Showing papers by "Daqing Zhang published in 2020"

An Ontology-based Context Model in Intelligent Environments

Abstract: Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.

MultiSense: Enabling Multi-person Respiration Sensing with Commodity WiFi

Abstract: In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. Existing approaches mainly rely on spectral analysis of the CSI amplitude to obtain respiration rate information, leading to multiple limitations: (1) spectral analysis works when multiple persons exhibit dramatically different respiration rates, however, it fails to resolve similar rates; (2) spectral analysis can only obtain the average respiration rate over a period of time, and it is unable to capture the detailed rate change over time; (3) they fail to sense the respiration when a target is located at the "blind spots" even the target is close to the sensing devices. To overcome these limitations, we propose MultiSense, the first WiFi-based system that can robustly and continuously sense the detailed respiration patterns of multiple persons even they have very similar respiration rates and are physically closely located. The key insight of our solution is that the commodity WiFi hardware nowadays is usually equipped with multiple antennas. Thus, each individual antenna can receive a different mix copy of signals reflected from multiple persons. We successfully prove that the reflected signals are linearly mixed at each antenna and propose to model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to separate the mixed signal and obtain the reparation information of each person. Extensive experiments show that with only one pair of transceivers, each equipped with three antennas, MultiSense is able to accurately monitor respiration even in the presence of four persons, with the mean absolute respiration rate error of 0.73 bpm (breaths per minute).

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

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

FingerDraw: Sub-wavelength Level Finger Motion Tracking with WiFi Signals

Abstract: This paper explores the possibility of tracking finger drawings in the air leveraging WiFi signals from commodity devices. Prior solutions typically require user to hold a wireless transmitter, or need proprietary wireless hardware. They can only recognize a small set of pre-defined hand gestures. This paper introduces FingerDraw, the first sub-wavelength level finger motion tracking system using commodity WiFi devices, without attaching any sensor to finger. FingerDraw can reconstruct finger drawing trajectory such as digits, alphabets, and symbols with the setting of one WiFi transmitter and two WiFi receivers. It uses a two-antenna receiver to sense the sub-wavelength scale displacement of finger motion in each direction. The theoretical underpinning of FingerDraw is our proposed CSI-quotient model, which uses the channel quotient between two antennas of the receiver to cancel out the noise in CSI amplitude and the random offsets in CSI phase, and quantifies the correlation between CSI value dynamics and object displacement. This channel quotient is sensitive to and enables us to detect small changes in In-phase and Quadrature parts of channel state information due to finger movement. Our experimental results show that the overall median tracking accuracy is 1.27 cm, and the recognition of drawing ten digits in the air achieves an average accuracy of over 93.0%.

Exploring LoRa for Long-range Through-wall Sensing

Abstract: Wireless signals have been extensively utilized for contactless sensing in the past few years. Due to the intrinsic nature of employing the weak target-reflected signal for sensing, the sensing range is limited. For instance, WiFi and RFID can achieve 3-6 meter sensing range while acoustic-based sensing is limited to less than one meter. In this work, we identify exciting sensing opportunities with LoRa, which is the new long-range communication technology designed for IoT communication. We explore the sensing capability of LoRa, both theoretically and experimentally. We develop the sensing model to characterize the relationship between target movement and signal variation, and propose novel techniques to increase LoRa sensing range to over 25 meters for human respiration sensing. We further build a prototype system which is capable of sensing both coarse-grained and fine-grained human activities. Experimental results show that (1) human respiration can still be sensed when the target is 25 meters away from the LoRa devices, and 15 meters away with a wall in between; and (2) human walking (both displacement and direction) can be tracked accurately even when the target is 30 meters away from the LoRa transceiver pair.

Sparse Mobile Crowdsensing With Differential and Distortion Location Privacy

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

ROD-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-on-Demand Service Using Multi-Source Urban Data

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

WiDIGR: Direction-Independent Gait Recognition System Using Commercial Wi-Fi Devices

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

VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Abstract: Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. ${\sf VTracer}$ is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle’s GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online map-matcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. ${\sf VTracer}$ meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented ${\sf VTracer}$ on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate that ${\sf VTracer}$ achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.

WiBorder: Precise Wi-Fi based Boundary Sensing via Through-wall Discrimination

Abstract: Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.

Your Smart Speaker Can "Hear" Your Heartbeat!

Abstract: Vital sign monitoring is a common practice amongst medical professionals, and plays a key role in patient care and clinical diagnosis. Traditionally, dedicated equipment is employed to monitor these vital signs. For example, electrocardiograms (ECG) with 3-12 electrodes are attached to the target chest for heartbeat monitoring. In the last few years, wireless sensing becomes a hot research topic and wireless signal itself is utilized for sensing purposes without requiring the target to wear any sensors. The contact-free nature of wireless sensing makes it particularly appealing in current COVID-19 pandemic. Recently, promising progress has been achieved and the sensing granularity has been pushed to millimeter level, fine enough to monitor respiration which causes a chest displacement of 5 mm. While a great success with respiration monitoring, it is still very challenging to monitor heartbeat due to the extremely subtle chest displacement (0.1 - 0.5 mm) - smaller than 10% of that caused by respiration. What makes it worse is that the tiny heartbeat-caused chest displacement is buried inside the respiration-caused displacement. In this paper, we show the feasibility of employing the popular smart speakers (e.g., Amazon Echo) to monitor an individual's heartbeats in a contact-free manner. To extract the submillimeter heartbeat motion in the presence of other interference movements, a series of novel signal processing schemes are employed. We successfully prototype the first real-time heartbeat monitoring system using a commodity smart speaker. Experiment results show that the proposed system can monitor a target's heartbeat accurately, achieving a median heart rate estimation error of 0.75 beat per minute (bpm), and a median heartbeat interval estimation error of 13.28 ms (less than 1.8%), outperforming even some popular commodity products available on the market.

Robust Dynamic Hand Gesture Interaction using LTE Terminals

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

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

Abstract: Most of current urban logistic systems fail to strike a nice trade-off between speed and cost. An express logistic service often implies a high delivery cost. To alleviate such contradiction, we propose an idea of leveraging the shared mobility for on-time package deliveries, i.e., using taxis that are already transporting passengers as package hitchhikers. It is well- recognized that taxi drivers are good at delivering passengers to their destinations efficently. Thus, the proposed new urban logistic system has potentials to lower the cost and accelerate package deliveries simultaneously. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express services. 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. Results show that around 9,500 packages can be delivered successfully on time per day with the success rate over 94%.

Sending More With Less: Crowdsourcing Integrated Transportation as a New Form of Citywide Passenger–Package Delivery System

Abstract: Although much effort has been devoted by both academic and industrial communities to improve the efficiency of urban passenger and package flows, current urban transport systems still fail to balance the speed and cost. To fill the gap, in this paper, we propose a novel form of transport system called crowdsourcing integrated transportation (CIT). It leverages the underused transport capacity which is generated while delivering passengers to hitchhike packages so that more transportation needs can be met with fewer vehicles and drivers (i.e., sending more with less). We identify the unique features of the new delivery system when comparing to the traditional transport systems and discuss the key research challenges and potential solutions. We further implement the passenger-occupied taxis as the package carriers and evaluate the effectiveness. With several future research directions discussed in CIT, we expect that more research interests will be stimulated in this novel transport paradigm.

A multi-person respiration monitoring system using COTS wifi devices

Abstract: In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. However, existing approaches only work when multiple persons exhibit dramatically different respiration rates and the performance degrades significantly when the targeted subjects have similar rates. What's more, they can only obtain the average respiration rate over a period of time and fail to capture the detailed rate change over time. These two constraints greatly limit the application of the proposed approaches in real life. Different from the existing approaches that apply spectral analysis to the CSI amplitude (or phase difference) to obtain respiration rate information, we leverage the multiple antennas provided by the commodity WiFi hardware and model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to obtain the reparation information of each person. In this demo, we will demonstrate MultiSense - a multi-person respiration monitoring system using COTS WiFi devices.

Compact Scheduling for Task Graph Oriented Mobile Crowdsourcing

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

DeepStore: Understanding Customer Behaviors in Unmanned Stores

Abstract: In recent years, we have witnessed a surge in new retail, which aims to combine the best of physical and online retailing using Internet of things and artificial intelligence techniques. The unmanned store is a representative type of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences. This paper gives an overview of this emerging research area, presents its key techniques and applications, and discusses the open issues of this field.

A Force-directed Approach to Seeking Route Recommendation in Ride-on-demand Service Using Multi-source Urban Data

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

Will Online Digital Footprints Reveal Your Relationship Status?: An Empirical Study of Social Applications for Sexual-Minority Men

Abstract: With the increasing social acceptance and openness, more and more sexual-minority men (SMM) have succeeded in creating and sustaining steady relationships in recent years. Maintaining steady relationships is beneficial to the wellbeing of SMM both mentally and physically. However, the relationship maintaining for them is also challenging due to the much less supports compared to the heterosexual couples, so that it is important to identify those SMM in steady relationship and provide corresponding personalized assistance. Furthermore, knowing SMM's relationship and the correlations with other visible features is also beneficial for optimizing the social applications' functionalities in terms of privacy preserving and friends recommendation. With the prevalence of SMM-oriented social apps (called SMMSA for short), this paper investigates the relationship status of SMM from a new perspective, that is, by introducing the SMM's online digital footprints left on SMMSA (e.g., presented profile, social interactions, expressions, sentiment, and mobility trajectories). Specifically, using a filtered dataset containing 2,359 active SMMSA users with their self-reported relationship status and publicly available app usage data, we explore the correlations between SMM's relationship status and their online digital footprints on SMMSA and present a set of interesting findings. Moreover, we demonstrate that by utilizing such correlations, it has the potential to construct machine-learning-based models for relationship status inference. Finally, we elaborate on the implications of our findings from the perspective of better understanding the SMM community and improving their social welfare.

Cyber-Physical-Social-Mediated Communication

Abstract: We introduce the concept of cyber-physical-social-mediated communication (CPS-C) and analyze why CPS-C is better than pure cyber-mediated communication for two popular applications: mobile social networks and mobile crowd sensing. CPS-C utilizes its unique characteristics (i.e., cyber-physical synchronization, human intelligence, and physical displacement) to bring benefits, including natural boundary of information exposure, tangible interaction, targeting receivers on the fly, decentralization, and piggybacking. As a result, energy efficiency and user experience are improved. We highlight the existence of human-machine intelligence in the communication process, which has rarely been addressed.

A Hierarchical Semantic Overlay for P2P Search.

Abstract: In this paper, we propose a hierarchical semantic overlay network for searching heterogeneous data over wide-area networks. In this system, data are represented as RDF triples based on ontologies. Peers that have the same semantics are organized into a semantic cluster, and the semantic clusters are self-organized into a one-dimensional ring space to form the toplevel semantic overlay network. Each semantic cluster has its low-level overlay network which can be built using an unstructured overlay or a DHT-based overlay. A search is first forwarded to the appropriate semantic cluster, and then routed to the specific peers that hold the relevant data using a parallel flooding algorithm or a DHT-based routing algorithm. By combining the advantages of both unstructured and structured overlay networks, we are able to achieve a better tradeoff in terms of search efficiency, search cost and overlay maintenance cost.

A P2P Context Lookup Service for Multiple Smart Spaces

Abstract: Context information has emerged as an important resource to enable autonomy and flexibility of pervasive applications. The widespread use of context information necessitates efficient wide-area lookup services. In this paper, we present the design and implementation of a peer-to-peer context lookup system to support contextaware applications over multiple smart spaces. Our system provides a distributed repository for context storage, and a semantic peer-to-peer network for context lookup. Collaborative context-aware applications that utilize different context information in multiple smart spaces can be easily built by invoking a pull or push service provided by our system. We outline the design and implementation of our system, and validate our system through the development of cross-domain applications