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.

The proliferation of hidden WiFi cameras has raised serious privacy concerns, making their accurate detection and localization essential for the secure development of future intelligent wireless networks. However, existing solutions often require substantial user involvement, large movement spaces, predefined system parameters, or pre-collected training data, limiting their practicality and scalability. In this paper, we present DiffLoc+, a novel and low-cost system that localizes hidden WiFi cameras by harnessing the fundamental physical principle of electromagnetic diffraction. When an obstacle crosses the line-of-sight path between a transmitter and a receiver, it causes a distinctive signal attenuation pattern. We theoretically analyze the feasibility of exploiting this phenomenon for localization and identify two key conditions for building an unbiased diffraction-based model: symmetry and observability. To satisfy these conditions, DiffLoc+ introduces a controllable diffraction generation mechanism that precisely rotates a small metal plate around a WiFi receiver (e.g. a Raspberry Pi), producing a stable and predictable diffraction “shadowing” effect. We then construct an unbiased localization model that maps this effect to the azimuth of the camera. To ensure the robustness of the theoretical model in real-world applications, DiffLoc+ further introduces two robustness-enhancing mechanisms: (1) an attenuation-region difference-driven subcarrier selection method, which filters subcarriers that reliably reflect the diffraction attenuation pattern by quantifying the signal contrast between diffraction- and reflection-dominated regions; and (2) an uncertainty evaluation framework that integrates result consistency and diffraction signal quality to eliminate unreliable estimates. Implemented entirely with commodity off-the-shelf (COTS) hardware, DiffLoc+ achieves an average angular error of 11.92° across six diverse indoor environments and eleven commercial camera models, demonstrating its effectiveness and robustness.

With the increasing use of electronic devices, online handwriting verification has become crucial for biometricsbased identity authentication. Traditional methods, which rely on content-dependent verification of the writer's name, are vulnerable to forgery. This paper introduces a content-independent handwriting authentication system, Ph-Wri, designed for commodity smartphones. The core innovation is a multi-path attention feature fusion network that combines both static features (image of the handwritten text) and dynamic features (time-dependent properties during writing), to abstract the handwriting style instead of specific content for recognition, enabling robust user authentication. To extract handwriting style from dynamic writing features, we propose a polarity-aware attention strategy during training. This strategy incorporates Style Channel Attention (SCA) to capture direction-sensitive stylistic features, and Trajectory Spatial Attention (TSA) to highlight key handwriting trajectory regions. In the fine-tuning stage, the Correlation-Aware Attention (CAA) module models inter-channel structural correlations, mitigating the influence of content and enhancing style-consistent representations. By linking content-independent handwriting style to user identity, the system achieves accurate authentication. Extensive experiments on both the self-built CIEHD dataset and the public BiosecurID dataset demonstrate exceptional performance, achieving a 99% Verification Accuracy on CIEHD. Compared to state-of-theart methods that utilize only static or dynamic data, Ph-Wri significantly reduces the Equal Error Rate, showcasing the effectiveness and practicality of the proposed approach.

Recent years have seen rapid advancements in ultra-wideband (UWB)-based localization systems. However, most existing solutions offer only centimeter-level accuracy and support a limited number of UWB tags, which fails to meet the growing demands of emerging sensing applications (e.g., virtual reality). This paper presents MULoc, the first system that can localize an unlimited number of UWB tags with millimeter-level accuracy. At the core of MULoc is the innovative use of UWB phase, which can provide finer-grained distance measurement than traditional time-of-f1ight (ToF) estimates. To accurately obtain phase estimates from unsynchronized devices, we introduce a novel localization scheme called anchor overhearing (AO) and eliminate raw signal errors through a signal-difference-based technique. For precise tag localization, we resolve phase ambiguity by combining a fusion-based filtering method and frequency hopping. We implement MULoc on commercial UWB modules. Extensive experiments demonstrate that our system achieves a median localization error of 0.47 mm and 90-th percentile error of 1.02 cm, reducing the error of traditional method by 91.12%.

Accurate 3D human mesh recovery from point clouds remains challenging. Most existing methods depend on full 3D supervision or complete input data, both of which are difficult to obtain in practice. %cr update This calls for robust solutions capable of handling partial point clouds in a self-supervised manner. However, the incompleteness of point clouds and the absence of supervision signals pose dual challenges. To tackle these challenges, This calls for robust solutions to handle partial point clouds in a self-supervised manner. To tackle the dual challenges of point cloud incompleteness and the absence of supervision signals, we propose a novel method named SS-HMR, which offers three key insights. First, we estimate point-wise semantics in a self-supervised manner to match partial inputs with a canonical template. The resulting correspondences serve as supervision signals for the regression network in human mesh recovery. Second, we incorporate regression-based and optimization-based paradigms into a self-improving loop: the regression network provides strong initialization for optimization, while the optimization routine generates pseudo-labels that, in turn, enhance the regression network. This mutual feedback enables more accurate and stable mesh recovery over time. Third, generating multiple initializations and selecting the best result mitigates the optimization routine's sensitivity to initialization, improving robustness to sparse and noisy data. %cr update Third, to mitigate sensitivity to initialization in the optimization routine, we generate diverse initialization candidates and transform the challenge of escaping local optima into a controllable selection task, improving robustness against sparse and noisy data. Extensive experiments are conducted on three public datasets and results demonstrate that SS-HMR outperforms existing methods. Notably, SS-HMR performs excellently on different test data, whether from original point clouds captured by depth cameras or LiDAR devices, or from noise-added ones. This shows that SS-HMR has strong generalization ability and robustness across different data sources. Codes are available at https://github.com/suchang-99/SS-HMR.

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.

Handwriting recognition systems have greatly enhanced AIoT applications, especially in human-computer interaction. Wireless-based methods, favored for their non-invasive nature and ease of deployment, are becoming more common. However, existing works, which typically depend on the user’s position, often perform poorly in varied writing positions. Additionally, they do not incorporate user identity information, which could lead to security vulnerabilities by failing to reject unauthorized users. To address these issues, this article introduces RF-Eye , a system that enables contactless, position-independent handwriting recognition and user identification without prior training. Its innovative approach uses each Radio-frequency identification (RFID) tag as a unique viewpoint for observing hand movements and employs pairs of tags to track directional changes. Specifically, building upon the signal transmission model and the Fresnel Zone, we propose a novel feature, DCG , to capture changes in gesture direction and confirm its consistency across different positions. Based on DCG , we develop unique patterns for common handwriting symbols that enhance our recognition algorithm. Moreover, to strengthen the system security, we link these patterns with distinct handwriting styles through the extraction of finer-grained features, thus, preventing the misuse of the system by unauthorized users. Extensive experiments demonstrate RF-Eye ’s efficacy, which achieves recognition accuracies of 93.5%, 95.2%, and 95.8% for 26 lowercase letters, 10 digits, and 10 graphic symbols, respectively, and identifying unauthorized users with 98.6% accuracy.

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.

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.

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.

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

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.

Existing contactless solutions on sleep respiration monitoring are either performed in controlled environments, having poor usability in practical scenarios, or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events of patients. In this paper, we propose Respnea, a non-invasive sleep respiration monitoring system using an impulse-radio ultra-wideband (IR-UWB) radar. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different states of subjects. Further, we construct a deep learning model which adopts a multi-headed self-attention and learn the patterns implicit in the respiration signal so as to distinguish sleep respiration events at a granularity of seconds. We conduct experiments on data collected from patients with sleep disorders and healthy subjects. The experimental results show that Respnea achieves a low error (less than 0.27 bpm) in respiration rate estimation and reaches the accuracy of 88.89% diagnosing the severity of Sleep Apnea-Hypopnea Syndrome.

Abstract This paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of AcousticPAD, and show that it supports 10 digits and 26 English letters recognition at high accuracies.

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.

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.

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.

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.

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.

Currently, cardiac diseases have become one of the biggest health concerns. Existing heartbeat monitoring methods either require dedicated intrusive devices (e.g., ECG devices) that suffer high costs or leverage video camera analyses that are light-sensitive. In this paper, leveraging the acoustic signals sent by a speaker and received by a microphone array, we develop a prototype system to achieve the contactless and low-cost heartbeat monitoring. In particular, while we exploit the passive beamforming to enhance the user's heartbeat signal, we design a filtering method in frequency domain to remove the line-of-sight (LoS) impact and retain the target-reflected signals, and propose a wideband time-delay method to estimate the direction of arrival of target-reflected signal. Thus, our prototype is able to robustly estimate the human heartbeat and push the limit of acoustic sensing range. The experimental results show that our prototype achieves a heart rate monitoring at 1.7 m with the estimation error of 0.5 bpm, which is comparable to ECG or other contact-based solutions.

Temperature is an important indicator for agriculture irrigation, industrial manufacture, food safety, etc. While temperature measurement can be achieved via dedicated sensors, there still have a demand to sense temperature with ubiquitous computing devices. In this paper, we propose to enable the sound signal to measure the air temperature using commodity acoustic devices. Different from existing FMCW and OFDM based acoustic sensing system, we are the first to employ acoustic pulse signal and get rid of offsets to obtain the accurate sound speed. Then we precisely obtain temperature by quantifying the relation between sound speed and temperature. We build a temperature monitoring prototype named AcousticThermo, and conduct extensive experiments. Experimental results show that the proposed system can achieve an average estimation error of below 0.2°C in various temperature environments.

Predicting the passenger flows in a city, especially in a metropolis, can guide traffic dispersion, and help assessing the risks of public safety and improving urban planning. However, it is challenging as passenger flows in a road network may vary with time and space, affected by weather conditions, urban activities, etc. In the paper, we propose a passenger flow prediction approach named Yildun, which constructs an encoder-decoder neural network and captures the spatial and temporal correlations inherent in passenger flows. More specifically, to predict the passenger flows at each and every station, a spatial attention mechanism is presented to adaptively extract inter-station correlations of flows by referring to the previous hidden state of the encoder at each time step. Meanwhile, a temporal attention mechanism is employed to capture time-dependent connections of flows by selecting relevant hidden states of the encoder across all time steps. Further, extra factors, such as POI (Point of Interest) data and day of the week, are fused in the decoder. With this spatio-temporal attention scheme, Yildun not only can make predictions effectively, but also is easily explainable. Extensive experiments are conducted on large-scale real-world data. The experimental results show that Yildun can predict passenger flows with small prediction errors and outperforms five baselines significantly.

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.

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.

Vehicles on the roads have high heterogeneity in vehicle types. Real-time and full-coverage vehicle classification has always been a challenge. Existing intrusive and non-intrusive methods cannot meet the requirements with satisfaction. Considering that signaling data from mobile operators have the advantages such as the wide coverage and the low cost, a new approach named Lepus, which analyzes the signaling stream to achieve the real-time multi-class classification of vehicles on highways, is proposed. Following the Lepus, the historical GPS trajectories with vehicle types and the signaling trajectories occurring at the same time and space are first examined to establish the relation among signaling trajectories, vehicles and vehicle types and then identify signaling-recognizable vehicles. Further, the driving characteristics of these labeled signaling-recognizable vehicles are analyzed so as to determine vehicle classification features. Finally, the vehicle classification model is established and used to analyze the incoming signaling stream and classify the vehicles in real time. Extensive experiments are conducted on real data and the results show that the Lepus approach is effective in real time vehicle classification.

Analyzing and mining trajectories of moving objects (such as persons or vehicles) in the cities bring a promising way to discover the potential knowledge and therefore can foster diversified applications, including personalized travel services, intelligent transportation systems (ITSs), and etc. For increasing the intelligence of current public transit systems, the paper proposes to discover and utilize the patterns in passenger trajectory streams to optimize bus scheduling. More specifically, the paper first analyzes the real world data from bus smart cards so as to fully understand the nature of passenger trajectories and bus operations. Based on it, the paper defines a new trip pattern, i.e., the frequent bus passenger trip pattern for bus scheduling (the FBPT4BS pattern in short). Then, the paper proposes an approach. The approach gives the procedure of discovering FBPT4BS patterns from passenger trajectory streams and finds the bus lines whose capacities are not enough to satisfy the passengers' travel demands. Further, the approach gives the suggestion on the corresponding scheduling adjustment strategy for bus lines. Experiments are conducted on the data from the Beijing Public Transport Group. The experimental results show that the proposed approach can efficiently decrease the travel times of passengers.

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

Compared to general vehicular systems, bus systems have advantages including wide coverage, fixed routes, and regular service. Inspired by these unique features of the bus systems, we propose to use the bus systems as routing backbones of VANETs. In this work, we present a Community-based Bus System (CBS) which consists of two components: a community-based backbone and a routing scheme over the backbone. The backbone construction is a one-off operation which is done offline while the routing is done online in individual buses. We build a community-based backbone by applying community detection techniques and propose a twolevel routing scheme which operates over the backbone. The proposed routing scheme performs sequentially in the inter-community level and the intra-community level, and is able to support message delivery to both buses and specific locations/areas. We develop a probabilistic model to analyze the message delivery latency of CBS. The average error of the analytically-derived latency is shown to be 8.9 percent of the latency derived from the real traces. Extensive experiments are conducted on real-world traces from the Beijing bus system and the Dublin bus system and the results show that CBS can significantly lower the delivery latency and improve the delivery ratio, compared to the existing solutions. CBS is a general solution which is applicable to any bus-based VANETs.

The newly emerging location-based social networks (LBSN) such as Tinder and Momo extends social interaction from friends to strangers, providing novel experiences of making new friends. Familiar strangers refer to the strangers who meet frequently in daily life and may share common interests; thus they may be good candidates for friend recommendation. In this paper, we study the problem of discovering familiar strangers, specifically, public transportation trip companions, and their common interests. We collect 5.7 million transaction records of smart cards from about 3.02 million people in the city of Beijing, China. We first analyze this dataset and reveal the temporal and spatial characteristics of passenger encounter behaviors. Then we propose a stability metric to measure hidden friend relations. This metric facilitates us to employ community detection techniques to capture the communities of trip companions. Further, we infer common interests of each community using a topic model, i.e., LDA4HFC (Latent Dirichlet Allocation for Hidden Friend Communities) model. Such topics for communities help to understand how hidden friend clusters are formed. We evaluate our method using large-scale and real-world datasets, consisting of two-week smart card records and 901,855 Point of Interests (POIs) in Beijing. The results show that our method outperforms three baseline methods with higher recommendation accuracy. Moreover, our case study demonstrates that the discovered topics interpret the communities very well.

With the aid of ubiquitous sensors/devices and pervasive networks, various types of multi-source data in the form of texts, videos, pictures, audios, etc. can be collected. They can be applied to detecting the traffic conditions. For highways, it has inevitable inherent defects in detecting traffic conditions by analyzing data from a single source or user participation. Therefore, the paper proposes a data fusion approach named Phecda. Phecda combines the signaling data of mobile phones with the data from static loop detectors. Phecda works in an unobtrusive way, it not only incorporates with the characteristics of traffic flows, but also includes the strategies of setting and optimizing parameters by learning historical data. Experiments are conducted with the large-scale real-world data as input. The experimental results show that the Phecda approach has high precisions and recalls in vehicle speed estimation. The corresponding Phecda system has been built and deployed in Fujian Province, China. It achieves the highway traffic monitoring with full road segment coverage at a very low cost.

Mobile crowdsensing (MCS) is a promising paradigm that utilizes the mobility of people and the sensing capabilities of their mobile devices to accomplish a variety of sensing tasks. In this paper, we adopt the Signaling System No.7 (SS7) as the MCS platform since SS7 can well capture trajectories and mobility patterns of the mobile users. We collect a real-world SS7 data of 1.18 million mobile users at 3512 cell towers/sites in Xiamen, China. We first analyze this dataset and reveal important characteristics of user mobility. Then, we address a Mobile User Recruitment (MUR) problem which is crucial to all MCS systems. Given SS7 data of mobile users, a set of target cells to be sensed/covered, and recruitment cost functions of the mobile users, the MUR problem is to recruit a set of mobile users such that all the target cells are covered and the total recruitment cost is minimized. Our MUR problem is general and includes the existing problems as its special cases. We prove NP-hardness of the problem. We propose an approximation algorithm to this problem and derive the approximation ratio. Extensive experiments are conducted on the real-world SS7 dataset and results show that the proposed solution outperforms two baseline algorithms by saving 22.6% and 62.9% recruitment costs, respectively, on average.

Bus networks in cities have distinctive features such as wide coverage and fixed bus routes so that they show the potential of forming the communication backbone in vehicular ad hoc networks (VANETs). This paper focuses on the geocast in bus-based VANETs and presents a geocast routing mechanism named Vela. Specifically, Vela analyzes and mines historical bus trajectories and characterizes spatial–temporal patterns (i.e., bus travel-time patterns and bus spatial encounter patterns) in a moderate granularity of road segments, which makes the mined patterns both accurate and steady. Furthermore, Vela exploits these acquired patterns to build a probabilistic spatial–temporal graph model and provides the available routing paths with the best possible quality-of-service levels for data delivery requests. Moreover, Vela also employs a two-hop aware strategy that utilizes the real-time spatial–temporal relationships between buses to increase the chances of forwarding the data. The results of the experiments on the real and synthetic trajectories show that Vela performs much better in terms of delivery ratio and delay and has stronger scalability than the other solutions.

Low delivery latency and high delivery ratio are two key goals in the design of routing schemes in Vehicular Ad Hoc Networks (VANETs). The existing routing schemes utilize real-time information (e.g., Geographical position and vehicle density) and historical information (e.g., Contacts of vehicles), which usually suffer from a long delivery latency and a low delivery ratio. Inspired by the unique features of bus systems such as wide coverage, fixed routes and regular service, we propose to use the bus systems as routing backbones of VANETs. In this work, we present a Community-based Bus System (CBS) which consists of two components: a community-based backbone and a routing scheme over the backbone. We collect real traces of 2515 buses in Beijing and build a community-based backbone by applying community detection techniques in the Beijing bus system. A two-level routing scheme is proposed to operate over the backbone. The proposed routing scheme performs sequentially in the inter-community level and the intra-community level, and is able to support message delivery to both mobile vehicles and specific locations/areas. Extensive experiments are conducted on the real trace data of the Beijing bus system and the results show that CBS can significantly lower the delivery latency and improve the delivery ratio. CBS is applicable to any bus-based VANETs.

The different types of applications in VANETs impose diversified traffic loads. Although multi-channel operations are applied in the MAC layer to improve throughput and potentially reduce the latency, the existing multi-channel approaches show some limitations to meet the changing demands of applications. In this paper, we present an adaptive channel coordination mechanism named Merak to enhance IEEE 1609.4 by adjusting the length ratio of CCH interval and SCH interval dynamically. To be specific, we model the channel interval allocation with a Markov decision process, and estimate the optimal channel interval for the current traffic load by employing a fuzzy actor-critic algorithm. The extensive experiments are conducted to observe Merak, the alternating access scheme in IEEE 1609.4 and the variable channel interval scheme (VCI). The experimental results indicate that Merak can adapt to the varying traffic loads better than the original IEEE 1609.4 and VCI, especially boosting the timeliness and delivery rate of burst emergent or periodic control message loads on the CCH and maintaining the satisfactory throughput and delivery rate of fluctuating service data on the SCHs.