Showing papers by "Daqing Zhang published in 2016"

Human respiration detection with commodity wifi devices: do user location and body orientation matter?

Abstract: 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.

Dynamic-MUSIC: accurate device-free indoor localization

Abstract: 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.

Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks

Abstract: 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.

Sparse mobile crowdsensing: challenges and opportunities

Abstract: Sensing cost and data quality are two primary concerns in mobile crowdsensing. In this article, we propose a new crowdsensing paradigm, sparse mobile crowdsensing, which leverages the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated, thus lowering overall sensing cost (e.g., smartphone energy consumption and incentives) while ensuring data quality. Sparse mobile crowdsensing applications intelligently select only a small portion of the target area for sensing while inferring the data of the remaining unsensed area with high accuracy. We discuss the fundamental research challenges in sparse mobile crowdsensing, and design a general framework with potential solutions to the challenges. To verify the effectiveness of the proposed framework, a sparse mobile crowdsensing prototype for temperature and traffic monitoring is implemented and evaluated. With several future research directions identified in sparse mobile crowdsensing, we expect that more research interests will be stimulated in this novel crowdsensing paradigm.

WiDir: walking direction estimation using wireless signals

Abstract: 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.

iCrowd : Near-Optimal Task Allocation for Piggyback Crowdsensing

Abstract: 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.

Dynamic cluster-based over-demand prediction in bike sharing systems

Abstract: 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.

TaskMe: multi-task allocation in mobile crowd sensing

Abstract: 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.).

TaskMe: Multi-Task Allocation in Mobile Crowd Sensing

Abstract: 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.).

Mobile crowd sensing and computing: when participatory sensing meets participatory social media

Abstract: With the development of mobile sensing and mobile social networking techniques, mobile crowd sensing and computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characteristic features: it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and it presents the fusion of human and machine intelligence in both the sensing and computing processes. This article characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

Differential Location Privacy for Sparse Mobile Crowdsensing

Abstract: 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.

Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data

Abstract: 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.

Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

Abstract: 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.

PrivCheck: privacy-preserving check-in data publishing for personalized location based services

Abstract: 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.

A Survey on Wi-Fi Based Contactless Activity Recognition

Abstract: Providing accurate information about human's state, activity is one of the most important elements in Ubiquitous Computing. Various applications can be enabled if one's state, activity can be recognized. Due to the low deployment cost, non-intrusive sensing nature, Wi-Fi based activity recognition has become a promising, emerging research area. In this paper, we survey the state-of-the-art of the area from four aspects ranging from historical overview, theories, models, key techniques to applications. In addition to the summary about the principles, achievements of existing work, we also highlight some open issues, research directions in this emerging area.

Mining Personal Frequent Routes via Road Corner Detection

Abstract: 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.

Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

Abstract: 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.

PicPick: a generic data selection framework for mobile crowd photography

Abstract: Mobile crowd photography (MCP) is a widely used technique in crowd sensing. In MCP, a picture stream is generated when delivering intermittently to the backend server by participants. Pictures contributed later in the stream may be semantically or visually relevant to previous ones, which can result in data redundancy. To meet diverse constraints (e.g., spatiotemporal contexts, single or multiple shooting angles) on the data to be collected in MCP tasks, a data selection process is needed to eliminate data redundancy and reduce network overhead. This issue has little been investigated in existing studies. To address this requirement, we propose a generic data collection framework called PicPick. It first presents a multifaceted task model that allows for varied MCP task specification. A pyramid tree (PTree) method is further proposed to select an optimal set of pictures from picture streams based on multi-dimensional constraints. Experimental results on two real-world datasets indicate that PTree can effectively reduce data redundancy while maintaining the coverage requests, and the overall framework is flexible.

A Context-Aware Framework for Reducing Bandwidth Usage of Mobile Video Chats

Abstract: 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.

When can we detect human respiration with commodity wifi devices

Abstract: Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively using commodity WiFi devices. However, it is not always possible to sense human respiration when a subject is in different locations or faces different orientations. In this demo, we will show how a centimeter-scale position change affects the respiration detection performance. Counter-intuitively, when a subject is moving closer to the LoS, the performance of respiration sensing is not always getting better. In fact, the detectable and undetectable regions are determined by the Fresnel zone model based theory developed in [7].

EnUp: Energy-Efficient Data Uploading for Mobile Crowd Sensing Applications

Abstract: Mobile crowd sensing enables large-scale sensing of the physical world at low cost by leveraging the available sensors on the mobile phones. One of the key factors for the success of mobile crowd sensing is uploading the sensing data to the cloud promptly. Traditional data uploading strategies leveraging whenever available networks may incur extra data cost, impact phone performance,, drain battery power significantly. In this paper, we propose an energy-efficient large data uploading framework using only WiFi network. Specifically, we propose to upload data at WiFi Ready Conditions (WRCs), when the WiFi network is connected, no front-end applications are using it. By forecasting the WRCs that will be encountered in a data uploading task, our framework intelligently selects optimal WRCs to minimize the overall energy consumption. Our evaluation results with the Device Analyzer Dataset show that the proposed method can effectively upload large data while consuming 30% less energy than the greedy-based baseline method.

A Context-Driven Worker Selection Framework for Crowd-Sensing

Abstract: Worker selection for many crowd-sensing tasks must consider various complex contexts to ensure high quality of data. Existing platforms and frameworks take only specific contexts into account to demonstrate motivating scenarios but do not provide general context models or frameworks in support of crowd-sensing at large. This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creator define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. In the first phase, it selects workers who satisfy predefined constraints. In the second phase, by leveraging the worker’s past participation history, it further selects those who are more likely to undertake a crowd-sensing task based on a case-based reasoning algorithm. We demonstrate the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluate the effectiveness of the case-based reasoning algorithm for willingness-based selection by using a questionnaire-generated dataset. Results show that our case-based reasoning algorithm outperforms the currently practiced baseline method.

Density adaptive clustering method orienting behavior identification

Abstract: The invention discloses a density adaptive clustering method orienting behavior identification, and relates to the technical field of clustering analysis. The density adaptive clustering method comprises the steps that clustering analysis is performed on a given data set from the highest density threshold to the lowest density threshold according to the decreasing order. The result generated in the previous clustering process can directly act as the input of the next clustering process, and necessary correction is performed on the previous clustering result under the current density threshold so that clustering of different density data clusters can be realized. Basic clustering operators adopt the clustering method based on density, and the clustering process is the typical iterative extension process so that the disadvantages that a distance-based algorithm only can discover quasi-circular clusters can be overcome. Therefore, the method is not sensitive to noise data and can automatically eliminate influence of the noise data on the clustering process and can discover the clusters of any shapes.