arXiv subjects

Daqing Zhang

Publications and source records attributed to Daqing Zhang.

33 records · Page 2Linked to original sources

arXiv2019

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

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.

eess.SP↗

arXiv2019

Inverse Reinforcement Learning with Multiple Ranked Experts

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.

cs.LG↗

arXiv2018

CrowdExpress: A Probabilistic Framework for On-Time Crowdsourced Package Deliveries

Speed and cost of logistics are two major concerns to on-line shoppers, but they generally conflict with each other in nature. To alleviate the contradiction, we propose to exploit existing taxis that are transporting passengers on the street to relay packages collaboratively, which can simultaneously lower the cost and accelerate the speed. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express deliveries. In the first phase, we mine the historical taxi GPS trajectory data offline to build the package transport network. In the second phase, we develop an online adaptive taxi scheduling algorithm to find the path with the maximum arriving-on-time probability "on-the-fly" upon real- time requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be delivered successfully on time per day with the success rate over 94%, moreover, the average computation time is within 25 milliseconds.

cs.OH↗

arXiv2018

Task Allocation in Mobile Crowd Sensing: State of the Art and Future Opportunities

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

cs.HC↗

arXiv2018

Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing

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

cs.AI↗

arXiv2018

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

With the advent of seamless connection of human, machine, 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 which can inform and guide future research directions.

cs.HC↗

arXiv2018

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

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

cs.HC↗

arXiv2018

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

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 influenced 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.

cs.SI↗

arXiv2017

MFDL: A Multicarrier Fresnel Penetration Model based Device-Free Localization System leveraging Commodity Wi-Fi Cards

Device-free localization plays an important role in many ubiquitous applications. Among the different technologies proposed, Wi-Fi based technology using commercial devices has attracted much attention due to its low cost, ease of deployment, and high potential for accurate localization. Existing solutions use either fingerprints that require labor-intensive radio-map survey and updates, or models constructed from empirical studies with dense deployment of Wi-Fi transceivers. In this work, we explore the Fresnel Zone Theory in physics and propose a generic Fresnel Penetration Model (FPM), which reveals the linear relationship between specific Fresnel zones and multicarrier Fresnel phase difference, along with the Fresnel phase offset caused by static multipath environments. We validate FPM in both outdoor and complex indoor environments. Furthermore, we design a multicarrier FPM based device-free localization system (MFDL), which overcomes a number of practical challenges, particularly the Fresnel phase difference estimation and phase offset calibration in multipath-rich indoor environments. Extensive experimental results show that compared with the state-of-the-art work (LiFS), our MFDL system achieves better localization accuracy with much fewer number of Wi-Fi transceivers. Specifically, using only three transceivers, the median localization error of MFDL is as low as 45$cm$ in an outdoor environment of 36$m^2$, and 55$cm$ in indoor settings of 25$m^2$. Increasing the number of transceivers to four allows us to achieve 75$cm$ median localization error in a 72$m^2$ indoor area, compared with the 1.1$m$ median localization error achieved by LiFS using 11 transceivers in a 70$m^2$ area.

cs.NI↗

arXiv2017

Ridesourcing Car Detection by Transfer Learning

Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.

cs.LG↗

arXiv2016

TaskMe: Multi-Task Allocation in Mobile Crowd Sensing

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.).

cs.HC↗

arXiv2015

Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices

Fall is one of the major health threats and obstacles to independent living for elders, timely and reliable fall detection is crucial for mitigating the effects of falls. In this paper, leveraging the fine-grained Channel State Information (CSI) and multi-antenna setting in commodity WiFi devices, we design and implement a real-time, non-intrusive, and low-cost indoor fall detector, called Anti-Fall. For the first time, the CSI phase difference over two antennas is identified as the salient feature to reliably segment the fall and fall-like activities, both phase and amplitude information of CSI is then exploited to accurately separate the fall from other fall-like activities. Experimental results in two indoor scenarios demonstrate that Anti-Fall consistently outperforms the state-of-the-art approach WiFall, with 10% higher detection rate and 10% less false alarm rate on average.

cs.HC↗

arXiv2015

Mobile Crowd Sensing and Computing: When Participatory Sensing Meets Participatory Social Media

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 characterizing features: (1) it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and (2) it presents the fusion of human and machine intelligence (HMI) in both the sensing and computing process. This paper characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

cs.HC↗

arXiv2014

From Participatory Sensing to Mobile Crowd Sensing

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

cs.HC↗

arXiv2011

CDTOM: A Context-driven Task-oriented Middleware for Pervasive Homecare Environment

With the growing number of the elderly, we see a greater demand for home care, and the vision of pervasive computing is also floating into the domain of the household that aims to build a smart home which can assist inhabitants (users) to live more conveniently and harmoniously. Such health-care pervasive applications in smart home should focus on the inhabitant's goal or task in diverse situations, rather than the various complex devices and services. The core challenge for homecare design is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently according to the user's intent. Due to the dynamic and heterogeneous nature of pervasive computing environment, it is difficult for an average user to obtain right information and service and in right place at right time. This paper proposes a context-driven task-oriented middleware (CDTOM) to meet the challenge. The most important component is its task model that provides an adequate high-level description of user-oriented tasks and their related contexts. Leveraging the model multiple entities can easily exchange, share and reuse their knowledge. Based on the hierarchy of task ontology, a novel task recognition approach using CBR (case-based reasoning) is presented and the performance of task recognition is evaluated by task number, context size and time costing. Moreover, a dynamic mechanism for mapping the recognized task and services is also discussed. Finally, we present the design and implementation of our task supporting system (TSS) to aid an inhabitant's tasks in light of his lifestyle and environment conditions in pervasive homecare environment, and the results of the prototype system show that our middleware approach achieves good efficiency of context management and good accuracy of user's activity inference, and can improve efficiently quality of user's life.

cs.HC↗