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| 2. |
- Hu, Xiping, et al.
(author)
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SAfeDJ : A crowd-cloud codesign approach to situation-aware music delivery for drivers
- 2015
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In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). - : Association for Computing Machinery (ACM). - 1551-6857 .- 1551-6865. ; 12:1s, s. 21:1-24
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Journal article (peer-reviewed)abstract
- Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.
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| 5. |
- Tu, Wei, et al.
(author)
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A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers
- 2016
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In: SMART CITY 360. - Cham : SPRINGER INT PUBLISHING AG. - 9783319336817 - 9783319336800 ; , s. 3-15
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Conference paper (peer-reviewed)abstract
- The rapid development of the Internet of Things (IoT) has provided innovative solutions to reduce traffic accidents caused by fatigue driving. When drivers are in bad mood or tired, their vigilance level decreases, which may prolong the reaction time to emergency situation and lead to serious accidents. With the help of mobile sensing and mood-fatigue detection, drivers' moodfatigue status can be detected while driving, and then appropriate measures can be taken to eliminate the fatigue or negative mood to increase the level of vigilance. This paper presents the basic concepts and current solutions of moodfatigue detection and some common solutions like mobile sensing and cloud computing techniques. After that, we introduce some emerging platforms which designed to promote safe driving. Finally, we summarize the major challenges in mood-fatigue detection of drivers, and outline the future research directions.
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| 6. |
- Wang, Xiaojie, et al.
(author)
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A City-Wide Real-Time Traffic Management System : Enabling Crowdsensing in Social Internet of Vehicles
- 2018
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In: IEEE Communications Magazine. - 0163-6804 .- 1558-1896. ; 56:9, s. 19-25
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Journal article (peer-reviewed)abstract
- As an emerging platform based on ITS, SIoV is promising for applications of traffic management and road safety in smart cities. However, the end-to-end delay is large in store-carry-and-forward-based vehicular networks, which has become the main obstacle for the implementation of large-scale SIoV. With the extensive applications of mobile devices, crowdsensing is promising to enable real-time content dissemination in a city-wide traffic management system. This article first provides an overview of several promising research areas for traffic management in SIoV. Given the significance of traffic management in urban areas, we investigate a crowdsensing-based framework to provide timely response for traffic management in heterogeneous SIoV. The participant vehicles based on D2D communications integrate trajectory and topology information to dynamically regulate their social behaviors according to network conditions. A real-world taxi trajectory analysis-based performance evaluation is provided to demonstrate the effectiveness of the designed framework. Furthermore, we discuss several future research challenges before concluding our work.
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| 7. |
- Wen, Quansi, et al.
(author)
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Modeling Human Activity With Seasonality Bursty Dynamics
- 2020
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In: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 16:2, s. 1130-1139
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Journal article (peer-reviewed)abstract
- The public's purchase incentive increases dramatically during the holiday season and subsequently returns to normal levels. This seasonality is common in various scenarios and highlights the following questions: how does the public's purchase incentive fluctuate over the course of a year? Which factors are conducive to this seasonal behavior and how can they be modeled? In this paper, we propose a model that explicitly integrates temporal point process theory with the construction of a networked community, to describe the dynamics of collective action propagation with seasonal fluctuation. Furthermore, a database is constructed of sales records for 21 video game consoles and 13 237 video games in France, Germany, Japan, the U.K., the USA, and worldwide from 1989 to 2018. Experimental results suggest that peak desire always appears in the holiday season about one week before Christmas and is about four times higher than consumption desire in a normal period in all areas.
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| 9. |
- Zhang, Jiao, et al.
(author)
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Joint Resource Allocation for Latency-Sensitive Services Over Mobile Edge Computing Networks With Caching
- 2019
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In: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 6:3, s. 4283-4294
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Journal article (peer-reviewed)abstract
- Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.
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| 10. |
- Fang, Jing, et al.
(author)
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Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality
- 2019
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 174425-174437
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Journal article (peer-reviewed)abstract
- In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.
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| 11. |
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| 12. |
- Hu, Xiping, et al.
(author)
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Crowdsourcing for Mobile Networks and IoT
- 2018
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In: Wireless Communications & Mobile Computing. - : WILEY-HINDAWI. - 1530-8669 .- 1530-8677.
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Journal article (other academic/artistic)
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| 13. |
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| 14. |
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| 15. |
- Li, Nan, et al.
(author)
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On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things
- 2021
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In: IEEE Transactions on Industrial Informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 17:2, s. 1069-1078
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Journal article (peer-reviewed)abstract
- An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.
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| 16. |
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| 17. |
- Sathyamoorthy, Peramanathan, et al.
(author)
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Energy Efficiency as an Orchestration Service for Mobile Internet of Things
- 2015
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Conference paper (peer-reviewed)abstract
- This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their on-board sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.
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| 18. |
- Sathyamoorthy, Peramanathan, et al.
(author)
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Profiling energy efficiency and data communications for mobile Internet of Things
- 2017
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In: Wireless Communications & Mobile Computing. - : Hindawi Limited. - 1530-8669 .- 1530-8677. ; 17
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Journal article (peer-reviewed)abstract
- This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their on-board sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.
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| 19. |
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| 20. |
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| 21. |
- Zhou, Li, et al.
(author)
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Green Small Cell Planning in Smart Cities under Dynamic Traffic Demand
- 2015
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In: 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). - 9781467371315 ; , s. 618-623
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Conference paper (peer-reviewed)abstract
- In smart cities, cellular network plays a crucial role to support connectivity anywhere and anytime. However, the communication demand brought by applications and services is hard to predict. Traffic in cellular networks might fluctuate heavily over time to time, which causes burden and waste under different traffic states. Recently, small cell was proposed to enhance spectrum efficiency and energy efficiency in cellular networks. However, how green the small cell network can be is still a question because of the accompanying interference. To meet this challenge, new green technologies should be developed. In this paper, we propose a green small cell planning scheme considering dynamic traffic states. First, we predefine a set of candidate locations for base stations (BSs) in a geographical area and generate a connection graph which contains all possible connections between BSs and user equipments (UEs). Then we adopt a heuristic to switch off small cell BSs (s-BSs) and update BS-UE connections iteratively. Finally we obtain a cell planning solution with energy efficiency without reducing spectrum efficiency and quality-of-service (QoS) requirements. The simulation results show that our dynamic small cell planning scheme has low computational complexity and achieves a significant improvement in energy efficiency comparing with the static cell planning scheme.
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