Showing papers by "Daqing Zhang published in 2019"

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

Abstract: The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target This sensing range constraint greatly limits the application of the proposed approaches in real life This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100% We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal Correlation

Abstract: 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.

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

Abstract: The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%.1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.

Towards a Diffraction-based Sensing Approach on Human Activity Recognition

Abstract: In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.

FooDNet: Toward an Optimized Food Delivery Network Based on Spatial Crowdsourcing

Abstract: 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.

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Abstract: 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.

Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices

Abstract: 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.

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Abstract: 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.

Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors

Abstract: 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.

Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

Abstract: 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

Crowd-Powered Sensing and Actuation in Smart Cities: Current Issues and Future Directions

Abstract: With the advent of seamless connections of humans, machines, and smart things, there is an emerging trend to leverage the power of crowds (e.g., citizens, mobile devices, and smart things) to monitor what is happening in a city, understand how the city is evolving, and further take actions to enable better quality of life, which is referred to as Crowd-Powered Smart City (CPSC). In this article, we provide a literature review for CPSC and identify future research opportunities. Specifically, we first define the concepts with typical CPSC applications. Then, we present the main characteristics of CPSC and further highlight the research issues. In the end, we point out existing limitations that can inform and guide future research directions.

LungTrack: Towards Contactless and Zero Dead-Zone Respiration Monitoring with Commodity RFIDs

Abstract: Respiration rate sensing plays a critical role in elderly care and patient monitoring. The latest research has explored the possibility of employing Wi-Fi signals for respiration sensing without attaching a device to the target. A critical issue with these solutions includes that good monitoring performance could only be achieved at certain locations within the sensing range, while the performance could be quite poor at other "dead zones." In addition, due to the contactless nature, it is challenging to monitor multiple targets simultaneously as the reflected signals are often mixed together. In this work, we present our system, named LungTrack, hosted on commodity RFID devices for respiration monitoring. Our system retrieves subtle signal fluctuations at the receiver caused by chest displacement during respiration without need for attaching any devices to the target. It addresses the dead-zone issue and enables simultaneous monitoring of two human targets by employing one RFID reader and carefully positioned multiple RFID tags, using an optimization technique. Comprehensive experiments demonstrate that LungTrack can achieve a respiration monitoring accuracy of greater than 98% for a single target at all sensing locations (within 1st -- 5th Fresnel zones) using just one RFID reader and five tags, when the target's orientation is known a priori. For the challenging scenario involve two human targets, LungTrack is able to achieve greater than 93% accuracy when the targets are separated by at least 10 cm.

DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial Embeddings

Abstract: 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.

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Abstract: Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI. INDEX TERMS Crowdsourced business intelligence, consumer behaviors, competitive intelligence, crowd intelligence, commercial site recommendation, brand trending.

Multi-Dimensional Urban Sensing in Sparse Mobile Crowdsensing

Abstract: Sparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra- and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective $\langle $ cell, task $\rangle $ pairs by using reinforcement learning. To effectively capture the intra- and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms.

A real-time and robust intrusion detection system with commodity wi-fi

Abstract: Intrusion detection plays a rather important role in many applications, like asset protection and elder caring. Since we cannot make any requirements on the intruder, a device-free passive way of intrusion detection is much more promising and practical. In order to achieve robust passive intrusion detection, various techniques have been proposed, including video-based, infra-based and sensor-based approaches, among which dedicated device installation is often required. In this work, we present a real-time and robust device-free intrusion detection system, named RR-Alarm. By reusing the existing Wi-Fi signals, RR-Alarm is able to detect human intrusion in real time, at the same time, requiring no additional facilities installation. By utilizing the Doppler effects incurred by human motion on multiple Wi-Fi devices, RR-Alarm is not only able to accurately detect the intrusion without any extra human efforts but also avoids a large number of false alarms caused by the human motion from outside the house. A long-term trial in a nursing home verifies the effectiveness of our Wi-Fi based RR-Alarm system.

Inverse Reinforcement Learning with Multiple Ranked Experts.

Abstract: We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators of varying performance. We assume the demonstrators are classified into one of k ranks, and use ideas from ordinal regression to find a reward function that maximizes the margin between the different ranks. This approach is based on the idea that agents should not only learn how to behave from experts, but also how not to behave from non-experts. We show there are MDPs where important differences in the reward function would be hidden from existing algorithms by the behaviour of the expert. Our method is particularly useful for problems where we have access to a large set of agent behaviours with varying degrees of expertise (such as through GPS or cellphones). We highlight the differences between our approach and existing methods using a simple grid domain and demonstrate its efficacy on determining passenger-finding strategies for taxi drivers, using a large dataset of GPS trajectories.

Assessing Mental Stress Based on Smartphone Sensing Data: An Empirical Study

Abstract: Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

A CSI-ratio model based house-level respiration monitoring system using COTS wifi devices

Abstract: The past few years have witnessed the great potential of exploiting channel state information (CSI) retrieved from COTS WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. This sensing range constraint greatly limits the application of the proposed approaches in real life. Different from the existing approaches that apply the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range.1 In this demo, we will demonstrate FarSense - a CSI-ratio model based house-level real-time respiration monitoring system using COTS WiFi devices.

A contactless gesture interaction system using LTE (4G) signals

Abstract: Nowadays, 4G devices are pervasive and most of the homes and offices in modern cities are covered by LTE signals. While it is very attractive to leverage ubiquitous LTE signals and use hand gestures to control the home appliances remotely, there is no work on such contactless gesture interaction systems reported yet. In this work, we present an LTE-based contactless gesture interaction system to recognize various hand gestures around a 4G terminal like mobile phone, which can be used to control the switch, channel and volume of a TV set remotely without holding any devices. The results show that the proposed system can recognize different hand gestures accurately leveraging LTE signals without training, and achieve remote TV control in real time in different settings.

Correlating MSM's Mental Health with Usage Behaviors on MSM-Specific Social Applications

Abstract: Homophobia and discrimination towards men who have sex with men (MSM) make the mental health of this community a severe concern. The prevalence of mobile social apps for MSM provides a new channel to study their mental health issues. However, the correlation between the psychological states of MSM and their behaviors on social apps are still uninvestigated. In this paper, we conduct a case study of 103 MSM in China to explore whether the User Generated Content, Profile, Mobility information of MSM on social apps correlate with their depression, loneliness, anxiety and stress level. The analysis results indicate that the mental health disorder level of MSM is correlated with the emotion expressed in posts, whether the user has filled in self-description blank, the social relationship among online friends, frequency of visiting popular gays-meeting places. Our findings imply future opportunities in early psychological problems detection and intervention.

BoardWatch: a tree-enhanced regression model for billboard popularity prediction with multi-source urban data

Abstract: Predicting the popularity of outdoor billboards is crucial for many applications such as guidance of billboard placement and estimation of advertising cost. Recently, some researchers have worked on leveraging single traffic data to access the performance of billboards, which often leads to coarse-grained performance estimation and undesirable ad placement plans. To solve the problem, we propose a data-driven system, named BoradWatch, for fine-grained billboard popularity prediction. In particular, we extract three types of critical features based on multi-source urban data, including billboard profile, geographic feature and commercial feature. Furthermore, we propose a hybrid model named Tree-Enhanced Regression Model (TERM) based on extracted features for prediction, which takes full advantage of the feature transformation of decision trees model to enhance the prediction performance of the linear model. Experiment results on real-world outdoor billboard data and multi-source urban data demonstrate the effectiveness of our work.

A contactless Morse code text input system using COTS wifi devices

Abstract: For the patients with speech and motion impairments, there is an indispensable need to facilitate their communication with other people, using approaches such as eyeball tracking. However, these systems are usually complex and expensive. In this demo, we propose a WiFi-based contactless text input system, called WiMorse. The system allows these patients to communicate with other people by using WiFi signals to track single-finger movements and encoding them as Morse code to input text. However, we note that a small change in the target's location would lead to a significant change in the received WiFi signal pattern, making it impossible to recognize the finger gestures. To tackle this problem, we propose a signal transformation mechanism to obtain a consistent and stable signal pattern at various locations. By deploying only a pair of COTS WiFi devices, WiMorse can achieve real time recognition of finger generated Morse code with high accuracy, and is robust against input position, environment change, and user diversity.