Copyright notice:
The papers are listed
here to ensure timely dissemination of scholarly and technical work. Copyright
and all other rights therein are retained by the copyright holders and the
authors.
My complete publication list
can be found at Google Scholar.
Edited Books
· X.-W. Chu, H. Jiang, B. Li, D. Wang, and W. Wang, Quality,
Reliability, Security and Robustness in Heterogeneous Systems, 15th EAI
International Conference, QShine 2019, Shenzhen, China, November 22–23, 2019,
Proceedings, Springer, 2019.
· Hai Liu, Y.W. Leung, and X.-W. Chu, Ad Hoc and Sensor
Wireless Networks: Architectures, Algorithms and Protocols, eISBN:
978-1-60805-018-5, Bentham Science, 2009.
Book Chapters
·
Y. Jiang, M. Shi,
X. Shen, C. Lin, and X.-W. Chu, “A Broadcasting Authentication Protocol with
DoS & Fault Tolerance for Wireless Ad Hoc Networks,” in Security in RFID and Sensor Networks. CRC Press,
Taylor&Francis Group, Edited by Y. Zhang and P. Kitsos, April 2009.
·
X.-W. Chu, J.
Liu, and Y. Sun, “Address Allocation Mechanisms for Mobile Ad Hoc Networks,” in Handbook of Wireless Ad Hoc and Sensor Networks,
Edited by S. Misra, I. Woungang, S. C. Misra, Springer, 2008.
·
Weizhao Wang,
Xiangyang Li, and X-W. Chu, “Nash Equilibria and Dominant Strategies in
Routing,” in Encyclopedia of Algorithms, Springer, 2008, Edited
by Ming-Yang Kao.
·
Z. Zhang, X.-W.
Chu, B. Li, Y. Zhang, and N. Ghani, “Optical Virtual Private Network (OVPN): an
Overview,”
IEC Annual Review of Communications, Vol.
57, 2004.
·
X.-W. Chu, B. Li,
and K. Sohraby, “Routing and Wavelength Assignment versus Wavelength Converter
Placement in All-Optical Networks,” in
Handbook on Optical Communication Networks, CRC press, April 2003.
Machine Learning and Deep Learning
·
S. Zhang, Z. Wang, Q. Wang, J. Zhang, G. Wei, and
X.-W. Chu, “EDNet: Efficient Disparity Estimation with Cost Volume Combination
and Attention-based Spatial Residual,” CVPR
2021. [PrePrint]
·
Q. Wang, S. Zheng, Q. Yan, F. Deng, K. Zhao, and X.-W.
Chu, “IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep
Models for Disparity and Surface Normal Estimation,” ICME 2021. [PrePrint]
·
S. Shi, X.-W. Chu, and B. Li, “MG-WFBP: Merging
Gradients Wisely for Efficient Communication in Distributed Deep Learning,” IEEE Transactions on Parallel and
Distributed Systems, Vol. 32, No. 8, Pages 1903-917, Aug 2021.
·
W. Yang, Y. Qin, Z. Jiang, and X.-W. Chu, “Traffic
Management for Distributed Machine Learning in RDMA-enabled Data Center
Networks,” IEEE ICC 2021. [PrePrint]
·
S. Shi, X.-W. Chu, and B. Li, “Exploiting Simultaneous
Communications to Accelerate Data Parallel Distributed Deep Learning,” IEEE INFOCOM 2021. [PrePrint]
·
X. He, S. Wang, X.-W. Chu, S. Shi, J. Tang, X. Liu, C.
Yan, J. Zhang, and G. Ding, “Automated Model Design and Benchmarking of Deep
Learning Models for COVID-19 Detection with Chest CT Scans,” AAAI 2021. [PrePrint]
·
X. He, K. Zhao, and X.-W. Chu, “AutoML: A Survey of
the State-of-the-Art,” Knowledge-Based
Systems, Vol. 215, Jan 2021.
·
S. Shi, Z. Tang, X.-W. Chu, C. Liu, W. Wang, and B.
Li, “A Quantitative Survey of Communication Optimizations in Distributed Deep
Learning,” IEEE Network, to appear. [PrePrint]
·
Y. Wang, Q. Wang, and X.-W. Chu, “Energy-efficient
Inference Service of Transformer-based Deep Learning Models on GPUs,” IEEE GreenCom 2020, Greece, 2020. (Best Paper Award)
·
R. Zeng, S. Zhang, J. Wang, and X.-W. Chu, “FMore: An
Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC,” IEEE ICDCS 2020, Singapore, 2020. [PrePrint]
·
Z. Tang, S. Shi, and X.-W. Chu,
“Communication-Efficient Decentralized Learning with Sparsification and
Adaptive Peer Selection,” IEEE ICDCS 2020
(Poster), Singapore, 2020. [Long
Version]
·
Y. Wang, Q. Wang, S. Shi, X. He, Z. Tang, K. Zhao,
X.-W. Chu, “Benchmarking the Performance and Power of AI Accelerators for AI
Training,” 3rd High Performance Machine
Learning Workshop (HPML 2020), co-located with IEEE CCGrid 2020, Melbourne,
Australia, 2020. [PrePrint]
·
S. Shi, Z. Tang, Q. Wang, K. Zhao, and X.-W. Chu,
“Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with
Convergence Guarantees,” The 24th
European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, June 2020. [PrePrint]
·
Q. Wang, S. Shi, S. Zheng, K. Zhao, and X.-W. Chu,
“FADNet: A Fast and Accurate Network for Disparity Estimation,” IEEE ICRA 2020, Paris, France, May-June
2020. [PrePrint]
·
D. Yan, W. Wang, and X.-W. Chu, “Demystifying Tensor
Cores to Optimize Half-Precision Matrix Multiply,” IEEE IPDPS 2020, New Orleans, USA, May 2020. [PrePrint]
·
S. Shi, Q. Wang, X-W. Chu, B. Li, Y. Qin, R. Liu, and
X. Zhao, “Communication-Efficient Distributed Deep Learning with Merged
Gradient Sparsification on GPUs,” IEEE
INFOCOM 2020, Beijing, China, May 2020. [PrePrint]
·
D. Yan, W. Wang, and X.-W. Chu, “Optimizing Batched
Winograd Convolution on GPUs,” ACM PPoPP
2020, San Diego, USA, Feb 2020. [GitHub]
·
Z. Sun, X. Zhang, Y. Ye, X.-W. Chu, and Z. Liu, “A
Probabilistic Approach towards an Unbiased Semi-supervised Cluster Tree,” Knowledge-Based Systems, Dec 2019.
·
X. Zhou, Z. Tang, W. Xu, F. Meng, X.-W. Chu, K. Xin,
and G. Fu, “Deep learning Identifies Burst Locations in Water Distribution
Networks,” Water Research, Vol. 166,
Dec 2019.
·
S. Shi, K.
Zhao, Q. Wang, Z. Tang, and X.-W.
Chu, “A Convergence Analysis of Distributed SGD
with Communication-Efficient Gradient Sparsification,” IJCAI 2019, Macau, P.R.C., August 2019. [PDF]
·
Z. Tang, Y. Wang, Q.
Wang, and X.-W. Chu, “The Impact of GPU DVFS
on the Energy and Performance of Deep Learning: an Empirical Study,” ACM e-Energy 2019, Phoenix,
AZ, USA, June 2019. (notes paper) [PrePrint]
·
S. Shi, Q.
Wang, K. Zhao, Z. Tang, Y.
Wang, X. Huang, and X.-W.
Chu, “A Distributed Synchronous SGD Algorithm with
Global Top-k Sparsification for Low
Bandwidth Networks,” IEEE ICDCS 2019,
Dallas, Texas, USA, July 2019. [arXiv:1901.04359].
·
S. Shi, X.-W. Chu, and B. Li, “MG-WFBP: Efficient Data
Communication for Distributed Synchronous SGD Algorithms,” IEEE INFOCOM 2019, Paris, France, May 2019. [PrePrint]
·
X. Jia, S. Song, S. Shi, W. He, Y. Wang, H. Rong, F.
Zhou, L. Xie, Z. Guo, Y. Yang, L. Yu, T. Chen, G. Hu, and X.-W. Chu, “Highly
Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet
in Four Minutes,” NeurIPS Workshop on
Systems for ML and Open Source Software, Montreal, Canada, Dec 2018. [PDF]
·
S. Shi, Q. Wang, X.-W. Chu, and B. Li, “A DAG Model of
Synchronous Stochastic Gradient Descent in Distributed Deep Learning,” IEEE International Conference on Parallel
and Distributed Systems (ICPADS) 2018, Singapore, Dec 2018.
·
S. Shi, Q. Wang, and X.-W. Chu, “Performance Modeling and
Evaluation of Distributed Deep Learning Frameworks on GPUs,” IEEE DataCom 2018, Athens, Greece,
August 2018. (Best Paper Award)
·
S. Shi, P. Xu, and X.-W. Chu, “Supervised Learning
Based Algorithm Selection for Deep Neural Networks,” IEEE ICPADS 2017, Shenzhen, China, Dec 2017.
·
P. Xu, S. Shi, and X.-W. Chu, “Performance Evaluation of Deep Learning Tools in
Docker Containers,” the
3rd International Conference on Big Data Computing and Communications,
Chengdu, China, Aug 2017.
·
S. Shi, Q. Wang, P. Xu, and X.-W. Chu, “Benchmarking State-of-the-Art Deep Learning Software
Tools,” the
7th International Conference on Cloud Computing and Big Data (CCBD 2016),
Macau, China, Nov 2016. [Long version at https://arxiv.org/abs/1608.07249]
GPU Computing
·
D. Yan, W. Wang, and X.-W. Chu, “Simplifying Low-Level
GPU Programming with GAS,” ACM PPoPP 2021
(Poster), Virtual Conference, Feb-March 2021.
·
Q.
Wang and X.-W. Chu, “GPGPU Performance Estimation with Core and Memory
Frequency Scaling,” IEEE Transactions on
Parallel and Distributed Systems, Vol. 31, No. 12, pages 2865-2881, Dec
2020.
·
D. Yan, W. Wang, and X.-W. Chu, “Demystifying Tensor
Cores to Optimize Half-Precision Matrix Multiply,” IEEE IPDPS 2020, New Orleans, USA, May 2020. [PrePrint]
·
D. Yan, W. Wang, and X.-W. Chu, “Optimizing Batched
Winograd Convolution on GPUs,” ACM PPoPP
2020, San Diego, USA, Feb 2020. [PDF]
·
Z. Tang, Y. Wang, Q.
Wang, and X.-W. Chu, “The Impact of GPU DVFS
on the Energy and Performance of Deep Learning: an Empirical Study,” ACM e-Energy 2019, Phoenix,
AZ, USA, June 2019. (notes paper) [PrePrint]
·
Q.
Wang and X.-W. Chu, “GPGPU Performance Estimation with Core and Memory
Frequency Scaling,” IEEE International
Conference on Parallel and Distributed Systems (ICPADS) 2018, Singapore,
Dec 2018. [A poster of this work has been presented at The International Conference for High Performance Computing,
Networking, Storage, and Analysis (SC18), Dallas, USA, Nov 2018.]
·
C.
Liu, Q. Wang, X.-W. Chu, and Y.-W. Leung, “G-CRS: GPU Accelerated Cauchy Reed-Solomon
Coding,” IEEE Transactions on Parallel
and Distributed Systems, Vol. 29, No. 7, pages 1482-1498, July 2018. [PrePrint] [Source Code]
·
Q.
Wang and X.-W. Chu, “GPGPU Power Estimation with Core and Memory Frequency
Scaling,” GreenMetrics 2017, in
conjunction with ACM Sigmetrics 2017,
Urbana-Champaign, IL, USA June 2017. [PDF]
·
Q.
Wang, P. Xu, Y. Zhang, and X.-W. Chu, “EPPMiner: An Extended Benchmark Suite
for Energy, Power and Performance Characterization of Heterogeneous
Architecture,” ACM e-Energy 2017,
Hong Kong, May 2017. [PDF]
·
V.
Chau, X.-W. Chu, H. Liu, and Y.-W. Leung, “Energy Efficient Job Scheduling with
DVFS for CPU-GPU Heterogeneous Systems,” ACM
e-Energy 2017, Hong Kong, May 2017. [PDF]
·
X.
Mei, X.-W. Chu, H. Liu, Y.-W. Leung, and Z. Li, “Energy Efficient Real-time
Task Scheduling on CPU-GPU Hybrid Clusters,” IEEE INFOCOM 2017, GA, USA, May 2017. (Acceptance rate = 20.93%) [PDF]
·
X. Mei
and X.-W. Chu, “Dissecting GPU Memory Hierarchy through Microbenchmarking,” IEEE Transactions on Parallel and
Distributed Systems, Vol.
28. No. 1, pages 72-86, Jan 2017. [Source Code][Preprint]
·
X.
Mei, Q. Wang, and X.-W. Chu, “A Survey and Measurement Study of GPU DVFS on
Energy Conservation,” Digital
Communications and Networks, 2017 [Preprint]
·
X.-W.
Chu, C. Liu, K. Ouyang, L. S. Yung, H. Liu, and Y.-W. Leung, “PErasure: a
Parallel Cauchy Reed-Solomon Coding Library for GPUs,” IEEE ICC 2015, London, UK, June 2015. [PDF]
[Demo
Code]
·
X.
Mei, K. Zhao, C. Liu, and X.-W. Chu, “Benchmarking the Memory Hierarchy of
Modern GPUs,” the 11th IFIP International
Conference on Network and Parallel Computing (NPC 2014), Taiwan, September
2014. [PDF]
·
K.
Zhao and X.-W. Chu, “G-BLASTN: Accelerating Nucleotide Alignment by Graphics
Processors,” Bioinformatics,
30(10):1384-91, May 2014. [Preprint]
[To get the final version, please access through Bioinformatics]
[Website of
G-BLASTN]
·
Y. Li, H. Chi, L.
Xia, and X.-W. Chu, “Accelerating the Scoring Module of Mass Spectrometry-Based
Protein Identification Using GPUs,” BMC
Bioinformatics, 15:121, April 2014. [Open Access]
·
Q.
Li, C. Zhong, K. Li, G. Zhang, X. Lu, Q. Zhang, K. Zhao and X.-W. Chu, “A
Parallel Lattice Boltzmann Method for Large Eddy Simulation on Multiple GPUs,” Springer Computing Journal, Vol. 96, No. 6, Page 479-501, 2014. [PDF]
· X. Mei, L. Yung, K. Zhao, and X.-W. Chu, “A
Measurement Study of GPU DVFS on Energy Conservation,” USENIX HotPower’13, co-located with the 24th ACM Symposium on
Operating Systems Principles (SOSP), Pennsylvania, USA, November 2013. [PDF]
· Y. Li, K. Zhao, X.-W. Chu, and Jiming Liu, “Speeding up K-Means Algorithm by GPUs,” Journal of
Computer and System Science, Vol. 79,
No. 2, pages 216-229, March 2013. [PDF]
·
Y.
Li, L. Xia, H. Chi, and X.-W. Chu, “Accelerating Mass Spectrometry-Based
Protein Identification Using GPUs,” Poster, RECOMB 2013, Beijing, China. [PDF]
·
K.
Zhao and X.-W. Chu, “GPU-BLASTN: Accelerating Nucleotide Sequence Alignment by
GPUs,” Poster, RECOMB 2013, Beijing, China. [PDF]
· X.-W. Chu, K. Zhao, and Z. Li, “Tsunami: Massively
Parallel Homomorphic Hashing on Many-core GPUs,” Concurrency and Computation: Practice
& Experience, Vol. 24, No. 7, pages
2028-2039, Dec 2012. (online). [PDF]
·
Q.
Li, C. Zhong, K. Li, G. Zhang, X. Lu, Q. Zhang, K. Zhao and X.-W. Chu,
“Implementation of a Lattice Boltzmann Method for Large Eddy Simulation on
Multiple GPUs,” The third International
Workshop on Frontier of GPU Computing, Liverpool, UK, June 2012. [PDF]
·
Q.
Li, C. Zhong, K. Zhao, X. Mei and X.-W. Chu, “Implementation and Analysis of
AES Encryption on GPU,” The third
International Workshop on Frontier of GPU Computing, Liverpool, UK, June
2012. [PDF]
·
Y.
Li and X.-W. Chu, “Speeding up Scoring Module of Mass Spectrometry Based
Protein Identification by GPU,” The Fifth
International Symposium on Advances of High Performance Computing and
Networking, Liverpool, UK, June 2012. [PDF]
· C.-M. Liu, T. Wong, E.
Wu, R. Luo, S.-M. Yiu, Y. Li, B. Wang, C. Yu, X.-W. Chu, K. Zhao, R. Li, T.-W.
Lam, “SOAP3: Ultra-fast GPU-based parallel alignment tool for short reads,” Applications Note, Bioinformatics, January 2012. [PDF]
[SOAP3]
· Y. Li, K. Zhao, X.-W. Chu, and Jiming Liu, “Speeding
up K-Means Algorithm by GPUs,” IEEE CIT 2010, July 2010,
Bradford, UK.
(Best Paper Award. 2 out of 485
submissions.) [PDF]
· K. Zhao and X.-W. Chu, “GPUMP: a Multiple-Precision Integer
Library for GPUs,” The 1st International Workshop on
Frontier of GPU Computing, Bradford,
UK, June-July 2010. [PDF]
· X.-W. Chu, K. Zhao, and M. Wang, “Accelerating Network
Coding on Many-core GPUs and Multi-core CPUs,” Journal of Communications, Special Issue on Network Coding and Applications,
Vol 4, No 11 (2009), pages 902-909, Dec 2009. [OPEN
ACCESS]
· K. Zhao, X.-W. Chu, M. Wang, and Y. Jiang, “Speeding
Up Homomorphic Hashing Using GPUs,” IEEE ICC’09,
Dresden, Germany, June 2009.
· X.-W. Chu, K. Zhao, and M. Wang, “Practical Random
Linear Network Coding on GPUs”, IFIP
Networking’09, Archen, Germany, May 2009. (Acceptance rate = 21.5%) [PDF]
· X.-W. Chu, K. Zhao, and M. Wang, “Massively Parallel
Network Coding on GPUs,” IEEE IPCCC’08, Austin, Texas,
USA, Dec 2008. [PDF]
Wireless Networks
· L. Yu, Y.-W. Leung, X.-W. Chu, and J. Ng,
“Multi-Fingerprint for Wireless Localization in Time-Varying Indoor
Environment,” IEEE Globecom 2020, Dec
2020.
· S. Qiu, X.-W. Chu, Y.-W. Leung, and J. Ng, “Joint
Access Point Placement and Power-Channel-Resource-Unit Assignment for
802.11ax-Based Dense WiFi with QoS Requirements,” IEEE INFOCOM 2020, Beijing, China, May 2020. [PrePrint]
· L. Yu, Y.-W. Leung, X.-W. Chu, and J. K. Y. Ng,
“Minimal Discrepancy Placement of Sniffers and Calibrators for Wireless Indoor
Localization,” IEEE PIMRC 2019,
Istanbul, Turkey, Sept. 2019.
· Z. Lin, H. Liu, L. Yu, Y.-W. Leung, and X.-W. Chu,
“ZOS: A Fast Rendezvous Algorithm Based on Set of Available Channels for
Cognitive Radios,” IEEE PIMRC 2018,
Bologna, Italy, Sept. 2018.
· O. Oshiga, X.-W. Chu, Y.-W. Leung, and J. Ng, “Anchor
Selection for Localization in Large Indoor Venue,” IEEE/ACM 26th International Symposium on Quality of Service (IWQoS),
Banff, Canada, June 2018.
· L. Yu, H. Liu, Y.-W. Leung, X.-W. Chu, and Z. Lin,
“Cooperative Rendezvous Protocol for Multiple User-Pairs in Cognitive Radio
Networks,” IEEE Wireless Communications
and Networking Conference, Barcelona, Spain, April 2018.
· F. Zhang, H. Liu, Y.-W. Leung, X.-W. Chu, and B. Jin,
“CBS: Community-based Bus System as Routing Backbone for Vehicular Ad Hoc
Networks,” IEEE Transactions on Mobile
Computing, Vol. 16, No. 8, pages 2132-2146, August 2017.
· F. Zhang, B. Jin, H. Liu, Y.-W. Leung, and X.-W. Chu,
“Minimum-Cost Recruitment of Mobile Crowdsensing in Cellular Networks,” IEEE Globecom 2016, Washington, USA, 4-8
December, 2016.
· L. Zhang, C. Zhang, J. Liu, X.-W. Chu, K. Xu, and H.
Wang, “Power-Aware Wireless Transmission for Computation Offloading in Mobile
Cloud,” IEEE ICCCN 2016, Hawaii, USA,
1-4 August, 2016.
· Y. Lu, H. Liu, Y.-W. Leung, X.-W. Chu, and Z. Lin,
“Adjustable Rendezvous in Multi-Radio Cognitive Radio Networks,” IEEE Globecom 2015, San Diego, CA, USA,
6-10 December, 2015.
·
Z. Lin, H. Liu,
X.-W. Chu, Y.-W. Leung, and I. Stojmenovic, “Constructing
Connected-Dominating-Set with Maximum Lifetime in Cognitive Radio Networks,” IEEE Transactions on Computers, to
appear. [PDF]
·
L. Yu, H. Liu,
Y.-W. Leung, X.-W. Chu, and Z. Lin, “Multiple Radios for Fast Rendezvous in
Cognitive Radio Networks,” IEEE Transactions on Mobile Computing, Vol. 14, No. 9, pages 1917-1931,
September 2015. [PDF]
· F. Zhang, H. Liu, Y.-W. Leung, X.-W. Chu, and B. Jin,
“Community-based Bus System as Routing Backbone for Vehicular Ad Hoc Networks,”
IEEE ICDCS 2015, Hilton Downtown,
Columbus, Ohio, USA, 29 Jun.-2 Jul., 2015. [PDF] (Acceptance rate = 12.9%)
·
L. Yu, H. Liu,
Y.-W. Leung, X.-W. Chu, and Z. Lin, “Channel-Hopping Based on Available Channel
Set for Rendezvous of Cognitive Radios,” IEEE
ICC 2014, Sydney, Australia, 10-14 June, 2014. [PDF]
·
H. Liu, X.-W.
Chu, Y.-W. Leung, and R. Du, “Minimum-Cost Sensor Placement for Required
Lifetime in Wireless Sensor-Target Surveillance Networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 9,
pp.1783-1796, September 2013.
· Z. Lin, H. Liu, X.-W. Chu, and Y.-W.
Leung, “Enhanced Jump-Stay Rendezvous Algorithm for Cognitive Radio
Networks,” IEEE Communications Letters,
Vol. 17, No. 9, Page 1742-1745, September 2013.
·
Gary K.W. Wong,
H. Liu, X.-W. Chu, Y.-W. Leung, and C. Xie, “Efficient Broadcasting in
Multi-hop Wireless Networks with A Realistic Physical Layer,” Ad Hoc Networks,
Vol. 11, No. 4, pages 1305-1318, June 2013.
·
L. Yu, H. Liu,
Y.-W. Leung, X.-W. Chu, and Z. Lin, “Multiple Radios for Effective Rendezvous
in Cognitive Radio Networks,” IEEE ICC
2013, Budapest, Hungary, 9-13 June, 2013. [PDF]
·
H. Liu, Z. Lin, X.-W. Chu, and Y.-W.
Leung, “Jump-Stay Rendezvous Algorithm for Cognitive Radio Networks,” IEEE
Transactions on Parallel and Distributed Systems, Vol. 23, No. 10,
pages 1867-1881, Oct 2012. (Spotlight
Paper of the issue)
·
Z. Lin, H. Liu,
X.-W. Chu, and Y.-W. Leung, “Ring-Walk Rendezvous Algorithms for Cognitive
Radio Networks,” Ad Hoc & Sensor Wireless Networks, Vol. 16, No. 4, pages
243-271, 2012.
·
H.
Liu, Y. Zhou, X.-W. Chu, Y.-W. Leung, and Z. Hao, “Generalized-Bi-Connectivity
for Fault Tolerant Cognitive Radio Networks,” IEEE ICCCN 2012, Munich, Germany, July-August 2012.
·
Z.
Lin, H. Liu, X.-W. Chu, Y.-W. Leung, and I. Stojmenovic, “Maximizing Lifetime
of Connected-Dominating-Set in Cognitive Radio Networks,” IFIP Networking 2012, Prague, Czech Republic, 21-25 May. 2012.
·
H.
Liu, Z. Lin, X.-W. Chu, and Y.-W. Leung, “Taxonomy and Challenges of Rendezvous
Algorithms in Cognitive Radio Networks,” The International Conference on
Computing, Networking and Communications (ICNC 2012), Maui, Hawaii, USA, 30
Jan.-2 Feb. 2012. (Invited Position Paper)
·
H. Liu, X.-W.
Chu, Y.-W. Leung, X. Jia, and P.-J. Wan, “General Maximal Lifetime
Sensor-Target Surveillance Problem and Its Solution,” IEEE Transactions on Parallel and Distributed Systems, Vol. 22, No. 10, pages 1757-1765, Oct. 2011.
·
Z.
Lin, H. Liu, X.-W. Chu, and Y-W. Leung, “Jump-Stay Based Channel-Hopping
Algorithm with Guaranteed Rendezvous for Cognitive Radio Networks,” IEEE
INFOCOM 2011, Shanghai, China, 10-15 Apr. 2011. (Acceptance rate = 15.9%)
·
H.
Liu, Z. Lin, X.-W. Chu, and Y-W. Leung, “Ring-Walk Based Channel-Hopping
Algorithms with Guaranteed Rendezvous for Cognitive Radio Networks,” The 2nd IEEE International Workshop on
Wireless Sensor, Actuator and Robot Networks (WiSARN2010-FALL), in conjunction
with IEEE/ACM CPSCom, Hangzhou, China, 18-20 Dec. 2010. (Invited Paper)
·
H. Liu, X.-W.
Chu, Y.-W. Leung, and R. Du, “Simple Movement Control Algorithm for Bi-Connectivity
in Robotic Sensor Networks,” IEEE Journal on Selected Areas in Communications, Vol. 28, No. 7, pages. 994-1005, Sept 2010.
·
H. Liu, X.-W.
Chu, Y. W. Leung, X. Jia, and P. J. Wan, “Maximizing Lifetime of Sensor-Target
Surveillance in Wireless Sensor Networks,” IEEE Globecom 2009, Dec. 2009.
·
X.-Y. Li, Y. Wu,
H. Chen, X.-W. Chu, Y. Wu, and Y. Qi, “Reliable and Energy Efficient Routing
for Static Wireless Ad Hoc Networks with Unreliable Links,” IEEE
Transactions on Parallel and Distributed Systems, Vol. 20, no. 10, pp.
1408-1421, Oct. 2009.
·
X.-Y. Li, A.
Nusairat, Y. Wu, Y. Qi, J. Zhao, X.-W. Chu, and Y. Liu, “Joint Throughput
Optimization for Next Generation Wireless Mesh Networks,” IEEE
Transactions on Mobile Computing,
Vol. 8, No. 7, pages 895-909, July 2009.
·
X.-W. Chu, Y. Sun, K. Xu, Z. Sakendar, and J. Liu, “Quadratic Residue Based
Address Allocation for Mobile Ad Hoc Networks,” IEEE ICC’08, Beijing, China, May 2008. [PDF]
·
Y. Yan and X.-W.
Chu, “An
Analytical Model for IEEE 802.11 Point-to-Point Link,” IEEE ICC’08, Beijing, China, May 2008. [PDF]
·
X.-W. Chu,
“Provisioning of Parameterized Quality of Service in 802.11e Based Wireless
Mesh Networks,” ACM/Springer MONET, Vol. 13,
No.1-2, pages 6-18, April 2008. [PDF]
·
Y. Jiang, C. Lin,
M. Shi, X. Shen, and X.-W. Chu , “A DoS and Fault Tolerant Authentication
Protocol for Group Communications in Ad Hoc Networks,” Computer
Communications, Vol. 30, Nos. 11/12,
pp. 2428-2441, September 2007.
·
X.-Y. Li, Y. Shu,
H. Chen, X.-W. Chu, and Y. Wu, “Energy Efficient Routing with
Unreliable Links in Wireless Networks,” The Third IEEE International Conference on
Mobile Ad-hoc and Sensor Systems (MASS 2006), 2006.
Distributed Systems/Cloud
Computing/Smart Grid
· Z. Peng, C. Xu, H. Wang, J. Huang, J. Xu, and X.-W. Chu,
“P2B-Trace: Privacy-Preserving Blockchain-based Contact Tracing to
Combat Pandemics,” SIGMOD 2021.
· Z. Peng, J. Xu, X.-W. Chu, S. Gao, Y. Yao, and R. Gu, “VFChain:
Enabling Verifiable and Auditable Federated Learning via Blockchain Systems,” IEEE Transactions on Network Science and
Engineering, to appear.
· C. Liu, Q. Wang, X.-W. Chu, Y.-W. Leung, and H. Liu,
“ESetStore: An Erasure-Coded Storage System with Fast Data Recovery,” IEEE Transactions on Parallel and
Distributed Systems, Vol. 31, No. 19, pages 2001-2016, September 2020.
· H. Zhao, H. Liu, Y.-W. Leung, and X.-W. Chu,
“Self-Adaptive Collective Motion of Swarm Robots,” IEEE Transactions on Automation Science and Engineering, Vol. 15,
No. 4, pages 1533-1545, October 2018.
· L. Ma, H. Liu, Y.-W. Leung, and X.-W. Chu, “Joint
VM-Switch Consolidation for Energy Efficiency in Data Centers,” IEEE Globecom 2016, Washington, USA, 4-8
December, 2016.
· Albert Y.S. Lam, Y.-W. Leung, and X.-W. Chu,
“Autonomous Vehicle Public Transportation System: Scheduling and Admission
Control,” IEEE Transactions on
Intelligent Transportation Systems, Vol. 17, No. 5, pages 1210-1226, May
2016.
· S. Fu, J. Liu, X.-W. Chu, and Y. Hu, “Toward a
Standard Interface for Cloud Providers: The Container as the Narrow Waist,” IEEE Internet Computing, Vol. 20, No. 2,
pages 66-71, March-April 2016.
· C. Liu, K. Ouyang, X.-W. Chu, H. Liu, and Y.-W. Leung,
“R-Memcached: a Reliable In-Memory Cache System for Big Key-Value Stores,” Tsinghua Science and Technology, Vol.
20, No. 6, pages 560-573, December 2015.
· J. Zhao, X.-W. Chu, H. Liu, Y.-W. Leung, and Z. Li,
“Online Procurement Auctions for Resource Pooling in Client-Assisted Cloud
Storage Systems,” IEEE INFOCOM 2015,
Hong Kong, April-May, 2015.
· Albert Y.S. Lam, Y.-W. Leung, and X.-W. Chu, “Electric
Vehicle Charging Station Placement: Formulation, Complexity, and Solutions,” IEEE Transactions on Smart Grid, Vol. 5,
No. 6, pages 2846–2856, Nov. 2014.
· X.-W. Chu, X. Chen, A. L. Jia, J. A. Pouwelse, and D.
H. J. Epema, “Dissecting Darknets: Measurement and Performance Analysis,” ACM Transactions on Internet Technology,
Vol. 13, No. 3, May 2014.
· Z. Li and X.-W. Chu, “On Achieving Group-Strategyproof
Multicast,” IEEE Transactions on Parallel
and Distributed Systems, Vol. 23, No. 5, pages 913-923, May 2012.
· X. Kong, C. Lin, Y. Jiang, W. Yan, and X.-W. Chu,
“Efficient Dynamic Task Scheduling in Virtualized Data Centers with Fuzzy
Prediction,” Journal of Network and
Computer Applications, Elsevier, Vol. 34, No. 4, July 2011, ISSN 1084-8045.
· X.-W. Chu and Y. Jiang, “Random Linear Network Coding
for Peer-to-Peer Applications,” IEEE
Network, Vol. 24, No. 4, pages 35-39, July-August 2010.
· X.-W. Chu, K. Zhao, Z. Li, and A. Mahanti, “Auction
Based On-Demand P2P Media Streaming with Minimum Cost,” IEEE Transactions on Parallel and Distributed Systems, Vol. 20, No.
12, pages 1816-1829, Dec 2009.
· J.-C. Liu, J. Xu, and X.-W. Chu, “Fine-Grained
Scalable Video Caching for Heterogeneous Clients,” IEEE Transactions on Multimedia, Vol. 8, No. 5, pages 1011-1020,
October 2006.
· J. Liu, X.-W. Chu, and J. Xu, “Proxy Cache Management
for Fine-Grained Scalable Video Streaming,” IEEE
INFOCOM'04, Hong Kong, March 2004.
Optical Networks
· X.-W. Chu, H. Yin, and X.-Y. Li, “Lightpath Rerouting
in Wavelength-Routed WDM Networks,” OSA
Journal of Optical Networking, Vol. 7, Issue 8, pages 721-735, July 2008.
· X.-W. Chu and B. Li, “Dynamic Routing and Wavelength
Assignment in the Presence of Wavelength Conversion for All-Optical Networks,” IEEE/ACM Transactions on Networking,
Vol. 12, No. 3, pages 704-715, June 2005.
· X.-W. Chu and J. Liu, “DLCR: A New Adaptive Routing
Scheme in WDM Mesh Networks,” IEEE
International Conference on Communications (ICC'05), pp. 1797-1801, Seoul,
Korea, May 2005.
· X.-W. Chu, J. Liu, B. Li, and Z. Zhang, “Analytical
Model of Sparse-Partial Wavelength Conversion in Wavelength-Routed WDM
Networks,” IEEE Communications Letters,
Vol. 9, No. 1, pages 69-71, January 2005.
· X.-W. Chu, J. Liu, and Z. Zhang, “Analysis of
Sparse-Partial Wavelength Conversion in Wavelength-Routed WDM Networks,” IEEE INFOCOM'04, Hong Kong, March 2004.
· K. Sohraby, Z. Zhang, X.-W. Chu, and B. Li, “Resource
Management in Integrated Optical Networks,” IEEE
Journal on Selected Areas in Communications, Vol. 21, No. 7, pages
1052-1062, September 2003.
· B. Li, X.-W. Chu, and K. Sohraby, “Routing and
Wavelength Assignment vs. Wavelength Converter Placement in All-Optical
Networks,” IEEE Communications Magazine,
Vol. 41, No. 8, pages S22-S28, August 2003.
· X.-W. Chu, B. Li, and I. Chlamtac, “Wavelength
Converter Placement under Different RWA Algorithms in Wavelength-Routed
All-Optical Networks,” IEEE Transactions
on Communications, Vol. 51, No. 4, pages 607-617, April 2003.
· X.-W. Chu, B. Li, and Z. Zhang, “A Dynamic RWA
Algorithm in a Wavelength-Routed All-Optical Network with Wavelength
Converters,” IEEE INFOCOM'03, pages
1795-1804, March 2003.