Selected Publications

Here we list our selected publications within the different themes of our research. To get a simple overview of our work in Network Slicing, have a look at our recent paper summaries by the Exeter Science Centre.

Network Function Virtualisation and Network Slicing

Y. Wu, H.-N. Dai, H. Wang, Z. Xiong, and S. Guo, “A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory,” IEEE Communications Surveys and Tutorials, vol. 24, no. 2, pp. 1175-1211, 2022.
J. Cheng, Y. Wu, Y. Lin, Y. E, F. Tang and J. Ge, “VNE-HRL: A Proactive Virtual Network Embedding Algorithm Based on Hierarchical Reinforcement Learning,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 4075-4087, 2021.
H. Wang, Y. Wu, G. Min, and W. Miao, “A Graph Neural Network-based Digital Twin for Network Slicing Management,” IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1367-1376, 2022.
X. Cheng, Y. Wu, G. Min, A. Y. Zomaya and X. Fang, “Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 7, pp. 1600-1613, 2020.
Z. Yan, J. Ge, Y. Wu, L. Li and T. Li, “Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 6, pp. 1040-1057, 2020.
W. Miao, G. Min, Y. Wu, H. Huang, Z. Zhao, H. Wang, and C. Luo, “Stochastic Performance Analysis of Network Function Virtualisation in Future Internet,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 3, pp. 613-626, 2019.
H. Wang, Y. Wu, G. Min, J. Xu, and P. Tang, “Data-driven Dynamic Resource Scheduling for Network Slicing: A Deep Reinforcement Learning Approach,” Information Sciences, vol. 498, pp. 106-116, 2019.
X. Cheng, Y. Wu, G. Min, and A.Y. Zomaya, “Network Function Virtualization in Dynamic Networks: A Stochastic Perspective,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 10, pp. 2218-2232, 2018.

Autonomous Networks / Zero Touch Networks

G. Lin, J. Ge, and Y. Wu, “Towards Zero Touch Networks: From the Perspective of Hierarchical Language Systems,” IEEE Network, dot: 10.1109/MNET.117.2200037.
Y. Wu, “Ethically Responsible and Trustworthy Autonomous Systems for 6G,” IEEE Network, vol. 36, no. 4, pp. 126-133, 2022.
Y. Wu, G. Lin, and J. Ge, “Knowledge-Powered Explainable Artificial Intelligence for Network Automation toward 6G,” IEEE Network, vol. 36, no. 3, pp. 16-23, 2022.
G. Lin, J. Ge, Y. Wu, H. Li, T. Li, W. Mi, and Y. E, “Network Automation for Path Selection: A New Knowledge Transfer Approach,” IFIP/IEEE Networking-2021.
G. Lin, J. Ge, Y. Wu, H. Li, and L. Li, “Digital Twin Networks: Learning Dynamic Network Behaviours from Network Flows,” ISCC-2022.

System Modelling and Digital Twins

H. Wang, Y. Wu, G. Min, and W. Miao, “A Graph Neural Network-based Digital Twin for Network Slicing Management,” IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1367-1376, 2022.
W. Miao, G. Min, Y. Wu, H. Huang, Z. Zhao, H. Wang, and C. Luo, “Stochastic Performance Analysis of Network Function Virtualisation in Future Internet,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 3, pp. 613-626, 2019.
C. Li, Y. Wu, Z. Lu, J. Wang and Y. Hu, “A Multiperiod Multiobjective Portfolio Selection Model With Fuzzy Random Returns for Large Scale Securities Data,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 59-74, 2021.
Y. Wu, G. Min, D. Zhu, and L.T. Yang, “An Analytical Model for On-Chip Interconnects in Multimedia Embedded Systems,” ACM Transactions on Embedded Computing Systems, vol. 13, no. 1s, Article No. 29, 2013.
Y. Wu, G. Min, and L.T. Yang, “Performance Analysis of Hybrid Wireless Networks under Bursty and Correlated Traffic,” IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 449-454, 2013.
Y. Wu, G. Min, K. Li, and B. Javadi, “Modeling and Analysis of Communication Networks in Multicluster Systems under Spatio-Temporal Bursty Traffic,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 5, pp. 902-912, 2012.
Y. Wu, G. Min, and A.Y. Al-Dubai “A New Analytical Model for Multi-Hop Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, vol. 11, no. 5, pp. 1643-1648, 2012.
G. Min, Y. Wu, and A.Y. Al-Dubai, “Performance Modelling and Analysis of Cognitive Mesh Networks,” IEEE Transactions on Communications, vol. 60, no. 6, pp. 1474-1478, 2012.

Internet of Things / Industrial Internet of Things

Z. Huang, Y. Wu, N. Tempini, H. Lin, H. Yin, “An Energy-efficient and Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT,” ACM Transactions on Sensor Networks, doi: 10.1145/3543855.
Y. Wu, H.-N. Dai, H. Tang, “Graph Neural Networks for Anomaly Detection in Industrial Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9214-9231, 2022.
Y. Wu, Z. Wang, Y. Ma, and V.C.M. Leung, “Deep reinforcement learning for blockchain in industrial IoT: A Survey,” Computer Networks, vol. 191, pp. 108004, 2021, https://doi.org/10.1016/j.comnet.2021.108004.
H.-N. Dai, Y. Wu, H. Wang, M. Imran, and N. Haider, “Blockchain-empowered Edge Intelligence for Internet of Medical Things Against COVID-19“, IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 34-39, 2021.
Y. Wu, H. -N. Dai and H. Wang, “Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2300-2317, 2021.
Y. Wu, “Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9568-9579, 2021.
B. Yin, H. Yin, Y. Wu and Z. Jiang, “FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6348-6359, 2020.
L. Jiao, Y. Wu, J. Dong and Z. Jiang, “Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1572-1581, 2020.
B. Yin, Y. Wu, T. Hu, J. Dong and Z. Jiang, “An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1582-1593, 2020.
R. Zhou, X. Zhang, X. Wang, G. Yang, H. Wang, and Y. Wu, “Privacy-Preserving Data Search with Fine-grained Dynamic Search Right Management in Fog-assisted Internet of Things,” Information Sciences, vol. 491, pp. 251-264, 2019.
D.S. Sabareesh, G.V.P. Reddy, S. Jaiswal, J.M. Ppallan, K. Arunachalam, Y. Wu, “Redundant TCP Connector (RTC) for Improving the Performance of Mobile Devices,” WCNC-2019.
L. Cheng, J. Liu, G. Xu, Z. Zhang, H. Wang, H.N. Dai, Y. Wu, and W. Wang, “SCTSC: A Semicentralized Traffic Signal Control Mode With Attribute-Based Blockchain in IoVs,” IEEE Transactions on Computational Social Systems, vol. 6, no. 6, pp. 1373-1385, 2019.
H. Huang, H. Yin, G. Min, J. Zhang, Y. Wu, and X. Zhang, “Energy-aware Dual-path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks,” IEEE Transactions on Mobile Computing, vol. 17, no. 6, pp. 1339-1352, 2018.
H. Huang, H. Yin, G. Min, X. Zhang, W. Zhu, and Y. Wu, “Coordinate-assisted routing approach to bypass routing holes in wireless sensor networks,” IEEE Communications Magazine, vol. 55, no. 7, pp. 180-185, 2017.
H. Huang, H. Yin, G. Min, D.O. Wu, Y. Wu, T. Zuo, and K. Li, “Network Distance Prediction for Enabling Service-Oriented Applications Over Large-scale Networks,” IEEE Communications Magazine, vol. 53, no.8, pp. 166-174, 2015.

Edge Computing

S. Yang, Q. Dong, L. Cui, X. Chen, S. Lei, Y. Wu, and C. Luo, “EC-MASS: Towards an Efficient Edge Computing-based Multi-Video Scheduling System,” Computer Communications, vol. 193, pp. 355-364, 2022.
H. Lu, G. Xu, C.W. Sung, S. Mostafa, Y. Wu, “A Game Theoretical Balancing Approach for Offloaded Tasks in Edge Datacenters,” ICDCS-2022.
J. Zhang, Y. Wu and G. Min, “System Revenue Maximization for Offloading Decisions in Mobile Edge Computing,” ICC-2021.
Y. Wu, H. -N. Dai and H. Wang, “Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2300-2317, 2021.
J. Zhang, Y. Wu, G. Min, F. Hao and L. Cui, “Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1204-1217, 2020.
H.-N. Dai, Y. Wu, H. Wang, M. Imran, and N. Haider, “Blockchain-empowered Edge Intelligence for Internet of Medical Things Against COVID-19“, IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 34-39, 2021.
L. Cui, D. Su, Y. Zhou, L. Zhang, Y. Wu and S. Chen, “Edge Learning for Surveillance Video Uploading Sharing in Public Transport Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2274-2285, 2021.
Y. Wu, “Cloud-Edge Orchestration for the Internet of Things: Architecture and AI-Powered Data Processing,” IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12792-12805, 2021.
Z. Zhao, G. Min, W. Gao, Y. Wu, H. Duan, and Q. Ni, “Deploying Edge Computing Nodes for Large-scale IoT: A Diversity Aware Approach,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3606-3614, 2018.

Anomaly Detection

Z. Huang, Y. Wu, N. Tempini, H. Lin, H. Yin, “An Energy-efficient and Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT,” ACM Transactions on Sensor Networks, doi: 10.1145/3543855.
Y. Wu, H.-N. Dai, H. Tang, “Graph Neural Networks for Anomaly Detection in Industrial Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9214-9231, 2022.
X. Zhang, H. Tang, Y. Wu, X. Chen, “STCIN: Spatial-Temporal Cross Interaction Networks for Urban Anomaly Prediction,” CSCWD-2022.
H. Cao, H. Tang, Y. Wu, F. Wang and Y. Xu, “On Accurate Computation of Trajectory Similarity via Single Image Super-Resolution,” IJCNN-2021.
Y. Guo, Y. Wu, Y. Zhu, B. Yang and C. Han, “Anomaly Detection using Distributed Log Data: A Lightweight Federated Learning Approach,” IJCNN-2021.
P. Sun, Y. E, T. Li, Y. Wu, J. Ge, J. You, and B. Wu, “Context-Aware Learning for Anomaly Detection with Imbalanced Log Data,” HPCC-2021.
Y. Zuo, Y. Wu, G. Min, C. Huang and K. Pei, “An Intelligent Anomaly Detection Scheme for Micro-Services Architectures With Temporal and Spatial Data Analysis,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 548-561, 2020.
C. Huang, G. Min, Y. Wu, Y. Ying, K. Pei, and Z. Xiang, “Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems,” IEEE Transactions on Big Data, vol. 8, no. 1, pp. 60-72, 2022.
C. Huang, Y. Wu, G. Min, and Y. Ying, “Kernelized Convex Hull Approximation and its Applications in Data Description Tasks,” IJCNN-2018.
C. Huang, Y. Wu, Y. Zuo, K. Pei, and G. Min, “Towards Experienced Anomaly Detector through Reinforcement Learning,” AAAI-2018.

Software Defined Networking

Y. Yang, H. Jiang, Y. Wu, C. Han, Y. Lv, X. Li, B. Yang, S. Fdida, G. Xie, “C2QoS: Network QoS guarantee in vSwitch through CPU-cycle management,” Journal of Systems Architecture, vol. 116, 102148, 2021.
Y. Yang, H. Jiang, Y. Wu, Y. Lv, X. Li, and G. Xie, “C2QoS: CPU-Cycle based Network QoS Strategy in vSwitch of Public Cloud,” IM-2021.
S. Yang, L. Bai, L. Cui, Z. Ming, Y. Wu, S. Yu, H. Shen, Y. Pan, “An efficient pipeline processing scheme for programming Protocol-independent Packet Processors,” Journal of Network and Computer Applications, vol. 71, 102806, 2020.
W. Miao, G. Min, Y. Wu, H. Wang, and J. Hu, “Performance Modelling and Analysis of Software Defined Networking under Bursty Multimedia Traffic,” ACM Transactions on Multimedia Computing Communications and Applications, vol. 12, no. 5s, pp. 77:1-77:19, 2016.
G. Wang, Y. Zhao, J. Huang, Y. Wu, “An Effective Approach to Controller Placement in Software Defined Wide Area Networks,” IEEE Transactions on Network and Service Management, vol. 15, no. 1, pp. 344-355, 2018.
S. Yang, L. Cui, X. Deng, Q. Li, Y. Wu, M. Xu, D. Wang and J. Wu, “FISE: A Forwarding Table Structure for Enterprise Networks,” IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 1181-1196, 2020.
H. Huang, H. Yin, G. Min, H. Jiang, J. Zhang, and Y. Wu, “Data-Driven Information Plane in Software-Defined Networking,” IEEE Communications Magazine, vol. 55, no. 6, pp. 218-224, 2017.

Network Security, Privacy and Safety

X. Wang, M. Peng, H. Lin, Y. Wu, and X. Fan, “A Privacy-Enhanced Multi-Area Task Allocation Strategy for Blockchain Empowered Healthcare 4.0,” IEEE Transactions on Industrial Informatics, doi:10.1109/tii.2022.3189439.
H. Lu, G. Xu, C.W. Sung, S. Mostafa, Y. Wu, “HRaft: Adaptive Erasure Coded Data Maintenance for Consensus in Distributed Networks,” IPDPS-2022.
Y. Liu, P. Zhou, L. Yang, Y. Wu, Z. Xu, K. Liu, and X. Wang, “Privacy-Preserving Context-based Electric Vehicle Dispatching for Energy Scheduling in Microgrids: An Online Learning Approach,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 462-478, 2022.
Y. Wu, H. -N. Dai, H. Wang and K. -K. R. Choo, “Blockchain-Based Privacy Preservation for 5G-Enabled Drone Communications,” IEEE Network, vol. 35, no. 1, pp. 50-56, 2021.
Y. Xu, X. Yan, Y. Wu, Y. Hu, W. Liang and J. Zhang, “Hierarchical Bidirectional RNN for Safety-enhanced B5G Heterogeneous Networks,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 4, pp. 2946-2957, 2021.
Y. Wu, Y. Ma, H.-N. Dai, and H. Wang, “Deep Learning for Privacy Preservation in Autonomous Moving Platforms Enhanced 5G Heterogeneous Networks,” Computer Networks, vol. 185, pp. 107743, 2021, doi: 10.1016/j.comnet.2020.107743.
H.-N. Dai, Y. Wu, H. Wang, M. Imran, and N. Haider, “Blockchain-empowered Edge Intelligence for Internet of Medical Things Against COVID-19“, IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 34-39, 2021.
B. Yin, H. Yin, Y. Wu and Z. Jiang, “FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6348-6359, 2020.
H. Wang, S. Ma, C. Guo, Y. Wu, H.-N. Dai and D. Wu, “Blockchain-based Power Energy Trading Management,” ACM Transactions on Internet Technology, vol. 21, no. 2, Article 43, 2021.
Y. Ma, Y. Wu, J. Li, and J. Ge, “APCN: A Scalable Architecture for Balancing Accountability and Privacy in Large-scale Content-based Networks,” Information Sciences, vol. 527, pp. 511-532, 2020.
Z. Yao, J. Ge, Y. Wu, and L. Jian, “A Privacy Preserved and Credible Network Protocol,” Journal of Parallel and Distributed Computing, vol. 132, pp. 150-159, 2019.
T. Liu, J. Ge, Y. Wu, B. Dai, Z. Yao and J. Wen, “A New Bitcoin Address Association Method Using a Two-level Learner Model,” ICA3PP-2019.

Traffic Classification

J. Cheng, Y. Wu, Y. E, J. You, T. Li, H. Li, and J. Ge, “MATEC: A lightweight neural network for online encrypted traffic classification,” Computer Networks, 108472, doi: 10.1016/j.comnet.2021.108472.
X. Liu, J. You, Y. Wu, T. Li, L. Li, Z. Zhang and J. Ge, “Attention-Based Bidirectional GRU Networks for Efficient HTTPS Traffic Classification,” Information Sciences, vol. 541, pp. 297-315, 2020.
Yao Z, Ge J, Wu Y, Lin X, He R, Ma Y. (2020) “Encrypted traffic classification based on Gaussian mixture models and Hidden Markov Models,” Journal of Network and Computer Applications, volume 166, pp. 102711, 2020.
J. Cheng, R. He, Y. E, Y. Wu, J. You, and T. Li, “Real-Time Encrypted Traffic Classification via Lightweight Neural Networks,” Globecom-2020.
Z. Yao, L. Zhang, J. Ge, Y. Wu, and X. Zhang, “An Invisible Flow Watermarking for Traffic Tracking: A Hidden Markov Model Approach,” ICC-2019.
Z. Zou, J. Ge, H. Zheng, Y. Wu, C. Han, and Z. Yao, “Encrypted Traffic Classification with a Convolutional Long Short-Term Memory Neural Network,” HPCC-2018.
Z. Yao et al., “Meek-Based Tor Traffic Identification with Hidden Markov Model,” HPCC-2018.

Social Networks

Y. Xu, H. Liu, J. Ge, X. Zhang, J. Hu, Y. Wu, H. Lv, H. Shi, and W. Zhou, “Mining Weak Relations between Reviews for Opinion Spam Detection,” IEEE/ACM Transactions on Audio, Speech and Language Processing, DOI: 10.1109/TASLP.2022.3221008.
G. Li, J. Hu, Y. Wu, X. Zhang, W. Zhou, and H. Lyu, “A Heterogeneous Propagation Graph Model for Rumor Detection under the Relationship among Multiple Propagation Subtrees,” ECML-PKDD-2022.
Y. Yang, F. Hao, B. Pang, G. Min, and Y. Wu, “Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1020-1032, 2022.
E. D. Raj, G. Manogaran, G. Srivastava and Y. Wu, “Information Granulation-Based Community Detection for Social Networks,” IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 122-133, 2021.
Y. Lin, X. Wang, F. Hao, Y. Jiang, Y. Wu, G. Min, D. He, S. Zhu, and W. Zhao, “Dynamic Control of Fraud Information Spreading in Mobile Social Networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3725-3738, 2021.
E. Yang, F. Hao, J. Gao, Y. Wu, and G. Min, “Entity Spatio-temporal Evolution Summarization in Knowledge Graphs,” ICKG-2020.
F. Hao, G. Pang, Y. Wu, Z. Pi, L. Xia and G. Min, “Providing Appropriate Social Support to Prevention of Depression for Highly Anxious Sufferers,” IEEE Transactions on Computational Social Systems, vol. 6, no. 5, pp. 879-887, 2019.

Webpage Analysis

Z. Jiang, H. Yin, Y. Wu, Y. Lyu, G. Min, and X. Zhang, “Constructing Novel Block Layouts for Webpage Analysis,” ACM Transactions on Internet Technology, vol. 19, no. 35, Article No. 35, 2019.

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