSI patterns vary significantly with multipath. Consequently, the common assumption of a one-to-one mapping between CSI patterns and activities in existing work fails due to multipaths, degrading sensing performance when multipath changes. To address this challenge, we propose to extract a relative change pattern from CSI signals to recover the one-to-one mapping relations and eliminate the impact of static multipath. Extensive experiments under various multipath conditions demonstrate an accuracy higher than 96% for the coarse-grained intrusion detection and an average error rate of 0.6 bpm for the fine-grained respiration monitoring.

Long-term monitoring of human vital signs like respiration and heartbeat is crucial for the early detection of diverse diseases and overall health monitoring. Contact-free vital signs monitoring using wireless signals, particularly mmWave-based methods, has gained attention due to its sensitivity and privacy-preserving benefits. However, we observe that even minor human movements could lead to significant mutations in the signal-to-noise ratio (SNR) of the wireless signal, which cannot be explained by the commonly used model that represents the human chest as a single reflection point. These fluctuations challenge the robustness of heart rate and heart rate variability (HRV) monitoring due to the vulnerability of faint heartbeats to noise interference. To tackle this, we introduce a multi-point reflection model to understand the underlying causes of SNR fluctuations and propose a frequency diversity based algorithm to enhance sensing SNR. Our solution, Robust-Vital, was rigorously evaluated using commercial mmWave radar systems and demonstrated superior performance on long-term heart rate and heart rate variability tracking in a user study with 12 participants.

IoT uplink performance is crucial for a wide variety of IoT applications such as health sensing and industrial control, which demand reliable delivery of sensor data to the cloud. However, due to the limited transmission power budget imposed on many power-constrained IoT devices, IoT uplinks are highly susceptible to cross-technology interference (CTI) caused by coexisting networks. Previous approaches to mitigating CTI have relied on MAC/PHY designs. They suffer from poor performance and limited generality in the presence of wideband CTI sources such as Wi-Fi and RF jammer, which transmit aggressively on large spectrum chunks using diverse radio technologies.

Recent years have witnessed a tremendous amount of interest in wireless sensing, i.e., instead of employing traditional sensors, wireless signal is utilized for sensing purposes. Contact-free wireless sensing has been successfully demonstrated using various RF signals such as WiFi, RFID, LoRa, and mmWave, enabling a large range of applications. However, limited by hardware thermal noise, the granularity of RF sensing is still relatively coarse. In this paper, instead of using the macro signal power/phase for sensing, we propose the first quantum wireless sensing system, which uses the micro energy level of atoms for sensing, improving the sensing granularity by an order of magnitude. The proposed quantum wireless sensing system is capable of utilizing a wide spectrum of frequencies (e.g., 2.4 GHz, 5 GHz and 28 GHz) for sensing. We demonstrate the superior performance of quantum wireless sensing with two widely-used signals, i.e., WiFi and 28 GHz millimeter wave. We show that quantum wireless sensing can push the sensing granularity of WiFi from millimeter level to sub-millimeter level and push the sensing granularity of millimeter wave to micrometer level.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

The lack of rich visual information affects the shopping experience of the visually impaired (VI), including identifying and selecting commodities. Recent studies on VI assistance have focused on commodity identification but neglected to provide fine-grained and intuitive pick-up guidance, which is not user-friendly enough. Therefore, we propose a user-driven shopping assistance system to improve the shopping experience for VI users. We first conduct an in-depth interview with VI, then implement a prototype shopping assistance system—MobiEye, with real-time video analysis. Further, we evaluate the prototype system and identify two directions to optimizing the existing system: (1) Improving the pick-up accuracy in dense placement; and (2) reducing the latency and communication overhead. To address these two problems, we design a new guidance strategy for dense placements and propose a mobile-edge cocomputing strategy with a motion predictor and a communication gate to filter the transmitted images. Finally, we invited VI participants to evaluate the effectiveness and efficiency of MobiEye. The experimental results show that MobiEye achieved a 13% improvement in pick-up success rate and a 12 s reduction in average pick-up time compared with other shopping assistance systems.

While spatial-temporal environment monitoring has become an indispensable way to collect data for enabling smart cities and intelligent transportation applications, the cost to deploy, operate and maintain a sensor network with sensors and massive communication infrastructure is too high to bear. Compared to the infrastructure-based sensing approach, community sensing, or namely mobile crowdsensing, that leverage community members' mobile devices to collect data becomes a feasible way to scale up the spatial-temporal coverage of the sensing system. However, a community sensing system would need to aggregate sensors and location data from community members and thus would raise concerns on privacy and data security In this paper, we present a novel community sensing paradigm AFCS -Sensor and Location Data Aggregation-Free Community Sensing, which is designed to obtain the environment information (e.g., spatial-temporal distributions of air pollution, temperature, and bike-shares) in each subarea of the target area, without aggregating sensor and location data collected by community members. AFCS proposes to orchestrate with the Trusted Execution Environments (TEEs) of every community member's mobile device to cover the communication, computation and storage with spatial-temporal data. Further, AFCS proposes a novel Decentralized Spatial-Temporal Compressive Sensing framework based on Parallelized Stochastic Gradient Descent. Through learning the latent structure of the spatial-temporal data via decentralized optimization, AFCS approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in every member's TEE instance. Experiments based on real-world datasets and the Virtual Mobile Infrastructure (VMI) with TEE emulations demonstrate that AFCS exhibits low approximation error (i.e., less than 0:2°C in city-wide temperature sensing, 10 units of PM2.5 index in urban air pollution sensing, and 2 bikes in city-wide bike sharing prediction) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation

WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as time-of-flight (ToF), angle-of-arrival (AoA), and Doppler-frequency-shift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standards-compliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds. This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error$<2.5\%$for typical room-sized trajectories.

Besides the communication function, various RF signals such as WiFi and RFID have been actively exploited for sensing purposes recently. However, a missing component of existing RF sensing is sensing under device motions. This paper takes the first step to involve device mobility into the ecosystem of RF sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chips in recent years, we propose to integrate the accuracy of UWB sensing with device mobility to support truly ubiquitous RF sensing. This is a challenging task because the motion artifacts from RF devices can easily overwhelm the target motion, such as subtle chest displacement for respiration sensing. In this demo, we propose Mobi2Sense to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using a commodity UWB module. Comprehensive evaluation demonstrates that Mobi2Sense is able to "hear" music and "see" human respiration at high accuracy in the presence of device motions.

Among various wireless sensing modalities, Ultra-Wideband (UWB) exhibits unique advantages such as fine granularity owing to its super large bandwidth (500 MHz - 2 GHz). Though promising, UWB sensing was only demonstrated on dedicated hardware including DW1000 and XETHRU X4 which are not available in existing consumer-level devices. In the last few years, we observed an interesting trend of UWB module being embedded into consumer-level devices such as smartphones and smart watches. However, leveraging UWB module inside consumer-level devices for sensing poses new challenges. One key challenge is that while dedicated UWB hardware can present us with raw physical-layer signal amplitude and phase, only upper-layer distance and angle information can be extracted from consumer-level devices. In this demo, we address the challenges and present the first UWB sensing system hosted on iPhone and Apple Watch without any dedicated hardware components. We show that with just the upper-layer UWB data reported from smartphones, exciting sensing applications such as fine-grained 3D handwriting and multi-target tracking can be realized, pushing RF sensing one step forward towards real-life adoption.

Besides the conventional communication function, wireless signals are actively exploited for sensing purposes recently. However, a missing component of existing wireless sensing is sensing under device motions. This is challenging because device motions can easily overwhelm target motions such as chest displacement used for respiration sensing. This paper takes a first step in the direction of involving device mobility into the ecosystem of wireless sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chip in recent years, we propose to integrate the accuracy of UWB sensing with mobility to support truly ubiquitous wireless sensing. We propose Mobi2Sense, a system design to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using commodity UWB hardware. Real-world applications demonstrate that even in the presence of device motions, fine-grained Mobi2Sense is able to capture subtle target motions to "hear" music, "see" human respiration, and "recognize" multi-target gestures at a high accuracy.

Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well being, such as mental health and chronic care. As the objectives of mobile sensing could be eitherpersonalized medicine for individualsorpublic health for populations, in this work, we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms—1)personal sensingand 2)crowdsensingparadigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such aswearable sensors,mobility monitoring,mobile data offloading, andcloud-based data analyticsto collect and process sensing data from individuals, we present two novel taxonomy systems based on the: 1)sensing objectives(e.g., goals of mobile health (mHealth) sensing systems and how technologies achieve the goals) and 2)the sensing systems design and implementation (D&I)(e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. By summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review thesensing objectivesandsensing systems D&Iwhile mapping the related research works onto the life-cycles of mHealth Sensing, i.e.: 1)sensing task creation and participation; 2)(health surveillance and data collection; and 3)data analysis and knowledge discovery. In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey.

Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.

Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve$9.38cm/s$, 13.42°,$31.08cm$median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

In this paper, based on the widely available GPS trajectory big data that records the driving behaviours implicitly, we propose a multi-task learning (MTL) framework called \textsf{semi-Traj2Graph} to recognize the fine-grained driving styles in the temporal dimension accurately. The MTL framework can incorporate the learning capability of graph representation in extracting high-level and interpretable features regarding complex driving behaviours and semi-supervised in exploiting unlabelled data and reducing labelling effort. More specifically, in the graph representation learning, a multi-view graph is first built to capture a more complete view of driving behaviours from the raw GPS trajectory data, then graph convolutional neural networks (Graph-CNNs) are applied. In the semi-supervised learning, a pseudo-label labelling is adopted to make use of the unlabelled data. We evaluate the proposed framework extensively based on two taxi trajectory datasets collected from the city of Beijing and Chongqing, China, respectively. Experimental results show that \textsf{semi-Traj2Graph} outperforms compared to other baselines, achieving an overall accuracy of around 90\%. We also implement the framework on users' smartphones via the collaborative cloud-edge computation manner to demonstrate the system usability in real case

Most of current urban logistic systems fail to strike a nice trade-off between speed and cost. An express logistic service often implies a high delivery cost. Crowdsourced logistics is a promising solution to alleviating such contradiction. In this article, we propose a new form of crowdsourced logistics that organizes passengers and packages in a shared room, i.e., using taxis that are already transporting passengers as package hitchhikers to achieve on-time deliveries. It is well-recognized that taxi drivers are good at delivering passengers to their destinations efficiently. As a result, the proposed new urban logistics system has potentials to lower the cost and accelerate package deliveries simultaneously. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express service. In the first phase, we mine the historical taxi GPS trajectory dataofflineto build the package transport network. In the second phase, we develop anonlinetaxi scheduling algorithm to adaptively discover the path with the maximum arriving-on-time probability “on-the-fly” upon real-time passenger-sending requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be successfully delivered daily on time with the success rate over 94 percent.

The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi proves the effectiveness of their new service model – using mobile apps and dynamic pricing to coordinate between drivers, passengers and the service provider, to manipulate the supply and demand, and to improve service responsiveness as well as quality. Despite its success, dynamic pricing creates a new problem for drivers: how to seek for passengers to maximize revenue under dynamic prices. Seeking route recommendation has already been studied extensively in traditional taxi service, but most studies do not consider the effects of taxis and passengers on the seeking taxi simultaneously. Further, in RoD service it is necessary to consider more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we employ a force-directed approach to model, by analogy, the relationship between vacant cars and passengers as that between positive and negative charges in electrostatic field. We extract features from multi-source urban data to describe dynamic prices, the status of RoD, taxi and public transportation services, and incorporate them into our model. The model is then used in route recommendation in every intersection so that a driver in a vacant RoD car knows which road segment to take next. We conduct extensive experiments based on our multi-source urban data, including RoD service operational data, taxi GPS trajectory data and public transportation distribution data, and results not only show that our approach outperforms existing baselines, but also justify the need to incorporate multi-source urban data and dynamic prices.

Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96 percent recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions.

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourcing scenario, where a mobile crowdsourcing task is too complex (e.g., post-earthquake recovery, citywide package delivery) but can be divided into a number of easier subtasks, which have interdependency between them. Under this scenario, we investigate an important problem, namelytask graph scheduling in mobile crowdsourcing(TGS-MC), which seeks to optimize a compact scheduling, such that the task completion time (i.e., makespan) and overall idle time are simultaneously minimized with the consideration of worker reliability. We analyze the complexity and NP-complete of the TGS-MC problem, and propose two heuristic approaches, including BFS-based dynamic priority schedulingBFSPriDalgorithm, and an evolutionary multitasking-basedEMTTSchalgorithm, to solve our problem from local and global optimization perspective, respectively. We conduct extensive evaluation using two real-world data sets, and demonstrate superiority of our proposed approaches.

Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15 percent, when the number of subjects is 50.

Sleep monitoring is essential to people's health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi-based sensing, this article proposes a low-cost and nonintrusive sleep monitoring system using commodity Wi-Fi devices, namely, WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.

In mobile crowdsourcing, organizers usually need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of users' locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. More specifically, we make participants obfuscate their reported locations under the guarantee of two rigorous privacy-preserving schemes, differential and distortion privacy, without the need to involve any third-party trusted entity. In order to achieve optimal task allocation with the differential-and-distortion geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraints of differential and distortion privacy. Moreover, a worker may be willing to accept multiple tasks, and a task organizer may be concerned with multiple utility objectives such as task acceptance ratio in addition to travel distance. Against this background, we also extend our solution to the multi-task allocation and multi-objective optimization cases. Evaluation results on both simulation and real-world user mobility traces verify the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47 percent shorter average travel distance on real-world data under the same level of privacy protection.

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

The surging traffic volumes and dynamic user mobility patterns pose great challenges for cellular network operators to reduce operational costs and ensure service quality. Cloud-radio access network (C-RAN) aims to address these issues by handling traffic and mobility in a centralized manner, separating baseband units (BBUs) from base stations (RRHs) and sharing BBUs in a pool. The key problem in C-RAN optimization is to dynamically allocate BBUs and map them to RRHs under cost and quality constraints, since real-world traffic and mobility are difficult to predict, and there are enormous numbers of candidate RRH-BBU mapping schemes. In this work, we propose a data-driven framework for C-RAN optimization. First, we propose a deep-learning-based Multivariate long short term memory (MuLSTM) model to capture the spatiotemporal patterns of traffic and mobility for accurate prediction. Second, we formulate RRH-BBU mapping with cost and quality objectives as a set partitioning problem, and propose a resource-constrained label-propagation (RCLP) algorithm to solve it. We show that the greedy RCLP algorithm is monotone suboptimal with worst-case approximation guarantee to optimal. Evaluations with real-world datasets from Ivory Coast and Senegal show that our framework achieves a BBU utilization above 85.2 percent, with over 82.3 percent of mobility events handled with high quality, outperforming the traditional and the state-of-the-art baselines.

Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data.

Gait recognition enables many potential applications requiring identification. Wi-Fi-based gait recognition is predominant because of its noninvasive and ubiquitous advantages. However, since the gait information changes with the walking direction, the existing Wi-Fi-based gait recognition systems require the subject to walk along a predetermined path. This direction dependence restriction impedes Wi-Fi-based gait recognition from being widely used. In order to address this issue, a direction-independent gait recognition system, called WiDIGR is proposed. WiDIGR can recognize a subject through the gait no matter what straight-line walking path it is. This relaxes the strict constraint of the other Wi-Fi-based gait recognition. Specifically, based on the Fresnel model, a series of signal processing techniques are proposed to eliminate the differences among induced signals caused by walking in different directions and generate a high-quality direction-independent signal spectrogram. Furthermore, effective features are extracted both manually and automatically from the direction-independent spectrogram. The experimental results in a typical indoor environment demonstrate the superior performance of WiDIGR, with mean accuracy ranging from 78.28% for a group of six subjects to 92.83% for a group of three.

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and infer urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we propose a novel location obfuscation mechanism combining E-differential-privacy and δ-distortion-privacy in Sparse MCS. More specifically, differential privacy bounds adversaries' relative information gain regardless of their prior knowledge, while distortion privacy ensures that the expected inference error is larger than a threshold under an assumption of adversaries' prior knowledge. To reduce the data quality loss incurred by location obfuscation, we design a differential-and-distortion privacy-preserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function. The linear program aims to minimize the uncertainty in data adjustment under the constraints of E-differential-privacy, δ-distortion-privacy, and evenly-distributed obfuscation. We also design an approximated method to reduce the required computation resources. Third, we propose an uncertainty-aware inference algorithm to improve the inference accuracy for the obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to the state-of-the-art methods with the same level of privacy protection; the approximated method incurs <; 3% additional quality loss than the optimal method, but only needs <; 1% of the computation time.

Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers' side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability-to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers' revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers' revenue by offering useful guidance.

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.

Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users’ preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low- and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models.

Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with motor neurone disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most of the patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus, we propose to develop a Morse code-based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the channel state information (CSI) between two antennas to enhance the signal to noise ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.

Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.

Mobile crowdsensing (MCS) is a new paradigm to collect sensing data and infer useful knowledge over a vast area for numerous monitoring applications. In urban environments, as more and more applications need to utilize multi-source sensing information, it is almost indispensable to develop a generic mechanism supporting multiple concurrent MCS task assignment. However, most existing multi-task assignment methods focus on homogeneous tasks. Due to the diverse spatiotemporal task requirements and sensing contexts, MCS tasks often differ from each other in many aspects (e.g., spatial coverage, temporal interval). To this end, in the paper, we present and formalize an important Heterogeneous Multi-Task Assignment (HMTA) problem in mobile crowdsensing systems, and try to maximize data quality and minimize total incentive budget. By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, we propose a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool. Finally, in order to improve the assignment search efficiency, a decomposition-and-combination framework is devised to accommodate large-scale problem scenario. We evaluate our approach extensively using two large-scale real-world data sets. The experimental results validate the effectiveness and efficiency of our proposed approach.

This paper builds a Food Delivery Network (FooDNet in short) using spatial crowdsourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.

This paper proposes a novel task allocation framework, PSTasker, for participatory sensing (PS), which aims to maximize the overall system utility on PS platform by coordinating the allocation of multiple tasks. While existing studies mainly optimize the task allocation from the perspective of the task organizer (e.g., maximizing coverage or minimizing incentive cost), PSTasker further considers diverse factors on the participants' side, including user work bandwidth, user availability, devices' sensor configuration, task completion likelihood, and mobility pattern. Furthermore, by considering the heterogeneity in three dimensions (i.e., task, time, and space), it adopts a novel model to measure task sensing quality and overall system utility. In PSTasker, it first calculates the utlity of a given task allocation plan by jointly fusing different participant-side factors into one unified estimation function, and then employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces demonstrate that PSTasker outperforms the baseline methods under various settings.

Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.

With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.

Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed. Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i.e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of quality. To address this issue, this paper proposes a Deep Reinforcement learning based Cell selection mechanism for Sparse MCS, called DR-Cell. We properly model the key concepts in reinforcement learning including state, action, and reward, and then propose to use a deep recurrent Q-network for learning the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Experiments on various real-life sensing datasets verify the effectiveness of DR-Cell over the state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15% of sensed cells with the same data inference quality guarantee.

Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and user's mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this paper, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation) , combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.

Task allocation is a fundamental research issue in mobile crowd sensing. While earlier research focused mainly on single tasks, recent studies have started to investigate multi-task allocation, which considers the interdependency among multiple tasks. A common drawback shared by existing multi-task allocation approaches is that, although the overall utility of multiple tasks is optimized, the sensing quality of individual tasks may become poor as the number of tasks increases. To overcome this drawback, we re-define the multi-task allocation problem by introducing task-specific minimal sensing quality thresholds, with the objective of assigning an appropriate set of tasks to each worker such that the overall system utility is maximized. Our new problem also takes into account the maximum number of tasks allowed for each worker and the sensor availability of each mobile device. To solve this newly-defined problem, this paper proposes a novel multi-task allocation framework named MTasker. Different from previous approaches which start with an empty set and iteratively select task-worker pairs, MTasker adopts a descent greedy approach, where a quasi-optimal allocation plan is evolved by removing a set of task-worker pairs from the full set. Extensive evaluations based on real-world mobility traces show that MTasker outperforms the baseline methods under various settings, and our theoretical analysis proves that MTasker has a good approximation bound.

This paper proposes a novel multi-task allocation framework, named PSAllocator, for participatory sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.

It has been well recognized that network transmission constitutes a large portion of smartphone energy consumption, mainly because of the tail energy caused by cellular network interface. Traffic offloading has been proposed to reduce energy by letting a smartphone offload network traffic to its neighbors in vicinity via low-power direct connections (e.g., WiFi Direct or Bluetooth). Our experiments conducted in a realistic environment reveal that energy efficiency cannot be improved or even deteriorates without a carefully designed offloading strategy. In this paper, we propose a selective traffic offloading scheme implemented as a smartphone middleware in a software-defined fashion, which consists of a packet classifier and a traffic scheduler. Using a light-weight machine learning approach exploiting unique smartphone context information, the packet classifier identifies packets generated on the fly as offloadable or not with substantially improved efficiency and feasibility on resource limited smartphones compared to traditional approaches. Both testbed and simulation based experiments are conducted and the results show that our proposal always attains the superior performance on a number of comparison metrics.

This paper builds a Food Delivery Network (FooDNet) that investigates the usage of urban taxis to support on demand take-out food delivery by leveraging spatial crowdsourcing. Unlike existing service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, we study the food delivery problem in association with the Opportunistic Online Takeout Ordering & Delivery service (O-OTOD). Specifically, the food is delivered incidentally by taxis when carrying passengers in the O-OTOD problem, and the optimization goal is to minimize the number of selected taxis to maintain a relative high incentive to the participated drivers. The two-stage method is proposed to solve the problem, consisting of the construction algorithm and the Large Neighborhood Search (LNS) algorithm. Preliminary experiments based on real-world taxi trajectory datasets verify that our proposed algorithms are effective and efficient.

Sensory data are often of low quality, for example, data are incomplete, ambiguous, or indirect, which has become the bottleneck of many data-driven applications. Two kinds of data which are handled in the paper for estimating traffic volumes on highways are no exception. In particular, the traffic volume data obtained from the loop detectors are accurate but sparse, and the mobile signaling data for estimating relative traffic volumes are wide in coverage and low in cost, but they are indirect and inaccurate. Keeping the characteristics of data in mind, the paper proposes a data fusion approach named Polaris which extends compressive sensing to estimate traffic volumes on highways. The Polaris analyzes the sparsity of the traffic volumes reported by detectors, mines the spatial-temporal correlations between the two kinds of data, and then gives the computational steps in the light of compressive sensing. Experiments are conducted on the large-scale real signaling data and the loop detector data. The experimental results show that the Polaris has the lowest estimation errors in comparison with several other methods. The corresponding Polaris system has been built and deployed in Fujian Province, China. It can obtain real-time traffic volumes on the highways with full coverage at a very low cost.1

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.

Understanding the irregular crowd movement and social activities caused by urban events such as city festivals and concerts can benefit event management and city planning. Although various urban data can be exploited to detect such irregularities, the crowd mobility data (e.g., bike trip records) are usually in a mixed state with several basic patterns (e.g., eating, working, and recreation), making it difficult to separate concurrent events happening in the same region. The social activity data (e.g., social network check-ins) are usually oversparse, hindering the fine-grained characterization of urban events. In this paper, we propose a tensor cofactorization-based data fusion framework for fine-grained urban event detection and characterization leveraging crowd mobility data and social activity data. First, we adopt a nonnegative tensor cofactorization approach to decompose the crowd mobility tensor into several basic patterns, with the help of the auxiliary social activity tensor. We then use a multivariate-outlier-detection-based method to identify irregularities from the decomposed basic patterns and aggregate them to detect and characterize the associated urban events. We evaluate the performance of our framework using real-world bike trip data and check-in data from New York City and Washington, DC, respectively. Results show that by fusing the two types of urban data, our method achieves fine-grained urban event detection and characterization in both cities and consistently outperforms the baselines.

Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin-Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

Visual crowdsensing is successfully applied in numerous application areas, yet little work has been done on measuring and improving the quality of worker contributed visual data. Rather than evaluating the visual quality based on traditional metrics such as resolution, we focus on data diversity, which is crucial for a broad stream of visual crowdsensing tasks. Two representative diversity-oriented task types are studied, namely static object imagery and evolving event photography. The former aims to collect multi-facet/ aspect yet low redundant data about a stationary object, while the latter wants to detect and collect details of key scenes throughout an event. We link these quality needs with data utility and propose a unified visual crowdsensing framework called UtiPay. Data utility is characterized by the macro and micro diversity needs: at the macro level, the pyramid-tree approach is proposed for multi-attribute-based data grouping; at the micro level, we use several strategies for intra-group data selection and worker contribution measurement. To study the impact of our proposed utility measurement approaches, we propose two utility-enhanced payment schemes as incentive mechanisms: Uti and Uti-Bid. Experiments over several user studies with a total of 43 subjects validate the performance of UtiPay for measuring and enhancing the data quality of visual crowdsensing tasks.

This paper presents the design and implementation of RT-Fall, a real-time, contactless, low-cost yet accurate indoor fall detection system using the commodity WiFi devices. RT-Fall exploits the phase and amplitude of the fine-grained Channel State Information (CSI) accessible in commodity WiFi devices, and for the first time fulfills the goal of segmenting and detecting the falls automatically in real-time, which allows users to perform daily activities naturally and continuously without wearing any devices on the body. This work makes two key technical contributions. First, we find that the CSI phase difference over two antennas is a more sensitive base signal than amplitude for activity recognition, which can enable very reliable segmentation of fall and fall-like activities. Second, we discover the sharp power profile decline pattern of the fall in the time-frequency domain and further exploit the insight for new feature extraction and accurate fall segmentation/detection. Experimental results in four indoor scenarios demonstrate that RT-fall consistently outperforms the state-of-the-art approach WiFall with 14 percent higher sensitivity and 10 percent higher specificity on average.

In mobile crowdsensing (MCS), one of the participants' main concerns is the cost for 3G data usage, which affects their willingness to participate in a crowdsensing task. In this paper, we present the design and implementation of an MCS data uploading mechanism-ecoSense-to help reduce additional 3G data cost incurred by the whole crowd of sensing participants. By considering the two most common real-life 3G price plans-unlimited data plan (UnDP) and pay as you go (PAYG), ecoSense partitions all the users into two groups corresponding to these two price plans at the beginning of each month, with the objective of minimizing the total refunding budget for all participants. The partitioning is based on predicting users' mobility patterns and sensed data size. The ecoSense mechanism is designed inspired by the observation that during the data uploading cycles, UnDP users could opportunistically relay PAYG users' data to the crowdsensing server without extra 3G cost, provided the two types of users are able to “meet” on a common local cost-free network (e.g., Bluetooth or WiFi direct). We conduct our experiments using both the Massachusetts Institute of Technology reality mining and the Small World In Motion (SWIM) simulation data sets. Evaluation results show that ecoSense could reduce total 3G data cost by up to ~50%, when compared to the direct-assignment method that assigns each participant to UnDP or PAYG directly according to the size of her sensed data.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes.

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).

Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.

Despite its importance, walking direction is still a key context lacking a cost-effective and continuous solution that people can access in indoor environments. Recently, device-free sensing has attracted great attention because these techniques do not require the user to carry any device and hence could enable many applications in smart homes and offices. In this paper, we present WiDir, the first system that leverages WiFi wireless signals to estimate a human's walking direction, in a device-free manner. Human motion changes the multipath distribution and thus WiFi Channel State Information at the receiver end. WiDir analyzes the phase change dynamics from multiple WiFi subcarriers based on Fresnel zone model and infers the walking direction. We implement a proof-of-concept prototype using commercial WiFi devices and evaluate it in both home and office environments. Experimental results show that WiDir can estimate human walking direction with a median error of less than 10 degrees.

With the widespread adoption of smartphones, we have observed an increasing popularity of Location-Based Services (LBSs) in the past decade. To improve user experience, LBSs often provide personalized recommendations to users by mining their activity (i.e., check-in) data from location-based social networks. However, releasing user check-in data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users' check-in data. In this paper, we propose PrivCheck, a customizable and continuous privacy-preserving check-in data publishing framework providing users with continuous privacy protection against inference attacks. The key idea of PrivCheck is to obfuscate user check-in data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which ensures the utility of the obfuscated data to empower personalized LBSs. Since users often give LBS providers access to both their historical check-in data and future check-in streams, we develop two data obfuscation methods for historical and online check-in publishing, respectively. An empirical evaluation on two real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized LBSs.

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and make inference on urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we adopt e-differential-privacy in Sparse MCS to provide a theoretical guarantee for participants' location privacy regardless of an adversary's prior knowledge. Furthermore, to reduce the data quality loss caused by differential location obfuscation, we propose a privacypreserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function, which aims to minimize the uncertainty in data adjustment. We also propose a fast approximated variant. Third, we propose an uncertaintyaware inference algorithm to improve the inference accuracy of obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to existing differential privacy methods.

For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants' mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

This paper first defines a novel spatial-temporal coverage metric, k-depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and k-depth coverage objectives/ constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals-i.e., Goal. 1 near-maximal k-depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined k-depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher k-depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same k-depth coverage constraint.

Mobile video chat apps offer users an approachable way to communicate with others. As high-speed 4G networks are being deployed worldwide, the number of mobile video chat app users increases. However, video chatting on mobile devices brings users financial concerns, since streaming video demands high bandwidth and can use up a large amount of data in dozens of minutes. Lowering the bandwidth usage of mobile video chats is challenging since video quality may be compromised. In this paper, we attempt to tame this challenge. Technically, we propose a context-aware frame rate adaption framework, named low-bandwidth video chat (LBVC). It follows a sender-receiver cooperative principle that smartly handles the tradeoff between lowering bandwidth usage and maintaining video quality. We implement LBVC by modifying an open-source app–Linphone– and evaluate it with both objective experiments and subjective studies.

Frequent route is an important individual outdoor behavior pattern that many trajectory-based applications rely on. In this paper, we propose a novel framework for extracting frequent routes from personal GPS trajectories. The key idea of our design is to accurately detect road corners and utilize these new metaphors to tackle the problem of frequent route extraction. Concretely, our framework contains three phases: 1) characteristic point (CP) extraction; 2) corner detection; and 3) trajectory mapping. In the first phase, we present a linear fitting-based algorithm to extract CPs. In the second phase, we develop a multiple density level DBSCAN (density-based spatial clustering of applications with noise) algorithm to locate road corners by clustering CPs. In the third phase, we convert each trajectory into an ordered sequence of road corners and obtain all routes that have been traversed by an individual for at least ${F}$ (frequency threshold) times. We evaluate the framework using real-world trajectory datasets of individuals for one year and the experimental results demonstrate that our framework outperforms the baseline approach by 7.8% on average in terms of precision and 21.9% in terms of recall.

Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors for maximizing the utility of such systems is placing bike stations at locations that can best meet users' trip demand. Traditionally, urban planners rely on dedicated surveys to understand the local bike trip demand, which is costly in time and labor, especially when they need to compare many possible places. In this paper, we formulate the bike station placement issue as a bike trip demand prediction problem. We propose a semi-supervised feature selection method to extract customized features from the highly variant, heterogeneous urban open data to predict bike trip demand. Evaluation using real-world open data from Washington, D.C. and Hangzhou shows that our method can be applied to different cities to effectively recommend places with higher potential bike trip demand for placing future bike stations.

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing applications. In this paper, we leverage the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated (corresponding to budget), yet ensuring the data quality. Specifically, we propose a novel framework called CCS-TA, combining the state-of-the-art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-areas under a probabilistic data accuracy guarantee. Evaluations on real-life temperature and air quality monitoring datasets show the effectiveness of CCS-TA. In the case of temperature monitoring, CCS-TA allocates 18.0-26.5% fewer tasks than baseline approaches, allocating tasks to only 15.5% of the sub-areas on average while keeping overall sensing error below 0.25°C in 95% of the cycles.

The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called "Douban Events", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.

This paper proposes a novel task allocation framework, CrowdTasker, for mobile crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model, and aims to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint. In order to achieve this goal, CrowdTasker first predicts the call and mobility of mobile users based on their historical records. With a flexible incentive model and the prediction results, CrowdTasker then selects a set of users in each sensing cycle for PCS task participation, so that the resulting solution achieves near-maximal coverage quality without exceeding incentive budget. We evaluated CrowdTasker extensively using a large-scale real-world dataset and the results show that CrowdTasker significantly outperformed three baseline approaches by achieving 3%-60% higher coverage quality.

Location prediction based on cellular network traces has recently spurred lots of attention. However, predicting user mobility remains a very challenging task due to the fuzziness of human mobility patterns. Our preliminary study included in this paper shows that there is a strong correlation between the calling patterns and co-cell patterns of users (i.e., co-occurrence in the same cell tower at the same time). Based on this finding, we propose NextCell—a novel algorithm that aims to enhance the location prediction by harnessing the social interplay revealed in cellular call records. Moreover, our proposal removes the assumption held in previous schemes that binds locations of cell towers to concrete physical coordinates, e.g., GPS coordinates. We validate our approach with the MIT Reality Mining dataset that involves 32,579 symbolic cell tower locations and 350,000 hours of continuous activity information. Experimental results show that NextCell achieves higher precision and recall than the state-of-the-art schemes at cell tower level in the forthcoming one to six hours.

Participatory sensing [Burke 2006] is an emerging computing paradigm that tasks everyday mobile devices to form participatory sensor networks.It allows the increasing number of mobile phone users to share local knowledge acquired by their sensorenhanced devices, such as monitoring of pollution or noise levels and traffic conditions.The sensing data from volunteer contributors can be further analyzed and processed to form crowd intelligence [Zhang et al. 2011], which can be elaborated into three dimensions: personal awareness, social awareness, and urban awareness.Layered on these concepts, we have raised the new term mobile crowd sensing and computing (MCSC) to characterize crowd intelligence extraction from large-scale and heterogeneous usercontributed data [Guo et al. 2014].A formal definition of MCSC is as follows: a new sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices, then aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery.It has the following three features compared to participatory sensing:-MCSC leverages both sensed data from mobile devices (from the physical space) and user-contributed data from mobile social network services (from the cyber space).In other words, MCSC counts both explicit and implicit user participation for data collection.-Having both online and offline user-contributed data, MCSC highlights the usage of heterogeneous crowdsourced data for crowd intelligence extraction.

Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy—among other things—may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost “for free”: with only an insignificant additional amount of energy required to piggy-back the data—usually incoming task assignments and outgoing sensor results—on top of the call. Here, we present an Energy-Efficient Mobile Crowdsensing (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed EEMC framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66% when compared to the 3G-based solution.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,1which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.

Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd-powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging.

This paper proposes a novel mobile crowdsensing (MCS) framework called EMC3, which intends to reduce energy consumption of individual user as well as all participants in data transfer caused by task assignment and data collection of MCS tasks, considering the user privacy issue, minimal number of task assignment requirement and sensing area coverage constraint. Specifically, EMC3incorporates novel pace control and decision making mechanisms for task assignment, leveraging participants' current call, historical call records as well as predicted future calls and mobility, in order to ensure the expected number of participants to return sensed results and fully cover the target area, with the objective of assigning a minimal number of tasks. Extensive evaluation with a large-scale real-world dataset shows that EMC3assigns much less sensing tasks compared to baseline approaches, it can save 43%-68% energy in data transfer compared to the traditional 3G-based scheme.

Energy consumption and mobile data cost are two key factors affecting users’ willingness to participate in mobile crowd-sensing tasks. While data-plan (DP) users are mostly concerned with energy consumption, non-data-plan (NDP) users are more sensitive to data cost. Traditional ways of data uploading in mobile crowdsensing tasks often go to two extremes: either in real time or completely offline after the whole task is over. In this paper, we propose effSense—an energy-efficient and cost-effective data uploading framework, which utilizes adaptive uploading schemes within fixed data uploading cycles. In each cycle, effSense empowers the participants with a distributed decision making scheme to choose the appropriate timing and network to upload data. effSense reduces data cost for NDP users by maximally offloading data to Bluetooth/WiFi gateways or DP users encountered; it reduces energy consumption for DP users by piggybacking data on a call or using more energy-efficient networks rather than initiating new 3G connections. By leveraging the predictability of users’ calls and mobility, effSense selects proper uploading strategies for both user types. Our evaluation with the MIT reality mining and Nodobo datasets shows that effSense can reduce 55%–65% energy consumption for DP users, and 48%–52% data cost for NDP users, respectively, compared to traditional uploading schemes.

With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users' spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.

Offline event marketing invites people to participate in a sponsored gathering, thus allowing marketers to have face-to-face, direct, and close contact with their current and potential customers. This paper presents a framework that supports marketers in improving marketing effectiveness by carefully selecting invitees to such sponsored offline events by leveraging location-based social networks. In particular, we first transform the participant selection task into a combinatorial optimization problem. Second, we propose a marketing effect quantitative model that considers the distance and overlapping social influence. Third, we introduce algorithms to determine a participant team that can maximize the marketing effect while fulfilling the scale and item coverage constraints. We finally evaluate the effectiveness of the framework and validate the proposed marketing effect of the quantitative model with real-world data.

Traditionally, the port container throughput, a crucial measurement of regional economic development, was manually collected by port authorities. This requires a large amount of human effort and often delays publication of this important figure. In this paper, by leveraging ubiquitous positioning techniques and open data, we propose a two-phase approach to estimation of port container throughput in real-time. First, we obtain the number of container ships arriving at berth by analyzing the ships' GPS traces. Then we estimate the throughput of each ship, in terms of number of containers transshipped, by considering the ship's berthing time, capacity, length, breadth, and crane operation performance, as extracted from different data sources. Evaluation results using real-world datasets from Hong Kong and Singapore show that the proposed approach not only estimates the container throughput quite accurately, but also outperforms the baseline method significantly.

This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale real-world dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.

Human memory often fails. People are frequently beset with questions like “Who is that person? I think I met him in Tokyo last year.” Existing memory aid tools cannot well support the recall of names effectively. This paper explores the memory recall enhancement issue from the perspective of memory cue extraction and associative search, and proposes a generic methodology to extract memory cues from heterogeneous, multimodal, physical/virtual data sources. Specifically, we use the contact name recall in the academic community as the target application to showcase our proposed methodology. We further develop an intelligent social contact manager that supports 1) autocollection of rich contact data from a combination of pervasive sensors and Web data sources, and 2) associative search of contacts when human memory fails. The system is validated by testing the performance of contact data collection techniques. An empirical user study on contact memory recall is also conducted, through which several findings about contact memorizing and recall are presented. Classic cognitive psychology theories are used to interpret these findings.

Taxi GPS traces can inform us the human mobility patterns in modern cities. Instead of leveraging the costly and inaccurate human surveys about people’s mobility, we intend to explore the night bus route planning issue by using taxi GPS traces. Specifically, we propose a two-phase approach for bidirectional night bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick up/drop off and then propose effective methods to split big hot areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops, and bus operation time constraints, we derive several effective rules to build the bus route graph and prune invalid stops and edges iteratively. Based on this graph, we further develop a bidirectional probability-based spreading algorithm to generate candidate bus routes automatically. We finally select the best bidirectional bus route, which expects the maximum number of passengers under the given conditions and constraints. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set, which contains more than 1.57 million night passenger delivery trips, generated by 7600 taxis in a month.

With the recent popularity of social network services, a significant volume of heterogeneous social media data is generated by users, in the form of texts, photos, videos and collections of points of interest, etc. Such social media data provides users with rich resources for exploring content, such as looking for an interesting video or a favorite point of interest. However, the rapid growth of social media causes difficulties for users to efficiently retrieve their desired media items. Fortunately, users' digital footprints on social networks such as comments massively reflect individual's fine-grained preference on media items, that is, preference on different aspects of the media content, which can then be used for personalized social media search. In this article, we propose SESAME, a fine-grained preference-aware social media search framework leveraging user digital footprints on social networks. First, we collect users' direct feedback on media content from their social networks. Second, we extract users' sentiment about the media content and the associated keywords from their comments to characterize their fine-grained preference. Third, we propose a parallel multituple based ranking tensor factorization algorithm to perform the personalized media item ranking by incorporating two unique features, viz., integrating an enhanced bootstrap sampling method by considering user activeness and adopting stochastic gradient descent parallelization techniques. We experimentally evaluate the SESAME framework using two datasets collected from Foursquare and YouTube, respectively. The results show that SESAME can subtly capture user preference on social media items and consistently outperform baseline approaches by achieving better personalized ranking quality.

Mobile social network (MoSoN) signifies an emerging area in the social computing research built on top of the mobile communications and wireless networking. It allows virtual community formation among like minded users to share data and to organize collaborative social activities at commonly agreed upon places and times. Such an activity scheduling in real-time is non-trivial as it requires tracing multiple users' profiles, preferences, and other spatio-temporal contexts, like location, and availability. Inherent conflicts among users regarding choices of places and time slots further complicates unanimous decision making. In this paper, we propose an autonomic system for activity scheduling in MoSoN communities. Our system allows flexible activity proposition while efficiently handling the user conflicts. As evident from our simulation and testbed results and analysis, our system can schedule multiple simultaneous activities in real-time while incurring low message and time cost.

With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed. In the meantime, the diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user check-in traces at venues and user/venue attributes, we come out with a novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users. By employing both intermode and intramode features, the proposed framework is not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset.

The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly characterize individual's fine-grained preference about those places and associated entities. In this paper, we propose SEALs - a fine-grained preference-aware location search framework leveraging the crowdsourced traces in LBSNs. We first collect user check-ins and tips from Foursquare and use them as direct user feedback on locations. Second, we extract users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference. Third, we incorporate such fine-grained user preference into personalized location ranking using tensor factorization techniques. Experimental results show that SEALs can achieve better location ranking comparing to the state-of-the-art solutions.

The performance of error concealment (EC) for damaged intra-frames (or I-frames) is very important for real time video transmission via error-prone network. Conventional algorithms essentially exploit either copying or interpolation from spatial neighbors and suffer from poor visual quality. This paper proposes an intra-frame EC adaptive algorithm framework (I-ECAF) which gives priority to temporal EC as the first, spatial copying as the second and spatial interpolation as the last. I-ECAF evaluates their feasibility by measuring the temporal or spatial similarity per a recent image quality metric, i.e., the weighted peak signal-to-noise ratio (WPSNR). Demonstrated by extensive experiments, the proposed I-ECAF gains attractive concealment accuracy, an increase from 0.06dB to 1.14dB of PSNR in comparison with the algorithm embedded within JM18.0.

In the context of a social gathering, such as a cocktail party, the memorable moments are generally captured by professional photographers or by the participants. The latter case is often undesirable because many participants would rather enjoy the event instead of being occupied by the photo-taking task. Motivated by this scenario, we propose the use of a set of cameras to automatically take photos. Instead of performing dense analysis on all cameras for photo capturing, we first detect the occurrence and location of social interactions via F-formation detection. In the sociology literature, F-formation is a concept used to define social interactions, where each detection only requires the spatial location and orientation of each participant. This information can be robustly obtained with additional Kinect depth sensors. In this paper, we propose an extended F-formation system for robust detection of interactions and interactants. The extended F-formation system employs a heat-map based feature representation for each individual, namely Interaction Space (IS), to model their location, orientation, and temporal information. Using the temporally encoded IS for each detected interactant, we propose a best-view camera selection framework to detect the corresponding best view camera for each detected social interaction. The extended F-formation system is evaluated with synthetic data on multiple scenarios. To demonstrate the effectiveness of the proposed system, we conducted a user study to compare our best view camera ranking with human's ranking using real-world data.

Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick-up/drop-off, and then propose effective methods to split big “hot” areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.

introduction Introduction to the special section on intelligent systems for socially aware computing Authors: Zhiwen Yu Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile , Daqing Zhang Institute Telecom and Management SudPans, France Institute Telecom and Management SudPans, FranceView Profile , Nathan Eagle Media Lab, MIT, USA Media Lab, MIT, USAView Profile , Diane Cook Washington State University, USA Washington State University, USAView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 4Issue 3Article No.: 45pp 1–3https://doi.org/10.1145/2483669.2483678Published:01 July 2013Publication History 25citation183DownloadsMetricsTotal Citations25Total Downloads183Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC)$\geq$0.99.

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

In location-based social networks (LBSNs), users implicitly interact with each other by visiting places, issuing comments and/or uploading photos. These heterogeneous interactions convey the latent information for identifying meaningful user groups, namely social communities, which exhibit unique location-oriented characteristics. In this work, we aim to detect and profile social communities in LBSNs by representing the heterogeneous interactions with a multimodality nonuniform hypergraph. Here, the vertices of the hypergraph are users, venues, textual comments or photos and the hyperedges characterize the k -partite heterogeneous interactions such as posting certain comments or uploading certain photos while visiting certain places. We then view each detected social community as a dense subgraph within the heterogeneous hypergraph, where the user community is constructed by the vertices and edges in the dense subgraph and the profile of the community is characterized by the vertices related with venues, comments and photos and their inter-relations. We present an efficient algorithm to detect the overlapped dense subgraphs, where the profile of each social community is guaranteed to be available by constraining the minimal number of vertices in each modality. Extensive experiments on Foursquare data well validated the effectiveness of the proposed framework in terms of detecting meaningful social communities and uncovering their underlying profiles in LBSNs.

With more and more elders in most countries, the health and safety issues become the primary concern for elderly people because ageing is often associated with physical and cognitive impairments. In order to provide intelligent assistive services for elders, in this paper, we propose an integrated service platform that can accommodate safety assurance, daily activity assistance, and health support services. Specifically, the platform takes care of anomalous events detection, daily activities tracking, and health status monitoring leveraging stationary sensors deployed in living environments and mobile sensing artifacts carried by elders. The services in this integrated platform can ensure safety and independency of elders, they can also help them sustain and/or improve health condition with personalized health support. Based on the proposed platform, we present a case study of assistive safety assurance service wandering detection, which monitors an elder's outdoor movements by using GPS sensor embedded in smart phone and recognizes the lapping or pacing movement pattern to find potential wandering behavior. The experimental results show that our method works well in detecting wandering behavior in terms of detection rate and false alarm rate.

Crowd sourcing is a new paradigm of service provision. Current commercial crowd sourcing platforms rarely consider the interaction between task takers, which is extremely required in the disaster management scenario. In this paper, we designed a framework for community based crowd sourcing, i.e., task takers are from an existing community or will easily form a new community. A size-specified community creation method using multiple social contexts is also proposed.

GPS enabled taxis provide us not only their locations and passenger status information in real-time, but also historical digital traces which could enable innovative services such as smart taxi dispatching, anomalous taxi detection, urban hotspots discovery, bus planning, and so on. Even though researchers have developed algorithms and systems in previous work targeting specific applications, there is no study on a general service framework that can integrate a scalable set of smart taxi services. In this paper, by identifying the potential services and applications in the previous research, we propose an integrated OSGi-based Smart Taxi Service Platform for taxi passengers, drivers, operators as well as the urban planners. By leveraging the component-based architecture of OSGi service framework, the proposed taxi service platform is highly scalable and reusable. We use the taxi fraud detection service as an example to show the reusability of the software components in the service platform, we further evaluate the system performance and response time using a large set of taxi GPS logs and verify the effectiveness of the platform.

The Internet of Things (IoT) is a technical revolution that represents the future of computing and communications. Under its vision, the next-generation Internet will promote the harmonious interaction between human, society, and smart things. The current research in IoT is mainly from the perspective of connecting and managing things. The humanized, social side of IoT, however, is still not explored. In this article, we intend to present the IoT from the human-centric perspective. By analyzing the tight-coupled relationship between human and opportunistic connection of smart things (e.g., mobile phones, vehicles), we propose Opportunistic IoT. It enables information sharing and dissemination within/among opportunistic communities that are formed with the movement and opportunistic contact nature of human. We characterize the bi-directional effect between human and opportunistic IoT, present the innovative application areas, and discuss the challenges raised by this new computing paradigm.

Wandering is among the most frequent, problematic, and dangerous behaviors for elders with dementia. Frequent wanderers likely suffer falls and fractures, which affect the safety and quality of their lives. In order to monitor outdoor wandering of elderly people with dementia, this paper proposes a real-time method for wandering detection based on individuals' GPS traces. By representing wandering traces as loops, the problem of wandering detection is transformed into detecting loops in elders' mobility trajectories. Specifically, the raw GPS data is first preprocessed to remove noisy and crowded points by performing an online mean shift clustering. A novel method called θ_WD is then presented that is able to detect loop-like traces on the fly. The experimental results on the GPS datasets of several elders have show that the θ_WD method is effective and efficient in detecting wandering behaviors, in terms of detection performance (AUC > 0.99, and 90% detection rate with less than 5 % of the false alarm rate), as well as time complexity.

More and more location-based services become relying on the logical notion of a physical location, known as logical location (e.g. Starbucks, KFC). In this paper, we propose a new way to identify logical location using (1) a GPS-enabled mobile phone and (2) a wearable camera embedded in user's glasses. When a user with a wearable camera is detected paying attention to a certain physical location, all the logical locations within the error range of the GPS coordinates are considered as the matched candidates. We select the representative frames in the video stream corresponding to user's interested location in real-time and use multi-view images taken beforehand to represent each logical location. We then extract the Scale Invariant Feature Transform visual features from both the representative video frames and pre-stored images of candidate logical locations for video-image matching, the logical location that the user pays attention to can thus be identified. In order to differentiate the cases where users watch certain objects rather than a logical location in the street, we use Support Vector Machine to classify the two cases so that only the valid logical location is identified. Our proposed approach is proved weather and user independent, and it does not request additional user efforts compared with previous solutions. The results tested using a real-world dataset can achieve an average accuracy of 91.08%.

Social and Community Intelligence (SCI) represents an emerging area that aims at revealing individual/group behaviors, social interactions as well as community dynamics by mining the digital traces left by people while interacting with cyber-physical spaces. The digital traces are generated mainly from three information sources: Internet and Web applications, static infrastructure, mobile devices and wearable sensors. In this workshop we hope to get people from different disciplines together to share their visions and insights on how to tackle the challenges faced by SCI, such as participatory sensing, heterogeneous data fusion, intelligence extraction, privacy issues, and so on.

GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection.

One of the key objectives of a pervasive computing system is to provide appropriate support to enable the user to manage the increasingly complex environment surrounding her. This includes managing the ever-increasing number of devices which can be accessed wirelessly as well as the vast range of services at her disposal. The aim of the Persist project was to develop a pervasive system that would bridge the gap between fixed smart spaces (e.g. smart homes) and systems created for mobile users. Using the concept of Personal Smart Spaces the Persist project has built a prototype system to demonstrate some of the capabilities that this can provide. The Societies project is currently building on these ideas to develop a new type of system that combines pervasive with social networking functionality. Personalisation is an essential feature of any pervasive system and plays a key role in the prototype implemented in Persist. This will also play a key role in the new platform being developed in the Societies project. This paper describes how personalisation is handled within the Persist system and some ideas for the new platform.

The ability to leverage the power of a network of social contacts is important to get things done. However, as the number of contacts increases, people often find it difficult to maintain their contact network by using merely memory, and are frequently encompassed with questions like "who is that person, I met him in Tokyo last year". Existing contact tools make up for the shortage of unreliable human memory by storing contact information in the digital format, but laying much burden on users on manually inputting contact data. This paper, however, presents a social contact management system called SCM, which supports the auto-collection of rich contact data by exploring the aggregated power of pervasive sensing and Web intelligence techniques. Regarding that people often need to leverage several associated things (e.g., meeting location) to fetch other information about a contact (e.g., his name), we also develop an associative contact retrieval method. The effectiveness and runtime performance of our system is validated through a set of experiments.

International audience

Elderly in-home assistance (EHA) has traditionally been tackled by human caregivers to equip the elderly with homecare assistance in their daily living. The emerging ambience intelligence (AmI) technology suggests itself to be of great potential for EHA applications, owing to its effectiveness in building a context-aware environment that is sensitive and responsive to the presence of humans. This paper presents a case-driven AmI (C-AmI) system, aiming to sense, predict, reason, and act in response to the elderly activities of daily living (ADLs) at home. The C-AmI system architecture is developed by synthesizing various sensors, activity recognition, case-based reasoning, along with EHA-customized knowledge, within a coherent framework. An EHA information model is formulated through the activity recognition, case comprehension, and assistive action layers. The rough set theory is applied to model ADLs based on the sensor platform embedded in a smart home. Assistive actions are fulfilled with reference to a priori case solutions and implemented within the AmI system through human–object–environment interactions. Initial findings indicate the potential of C-AmI for enhancing context awareness of EHA applications.

In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer- to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation.

Elders with mild Dementia exhibit impairments of memory, thought and reasoning. It has been recognized that pervasive computing technologies can assist those suffering from mild Dementia to improve their level of independence and quality of life through cognitive reinforcement. In this paper, we present a user-centred design approach, which is based on the iterative process of user study, prototyping, user test and evaluation, to achieve the goal of developing a cost-effective cognitive prosthetic device with associated services for elders with mild Dementia. Specifically, we describe the results of user study in three different test sites, four areas of cognitive reinforcement have been identified to assist their independent living. Of different assistive services, we choose two context-aware reminding services as a case study to illustrate how to deploy pervasive computing techniques in the system design. Finally, we present the overall system architecture and initial system implementation with first trial results.

With mobile devices and wireless hotspots becoming more prevalent, customers can desire greater access to media and services that can be achieved from the marrying of these two technologies. However, often existing devices need to be augmented with hardware and software to achieve this connectivity, and the existing services are heterogeneous and incompatible with one another. By leveraging the captive portal mechanism and proposed context-aware technologies, we propose a lightweight framework that can aggregate these services in smart spaces, and allow mobile users to spontaneously discover them without any specialized installation. In this paper, we present the system requirements for spontaneous interaction, the service framework design and implementation details.

People have the desire to be always informed by the information to their importance everywhere and anytime. However, for some casual and relaxing occasions such as in home environment, information access and exhibition should be offered in a non-distracting and non-disruptive manner. To facilitate this goal and make use of embedded displays in smart home environments, this paper proposes a framework, i.e., context-aware informative display which offers a novel design for information representation catering for home and shared space users. The system architecture and techniques used are illustrated in details. Lastly, a prototype with several user cases is described to prove the workability of the framework.

This paper proposes a two-tier semantic peer-to-peer network that facilitates efficient search for context information in wide-area networks. Context data with the same semantics are grouped together into a one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology-based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, we demonstrate the effectiveness of our proposed scheme.

While mobile users move from one smart space to another, it is highly desirable for them to access the right media contents from the overabundant media information in the right form with their own devices. This paper deals with two important issues in pervasive media access: one is the spontaneous media access in heterogeneous environments, the other is the context-aware media recommendation in different spaces. A general platform for spontaneous media access and context-aware media recommendation has been proposed and implemented. The proposed hybrid recommendation algorithm shows quite good performance in heterogeneous environments.

We present an object geometry based error resilience method. We extract object geometry through analyzing motion vector field at the encoder using the iterative self-organizing data analysis technique algorithm. The extracted object geometry, in the form of an index map, is then embedded in the bitstream. At the decoder side, this information is exploited to recover motion vectors of corrupted macroblocks. The proposed method has been validated in MPEG2 codec in a standard-compliant way. Experimental results carried out on several sequences have shown that the proposed method outperforms two conventional temporal error concealment methods by an average PSNR gain of 0.9 dB and 1.5 dB, respectively.

In order to facilitate rapid development of context-aware applications, there is a need for architectural support in the entire context processing flow, and improved programming abstractions that ease the prototyping. In this paper, a toolkit called Semantic Space, is proposed to support rapid prototyping of context-aware applications via a set of programming abstractions on context processing. The functionality encapsulated in the toolkit handles the common, time-consuming and low-level details in context acquisition, aggregation, storage and inference. Architectural design and implementation issues of the Semantic Space toolkit are discussed in detail. Finally, a case study on building a mobile situation aware phone is described to illustrate the validity of our approach and usability of the toolkit.

The widespread use of context information necessitates an efficient wide-area lookup service in pervasive computing. In this paper, we present semantic context space (SCS), a semantic overlay network that facilitates efficient search for context information in distributed environments. Peers in SCS are grouped based on the semantics of their local data and self-organized into a one-dimensional ring space. Context search requests are only routed to the appropriate semantic clusters, reducing unnecessary search cost on peers that have irrelevant context data, and increasing the chances that the context data will be found quickly. By exploring parallelism in a semantic cluster, search request can be found quickly. Our simulation studies demonstrate the effectiveness of SCS.

Context information produced by various interconnected sensors and software sources needs to be efficiently identified and located by a context-aware application. In this paper, we propose an inter-space context discovery platform, called Orion, which allows context to be discovered and retrieved from multiple smart spaces. Orion employs semantic overlay network peer-to-peer architecture to provide connectivity between the smart spaces, allowing lookup query to be forwarded to the destined spaces. On the other hand, to overcome data heterogeneity, semantic Web ontology-driven approach is used for the context modeling and matchmaking.

As computing technology moves towards pervasive computing, many applications are beginning to make use of context information to adapt to and respond appropriately to their environments. Such a trend necessitates efficient search for context information in wide-area networks. In this paper, we propose a semantic P2P context lookup system. Peers are grouped based on the semantics of their local data which are extracted according to a set of schemas and are self-organized as a semantic overlay network. Context search requests are only routed to the appropriate nodes that have relevant data, reducing unnecessary query traffic and increasing the chances that the context data will be found quickly. To reduce maintenance overheads incurred by high-dimensional semantic overlay networks, we propose a one-dimensional ring space to construct peers and facilitate efficient query routing. Our simulation studies demonstrate the effectiveness of our proposed routing techniques.