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Publications (10 of 99) Show all publications
Kaivonen, S. & Ngai, E.-H. C. H. (2020). Real-time air pollution monitoring with sensors on city bus. Digital Communications and Networks, 6(1), 23-30
Open this publication in new window or tab >>Real-time air pollution monitoring with sensors on city bus
2020 (English)In: Digital Communications and Networks, ISSN 2468-5925, Vol. 6, no 1, p. 23-30Article in journal (Refereed) Published
Abstract [en]

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles. The study is part of the GreenIoT project in Sweden, which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala. Through deploying low-cost wireless sensors, it is possible to obtain more fine-grained and real-time air pollution levels at different locations. The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala. The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed. Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

Place, publisher, year, edition, pages
KeAi, 2020
Keywords
Smart city, Internet-of-Things, Mobile sensor network, Air pollution monitoring
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:uu:diva-397761 (URN)10.1016/j.dcan.2019.03.003 (DOI)000516829300003 ()
Projects
Green IoT
Funder
Vinnova, 2015-00347
Available from: 2019-03-16 Created: 2019-11-25 Last updated: 2020-04-07Bibliographically approved
Fang, J., Wang, T., Li, C., Hu, X., Ngai, E., Seet, B.-C., . . . Jiang, X. (2019). Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality. IEEE Access, 7, 174425-174437
Open this publication in new window or tab >>Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 174425-174437Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Depression prevalence, depression analysis, gait abnormality, machine learning
National Category
Other Health Sciences
Identifiers
urn:nbn:se:uu:diva-406214 (URN)10.1109/ACCESS.2019.2957179 (DOI)000509384100004 ()
Available from: 2020-03-06 Created: 2020-03-06 Last updated: 2020-03-06Bibliographically approved
Chen, W., Zheng, Z., Ngai, E., Zheng, P. & Zhou, Y. (2019). Exploiting blockchain data to detect smart Ponzi schemes on Ethereum. IEEE Access, 7, 37575-37586
Open this publication in new window or tab >>Exploiting blockchain data to detect smart Ponzi schemes on Ethereum
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 37575-37586Article in journal (Refereed) Published
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-382362 (URN)10.1109/ACCESS.2019.2905769 (DOI)000464140500001 ()
Available from: 2019-03-18 Created: 2019-04-24 Last updated: 2019-12-02Bibliographically approved
Zhang, J., Hu, X., Ning, Z., Ngai, E., Zhou, L., Wei, J., . . . Leung, V. C. M. (2019). Joint Resource Allocation for Latency-Sensitive Services Over Mobile Edge Computing Networks With Caching. IEEE Internet of Things Journal, 6(3), 4283-4294
Open this publication in new window or tab >>Joint Resource Allocation for Latency-Sensitive Services Over Mobile Edge Computing Networks With Caching
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2019 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 3, p. 4283-4294Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Content caching, Internet of Things (IoT), mobile edge computing (MEC), resource allocation
National Category
Communication Systems
Identifiers
urn:nbn:se:uu:diva-390657 (URN)10.1109/JIOT.2018.2875917 (DOI)000472596200024 ()
Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved
Zhang, J., Zhou, L., Tang, Q., Ngai, E.-H. C. H., Hu, X., Zhao, H. & Wei, J. (2019). Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing. IEEE Internet of Things Journal, 6(2), 3688-3699
Open this publication in new window or tab >>Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing
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2019 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 2, p. 3688-3699Article in journal (Refereed) Published
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-385573 (URN)10.1109/JIOT.2018.2890133 (DOI)000467564700201 ()
Available from: 2018-12-28 Created: 2019-06-17 Last updated: 2019-12-02Bibliographically approved
Wang, X., Ning, Z., Hu, X., Ngai, E., Wang, L., Hu, B. & Kwok, R. (2018). A City-Wide Real-Time Traffic Management System: Enabling Crowdsensing in Social Internet of Vehicles. IEEE Communications Magazine, 56(9), 19-25
Open this publication in new window or tab >>A City-Wide Real-Time Traffic Management System: Enabling Crowdsensing in Social Internet of Vehicles
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2018 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 56, no 9, p. 19-25Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-366229 (URN)10.1109/MCOM.2018.1701065 (DOI)000444843900007 ()
Available from: 2018-11-18 Created: 2018-11-18 Last updated: 2018-11-26Bibliographically approved
Huang, W., Zhou, Y., Xie, X., Wu, D., Chen, M. & Ngai, E. (2018). Buffer State is Enough: Simplifying the Design of QoE-Aware HTTP Adaptive Video Streaming. IEEE transactions on broadcasting, 64(2), 590-601
Open this publication in new window or tab >>Buffer State is Enough: Simplifying the Design of QoE-Aware HTTP Adaptive Video Streaming
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2018 (English)In: IEEE transactions on broadcasting, ISSN 0018-9316, E-ISSN 1557-9611, Vol. 64, no 2, p. 590-601Article in journal (Refereed) Published
Abstract [en]

Recently, the prevalence of mobile devices together with the outburst of user-generated contents has fueled the tremendous growth of the Internet traffic taken by video streaming. To improve user-perceived quality-of-experience (QoE), dynamic adaptive streaming via HTTP (DASH) has been widely adopted by practical systems to make streaming smooth under limited bandwidth. However, previous DASH approaches mostly performed complicated rate adaptation based on bandwidth estimation, which has been proven to be unreliable over HTTP. In this paper, we simplify the design by only exploiting client-side buffer state information and propose a pure buffer-based DASH scheme to optimize user QoE. Our approach can not only get rid of the drawback caused by inaccurate bandwidth estimation, but also incur very limited overhead. We explicitly define an integrated user QoE model, which takes playback freezing, bitrate switch, and video quality into account, and then formulate the problem into a non-linear stochastic optimal control problem. Next, we utilize control theory to design a dynamic buffer-based controller for DASH, which determines video bitrate of each chunk to be requested and stabilize the buffer level in the meanwhile. Extensive experiments have been conducted to validate the advantages of our approach, and the results show that our approach can achieve the best performance compared with other alternative approaches.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
DASH, control theory, buffer state, rate-adaption
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-358078 (URN)10.1109/TBC.2018.2789580 (DOI)000434455200022 ()
Funder
Australian Research Council, DE180100950
Available from: 2018-08-30 Created: 2018-08-30 Last updated: 2018-08-30Bibliographically approved
Hu, X., Ning, Z., Zhang, K., Ngai, E., Bai, K. & Wang, F. (2018). Crowdsourcing for Mobile Networks and IoT. Wireless Communications & Mobile Computing, Article ID 6231236.
Open this publication in new window or tab >>Crowdsourcing for Mobile Networks and IoT
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2018 (English)In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, article id 6231236Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
WILEY-HINDAWI, 2018
National Category
Information Systems Communication Systems
Identifiers
urn:nbn:se:uu:diva-365881 (URN)10.1155/2018/6231236 (DOI)000435849700001 ()
Available from: 2018-11-20 Created: 2018-11-20 Last updated: 2018-11-20Bibliographically approved
Zhang, C., Liu, J., Chen, F., Cui, Y., Ngai, E.-H. C. H. & Hu, Y. (2018). Dependency- and similarity-aware caching for HTTP adaptive streaming. Multimedia tools and applications, 77(1), 1453-1474
Open this publication in new window or tab >>Dependency- and similarity-aware caching for HTTP adaptive streaming
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2018 (English)In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 77, no 1, p. 1453-1474Article in journal (Refereed) Published
Abstract [en]

There has been significant interest in the use of HTTP adaptive streaming for live or on-demand video over the Internet in recent years. To mitigate the streaming transmission delay and reduce the networking overhead, an effective and critical approach is to utilize cache services between the origin servers and the heterogeneous clients. As the underlying protocol for web transactions, HTTP has great potentials to explore the resources within state-of-the-art CDNs for caching; yet distinct challenges arise in the HTTP adaptive streaming context. After examining a long-term and large-scale adaptive streaming dataset as well as statistical analysis, we demonstrate that the switching requests among the different qualities frequently emerge and constitute a significant portion in a per-day view. Consequently, they have substantially affected the performance of cache servers and Quality-of-Experience (QoE) of viewers. In this paper, we propose a novel cache model that captures the dependency among the segments in the cache server for adaptive HTTP streaming. Our work does not assume any specific selection algorithm on the client's side and hence can be easily incorporated into existing streaming cache systems. Its centralized nature is also well accommodated by the latest DASH specification. Moreover, we extend our work to the multi-server caching context and present a similarity-aware allocation mechanism to enhance the caching efficiency. The performance evaluation shows our dependency- and similarity-aware strategy can significantly improve the cache hit-ratio and QoE of HTTP streaming as compared to previous approaches.

National Category
Computer Engineering
Identifiers
urn:nbn:se:uu:diva-336344 (URN)10.1007/s11042-016-4308-z (DOI)000419995400060 ()
Available from: 2017-01-13 Created: 2017-12-13 Last updated: 2018-02-26Bibliographically approved
Chen, W., Zheng, Z., Jiahui, C., Ngai, E., Zheng, P. & Zhou, Y. (2018). Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology. In: WWW '18: Proceedings of the 2018 World Wide Web Conference. Paper presented at World Wide Web Conference (TheWebConf 2018), April 23 - 27 2018, Lyon, France (pp. 1409-1418). ACM Digital Library
Open this publication in new window or tab >>Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology
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2018 (English)In: WWW '18: Proceedings of the 2018 World Wide Web Conference, ACM Digital Library, 2018, p. 1409-1418Conference paper, Published paper (Refereed)
Abstract [en]

Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-366224 (URN)10.1145/3178876.3186046 (DOI)000460379000138 ()978-1-4503-5639-8 (ISBN)
Conference
World Wide Web Conference (TheWebConf 2018), April 23 - 27 2018, Lyon, France
Projects
STINT Blockchain
Funder
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT)
Available from: 2018-11-18 Created: 2018-11-18 Last updated: 2019-04-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3454-8731

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