Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),t...Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.展开更多
Background Video anomaly detection has always been a hot topic and has attracted increasing attention.Many of the existing methods for video anomaly detection depend on processing the entire video rather than consider...Background Video anomaly detection has always been a hot topic and has attracted increasing attention.Many of the existing methods for video anomaly detection depend on processing the entire video rather than considering only the significant context. Method This paper proposes a novel video anomaly detection method called COVAD that mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an autoencoded convolutional neural network and a coordinated attention mechanism,which can effectively capture meaningful objects in the video and dependencies among different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can significantly predict the future motion and appearance of objects in a video more effectively. Result The proposed algorithm obtained better experimental results on multiple datasets and outperformed the baseline models considered in our analysis. Simultaneously, we provide an improved visual test that can provide pixel-level anomaly explanations.展开更多
The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for prov...The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things （COT）, which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable ＂middleware＂ which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.展开更多
The recent advances in integrated circuit technologies, microprocessor hardware, wireless communications, embedded systems and technologies as well as the emergence of Ad-hoc networking, made up the concept of wireles...The recent advances in integrated circuit technologies, microprocessor hardware, wireless communications, embedded systems and technologies as well as the emergence of Ad-hoc networking, made up the concept of wireless sensor networks. Regarding the nature of sensors and the nature of the environment of deployment sensor networks are exposed to many attacks more than any other networks, therefore new strategies and protocols of security must be defined for these networks taking into consideration the characteristics of sensors as well as the architecture of the network. In this paper we propose a lightweight implementation of public key infrastructure called cluster based public infrastructure (CBPKI), CBPKI is based on the security and the authenticity of the base station for executing a set of handshakes intended to establish session keys between the base station and sensors over the network used for ensuring data confidentiality and integrity.展开更多
Peer-to-peer technologies have emerged as a powerful and scalable communication model for large scale content shar-ing. However, they are not yet provided with optimized heterogeneous aggregated content management fun...Peer-to-peer technologies have emerged as a powerful and scalable communication model for large scale content shar-ing. However, they are not yet provided with optimized heterogeneous aggregated content management functionality since they lack rich semantic specifications. To overcome these shortcomings, we elaborated a reference model of P2P architecture for a dynamic aggregation, sharing and retrieval of heterogeneous multimedia contents (simple or aggre-gated). This architecture was mainly developed under the CAM4Home European research project and is fully based on the CAM4Home semantic metadata model. This semantic model relies on RDF (Resource Description Framework) and is rich (but simple enough), extensible and dedicated for the description of any kind of multimedia content.In this paper, we detail and evaluate an original semantic-based community network architecture for heterogeneous multimedia con-tent sharing and retrieval. Within the presentedarchitecture, multimedia contents are managed according to their asso-ciated CAM4Home semantic metadata through a structured P2P topology. This topology relies on a semantically en-hanced DHT (Distributed Hash Table) and is also provided with an additional indexing system for offering semantic storage and search facilities and overcoming the problem of exact match keywords in DHTs.展开更多
This paper investigates the steady-state behavior of a semiconductor laser subject to arbitrary levels of external optical feedback by means of an iterative travelling-wave (ITW) model. Analytical expressions are deve...This paper investigates the steady-state behavior of a semiconductor laser subject to arbitrary levels of external optical feedback by means of an iterative travelling-wave (ITW) model. Analytical expressions are developed based on an iterative equation. We show that, as in good agreement with previous work, in the weak-feedback regime of operation except for a phase shift the ITW model will be simplified to the Lang-Kobayashi (LK) model, and that in the case where this phase shift is equal to zero the ITW model is identical to the LK model. The present work is of use in particular for distinguishing the coherence-collapse regime from the strong-feedback regime where low-intensity-noise and narrow-linewidth laser operation would be possible at high feedback levels with re-stabilization of the compound laser system.展开更多
A compact acquisition system developed for a flexible large area monoport tactile surface is presented in this paper. This sensor requires a single port connection and avoids complicated matrix acquisition system and ...A compact acquisition system developed for a flexible large area monoport tactile surface is presented in this paper. This sensor requires a single port connection and avoids complicated matrix acquisition system and multiplexing. The tactile surface is based on a coplanar transmission line printed on a large area flexible substrate. Touching the waveguide generates a reflected signal. A harmonic analysis of this reflected signal at the line input port allows locating the touch event. A compact and low complexity acquisition system has been developed in order to demonstrate the principle and evaluate the feasibility of its integration on the sensor. Theoretical background, design and measurements on the overall sensor are exposed. The acquisition circuit imperfections have been demonstrated experimentally and correction methods have been proposed and implemented. Results are presented, and to assess the precision of the compact acquisition system, they are compared to reference measurements made with a Vector Network Analyzer.展开更多
Monitoring respiration is an important component of personal health care.Though recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-b...Monitoring respiration is an important component of personal health care.Though recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based multi-person respiration sensing mainly extract individual's respiration rate in the frequency domain using the fast Fourier transform(FFT)or multiple signal classification(MUSIC)method,leading to the following limitations:1)largely ineffective in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths,2)unable to acquire the time-varying respiration pattern when the subject has respiratory abnormity,such as apnea and changing respiration rates,and 3)difficult to identify the real number of subjects when multiple subjects share the same or similar respiration rates.To address these issues,we propose Wi-Fi-enabled MUlti-person SEnsing(WiMUSE)as a signal processing pipeline to perform respiration monitoring for multiple persons simultaneously.Essentially,as a pioneering time domain approach,WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms,so as to form a blind source separation(BSS)problem.The effective separation of the signal sources(respiratory waveforms)further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects,and thus to identify the number of subjects along with their respective respiration waveforms.We implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that,compared with the approaches based on the FFT or MUSIC method,90%error of respiration rate can be reduced by more than 60%.展开更多
This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） ...This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl^di^fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,展开更多
The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly...The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.展开更多
Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the ...Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.展开更多
People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time moni...People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.展开更多
基金funded by the Key Research and Development Program of Zhejiang Province No.2023C01141the Science and Technology Innovation Community Project of the Yangtze River Delta No.23002410100suported by the Open Research Fund of the State Key Laboratory of Blockchain and Data Security,Zhejiang University.
文摘Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.
文摘Background Video anomaly detection has always been a hot topic and has attracted increasing attention.Many of the existing methods for video anomaly detection depend on processing the entire video rather than considering only the significant context. Method This paper proposes a novel video anomaly detection method called COVAD that mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an autoencoded convolutional neural network and a coordinated attention mechanism,which can effectively capture meaningful objects in the video and dependencies among different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can significantly predict the future motion and appearance of objects in a video more effectively. Result The proposed algorithm obtained better experimental results on multiple datasets and outperformed the baseline models considered in our analysis. Simultaneously, we provide an improved visual test that can provide pixel-level anomaly explanations.
文摘The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things （COT）, which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable ＂middleware＂ which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.
文摘The recent advances in integrated circuit technologies, microprocessor hardware, wireless communications, embedded systems and technologies as well as the emergence of Ad-hoc networking, made up the concept of wireless sensor networks. Regarding the nature of sensors and the nature of the environment of deployment sensor networks are exposed to many attacks more than any other networks, therefore new strategies and protocols of security must be defined for these networks taking into consideration the characteristics of sensors as well as the architecture of the network. In this paper we propose a lightweight implementation of public key infrastructure called cluster based public infrastructure (CBPKI), CBPKI is based on the security and the authenticity of the base station for executing a set of handshakes intended to establish session keys between the base station and sensors over the network used for ensuring data confidentiality and integrity.
文摘Peer-to-peer technologies have emerged as a powerful and scalable communication model for large scale content shar-ing. However, they are not yet provided with optimized heterogeneous aggregated content management functionality since they lack rich semantic specifications. To overcome these shortcomings, we elaborated a reference model of P2P architecture for a dynamic aggregation, sharing and retrieval of heterogeneous multimedia contents (simple or aggre-gated). This architecture was mainly developed under the CAM4Home European research project and is fully based on the CAM4Home semantic metadata model. This semantic model relies on RDF (Resource Description Framework) and is rich (but simple enough), extensible and dedicated for the description of any kind of multimedia content.In this paper, we detail and evaluate an original semantic-based community network architecture for heterogeneous multimedia con-tent sharing and retrieval. Within the presentedarchitecture, multimedia contents are managed according to their asso-ciated CAM4Home semantic metadata through a structured P2P topology. This topology relies on a semantically en-hanced DHT (Distributed Hash Table) and is also provided with an additional indexing system for offering semantic storage and search facilities and overcoming the problem of exact match keywords in DHTs.
文摘This paper investigates the steady-state behavior of a semiconductor laser subject to arbitrary levels of external optical feedback by means of an iterative travelling-wave (ITW) model. Analytical expressions are developed based on an iterative equation. We show that, as in good agreement with previous work, in the weak-feedback regime of operation except for a phase shift the ITW model will be simplified to the Lang-Kobayashi (LK) model, and that in the case where this phase shift is equal to zero the ITW model is identical to the LK model. The present work is of use in particular for distinguishing the coherence-collapse regime from the strong-feedback regime where low-intensity-noise and narrow-linewidth laser operation would be possible at high feedback levels with re-stabilization of the compound laser system.
文摘A compact acquisition system developed for a flexible large area monoport tactile surface is presented in this paper. This sensor requires a single port connection and avoids complicated matrix acquisition system and multiplexing. The tactile surface is based on a coplanar transmission line printed on a large area flexible substrate. Touching the waveguide generates a reflected signal. A harmonic analysis of this reflected signal at the line input port allows locating the touch event. A compact and low complexity acquisition system has been developed in order to demonstrate the principle and evaluate the feasibility of its integration on the sensor. Theoretical background, design and measurements on the overall sensor are exposed. The acquisition circuit imperfections have been demonstrated experimentally and correction methods have been proposed and implemented. Results are presented, and to assess the precision of the compact acquisition system, they are compared to reference measurements made with a Vector Network Analyzer.
基金supported by the National Natural Science Foundation of China A3 Foresight Program under Grant No.62061146001the Peking University(PKU)-Nanyang Technological University(NTU)Collaboration Project,the Project funded by China Postdoctoral Science Foundation under Grant No.2021TQo048+2 种基金the National Natural Science Foundation of China under Grant No.62172394the Beijing Natural Science Foundation under Grant No.L223034the Beijing Nova Program,and the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No.2020109.
文摘Monitoring respiration is an important component of personal health care.Though recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based multi-person respiration sensing mainly extract individual's respiration rate in the frequency domain using the fast Fourier transform(FFT)or multiple signal classification(MUSIC)method,leading to the following limitations:1)largely ineffective in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths,2)unable to acquire the time-varying respiration pattern when the subject has respiratory abnormity,such as apnea and changing respiration rates,and 3)difficult to identify the real number of subjects when multiple subjects share the same or similar respiration rates.To address these issues,we propose Wi-Fi-enabled MUlti-person SEnsing(WiMUSE)as a signal processing pipeline to perform respiration monitoring for multiple persons simultaneously.Essentially,as a pioneering time domain approach,WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms,so as to form a blind source separation(BSS)problem.The effective separation of the signal sources(respiratory waveforms)further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects,and thus to identify the number of subjects along with their respective respiration waveforms.We implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that,compared with the approaches based on the FFT or MUSIC method,90%error of respiration rate can be reduced by more than 60%.
文摘This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl^di^fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,
文摘The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.
文摘Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.
基金This work was mainly funded by the National Natural Science Foundation of China （Grant No. 61572048）, Research Fund from China Electric Power Research Institute （JS71-16-005）, and Microsoft Col- laboration Research Grant. Besides, the work was partially supported by the Fundamental Research Funds for the Central Universities （106112015CD-JXY180001）, Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services （Shenzhen University, China）, and Chongqing Basic and Frontier Research Program （cstc2015jcyjA00016）.
文摘People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.
