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Publications (10 of 97) Show all publications
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
Abstract [en]

Blockchain technology becomes increasingly popular. It also attracts scams, for example, a Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help to deal with this issue and to provide reusable research data sets for future research, this paper collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. The extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart 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.

Keywords
Blockchain, smart contract, Ponzi Schemes, ethereum, data mining
National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-382362 (URN)10.1109/ACCESS.2019.2905769 (DOI)000464140500001 ()
Funder
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), IB2017-6978
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-04-24Bibliographically 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., 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
Abstract [en]

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Mobile edge computing (MEC), stochastic computation offloading, trajectory scheduling, unmanned aerial vehicle (UAV)-assisted
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-385573 (URN)10.1109/JIOT.2018.2890133 (DOI)000467564700201 ()
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17Bibliographically 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
Zhang, J., Hu, X., Ning, Z., Ngai, E.-H. C. H., Zhou, L., Wei, J., . . . Hu, B. (2018). Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks. IEEE Internet of Things Journal, 5(4), 2633-2645
Open this publication in new window or tab >>Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks
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2018 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 5, no 4, p. 2633-2645Article in journal (Refereed) Published
National Category
Communication Systems
Identifiers
urn:nbn:se:uu:diva-362838 (URN)10.1109/JIOT.2017.2786343 (DOI)000441428700038 ()
Available from: 2017-12-22 Created: 2018-10-12 Last updated: 2018-12-28Bibliographically approved
Tian, Y., Li, X., Sangaiah, A. K., Ngai, E., Song, Z., Zhang, L. & Wang, W. (2018). Privacy-preserving scheme in social participatory sensing based on Secure Multi-party Cooperation. Computer Communications, 119, 167-178
Open this publication in new window or tab >>Privacy-preserving scheme in social participatory sensing based on Secure Multi-party Cooperation
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2018 (English)In: Computer Communications, ISSN 0140-3664, E-ISSN 1873-703X, Vol. 119, p. 167-178Article in journal (Refereed) Published
Abstract [en]

Social participant sensing has been widely used to collect location related sensory data for various applications. In order to improve the Quality of Information (QoI) of the collected data with constrained budget, the application server needs to coordinate participants with different data collection capabilities and various incentive requirements. However, existing participant coordination methods either require participants to reveal their trajectories to the server which causes privacy leakage, or tradeoff the location accuracy of participants for privacy, thereby leading to lower QoI. In this paper, we propose a privacy-preserving scheme, which allows application server to provide quasi-optimal QoI for social sensing tasks without knowing participants’ trajectories and identity. More specifically, we first suggest a Secure Multi-party Cooperation (SMC) based approach to evaluate participant’s contribution in terms of QoI without disclosing each individual’s trajectory. Second, a fuzzy decision based approach which aims to finely balance data utility gain, incentive budget and inferable privacy protection ability is adopted to coordinate participant in an incremental way. Third, sensory data and incentive are encrypted and then transferred along with participant-chain in perturbed way to protect user privacy throughout the data uploading and incentive distribution procedure. Simulation results show that our proposed method can efficiently select appropriate participants to achieve better QoI than other methods, and can protect each participant’s privacy effectively.

National Category
Communication Systems
Identifiers
urn:nbn:se:uu:diva-336347 (URN)10.1016/j.comcom.2017.10.007 (DOI)000429513100013 ()
Available from: 2017-10-16 Created: 2017-12-13 Last updated: 2018-08-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3454-8731

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