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Xiulei Liu
Researcher at Beijing Information Science & Technology University
Publications - 24
Citations - 608
Xiulei Liu is an academic researcher from Beijing Information Science & Technology University. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 4, co-authored 12 publications receiving 481 citations. Previous affiliations of Xiulei Liu include Beijing University of Posts and Telecommunications.
Papers
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Journal ArticleDOI
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
Omprakash Kaiwartya,Abdul Hanan Abdullah,Yue Cao,Ayman Altameem,Mukesh Prasad,Chin-Teng Lin,Xiulei Liu +6 more
TL;DR: This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects, and performs a qualitative comparison between IoV and VANETs.
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Geographic-Based Spray-and-Relay (GSaR): An Efficient Routing Scheme for DTNs
TL;DR: Design and evaluation of the proposed geographic-based spray-and-relay (GSaR) routing scheme in delay/disruption-tolerant networks show that GSaR is reliable for delivering messages before the expiration deadline and efficient for achieving low routing overhead ratio.
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T-MQM: Testbed-Based Multi-Metric Quality Measurement of Sensor Deployment for Precision Agriculture—A Case Study
Omprakash Kaiwartya,Abdul Hanan Abdullah,Yue Cao,Ram Shringar Raw,Sushil Kumar,D. K. Lobiyal,Ismail Fauzi Isnin,Xiulei Liu,Rajiv Ratn Shah +8 more
TL;DR: The investigative evaluation of the geometrical-model-based deployment patterns presented in this paper could be useful for practitioners and researchers in developing performance guaranteed applications for precision agriculture and novel coverage and connectivity models for deployment patterns.
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Common Knowledge Based and One-Shot Learning Enabled Multi-Task Traffic Classification
TL;DR: A multi-output DNN model simultaneously learning multi-task traffic classifications that shares the potential of meeting new demands in the future and meanwhile being able to achieve the classification with advanced speed and fair accuracy is proposed.
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Improving Software Fault Localization by Combining Spectrum and Mutation
TL;DR: A novel approach by combining spectrum and mutation to improve the fault localization accuracy is proposed and the accuracy of the proposed approach outperforms those of the SBFL and MBFL techniques.