Journal

Conference

Journal

2024/2023

[1] Liangxin Qian, Chang Liu, and Jun Zhao, “User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse over 6G Wireless Communications,” IEEE Transactions on Wireless Communications (TWC), 2024.

[2] Terence Jie Chua, Wenhan Yu, and Jun Zhao, “Play to Earn in Augmented Reality with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach,” IEEE Transactions on Wireless Communications (TWC), 2024, Major Revision.

[3] Wenhan Yu and Jun Zhao, “Optimization for 6G Wireless Communications with Heterogeneous VR and Non-VR 360-Degree Videos: A Differentiated Reinforcement Learning Approach,” IEEE Transactions on Wireless Communications (TWC), 2024, Major Revision.

[4] Peiyuan Si, Renyang Liu, Liangxin Qian, Jun Zhao, Kwok-Yan Lam, “Post-Deployment Fine-Tunable Semantic Communication”, IEEE Transactions on Wireless Communications (TWC), 2024, Major Revision.

[5] Jun Zhao, Liangxin Qian, and Wenhan Yu, “Human-Centric Resource Allocation in the Metaverse over Wireless Communications,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 42, no. 3, pp. 514-537, March 2024. Available: https://arxiv.org/pdf/2304.00355.

[6] Ziyao Liu, Jiale Guo, Wenzhuo Yang, Jiani Fan, Kwok-Yan Lam, and Jun Zhao, “Dynamic User Clustering for Efficient and Privacy-Preserving Federated Learning ,” IEEE Transactions on Dependable and Secure Computing (TDSC), 2024.

[7] Heng Dong, Xiaojie Fang, Jun Zhao, Xuejun Sha, and Zhuoming Li, “Signal Domain Multi-Component Based Secure Hybrid Precoding for mmWave Systems,” IEEE Transactions on Wireless Communications (TWC), 2024, Major Revision.

[8] Dewen Qiao, Mingyan Li, Songtao Guo, Jun Zhao, Bin Xiao, “Resources-Efficient Adaptive Federated Learning for Digital Twin-Enabled IIoT”. in IEEE Transactions on Network Science and Engineering (TNSE), 2024.

[9] Renyang Liu, Wei Zhao, Tianwei Zhang, Kangjie Chen, Jun Zhao, Kwok-Yan Lam, “Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models”. in IEEE Transactions on Information Forensics and Security (TIFS), 2024.

[10] Tinghao Zhang, Kwok-Yan Lam, and Jun Zhao, “Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things,” IEEE Internet of Things Journal (IoT-J), 2024.

[11] Mohamed R. Shoaib, Heba M. Emara, Jun Zhao, Walid El-Shafai, Naglaa F. Soliman, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel, “Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model,” Computers in Biology and Medicine, 2024.

[12] Mengmeng Yang, Taolin Guo, Tianqing Zhu, Ivan Tjuawinata, Jun Zhao, Kwok-Yan Lam, “Local Differential Privacy and its Applications: A Comprehensive Survey,” Computer Standards & Interfaces, 2024.

[13] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications,” IEEE Journal on Selected Areas in Communications (JSAC), 2023, Available: https://arxiv.org/abs/2212.14749.

[14] Xinyu Zhou, Chang Liu, and Jun Zhao, “Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality,” IEEE Transactions on Wireless Communications (TWC), 2023, Available: https://arxiv.org/pdf/2211.08705.

[15] Terence Jie Chua, Wenhan Yu, and Jun Zhao, “Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse: A Deep Reinforcement Learning Approach,” IEEE Transactions on Wireless Communications (TWC), 2023, Available: https://arxiv.org/abs/2303.10288.

[16] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks,” IEEE Transactions on Wireless Communications (TWC), 2023, Available: https://arxiv.org/abs/2302.01471.

[17] Tao Bai, Jun Zhao, and Bihan Wen, “Guided adversarial contrastive distillation for robust students,” IEEE Transactions on Information Forensics and Security (TIFS), 2023.

[18] Peiyuan Si, Liangxin Qian, Jun Zhao and Lam Kwok Yan, “A Novel Hybrid Framework with Reinforcement Learning and Convex Optimization for UAV-Assisted Autonomous Metaverse Data Collection,” IEEE Network, 2023, Available: https://arxiv.org/pdf/2305.18481.

[19] Huimei Han, Jun Zhao, and Xinyu Zhou, “A Random Access Scheme for Federated Learning over Massive MIMO Systems,” IEEE Internet of Things Journal (IoT-J), 2023, Available: https://ieeexplore.ieee.org/document/10130012.

[20] Jie Feng and Jun Zhao, “Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse,” IEEE Transactions on Communications (TCOM), 2023, Available: https://arxiv.org/abs/2212.01325.

[21] Hua-Qiang Xu, Shuai Gu, Yu-Cheng Fan, Xiao-Shuang Li, Yue-Feng Zhao, Jun Zhao, and Jing-Jing Wang, “A strategy learning framework for particle swarm optimization algorithm,” Information Sciences, vol. 619, pp. 126–152, 2023, Available: https://www.sciencedirect.com/science/article/abs/pii/S0020025522011896

[22] Huizi Xiao, Jun Zhao, Jie Feng, Lei Liu, Qingqi Pei, and Weisong Shi, “Joint Optimization of Security Strength and Resource Allocation for Computation Offloading in Vehicular Edge Computing,” IEEE Transactions on Wireless Communications (TWC), 2023, Available: https://personal.ntu.edu.sg/junzhao/TWC2023SecurityStrength.pdf

[23] Wanting Lyu, Yue Xiu, Jun Zhao, and Zhongpei Zhang, “Optimizing the Age of Information in RIS-aided SWIPT Networks,” IEEE Transactions on Vehicular Technology (TVT), 2023, Available: https://doi.org/10.1109/TVT.2022.3208612

[24] Tinghao Zhang, Kwok-Yan Lam, Jun Zhao, and Feng Jie, “Joint Device Scheduling and Bandwidth Allocation for Federated Learning over Wireless Networks,” IEEE Transactions on Wireless Communications (TWC), 2023, Available: https://doi.org/10.1109/TWC.2023.3291701

[25] Tinghao Zhang, Kwok-Yan Lam, Jun Zhao, Feng Li, Huimei Han, and Norziana Jamil, “Enhancing federated learning with spectrum allocation optimization and device selection,” IEEE/ACM Transactions on Networking (TON), 2023, Available: https://arxiv.org/abs/2212.13544

[26] Yuyan Zhou, Yang Liu, Qingqing Wu, Qingjiang Shi and Jun Zhao, “Queueing Aware Power Mnimization for Wireless Communication Aided by Double-Faced Active RIS,” IEEE Transactions on Communications (TCOM), 2023.

[27] Tinghao Zhang, Kwok Yan Lam, and Jun Zhao, “Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems,” Future Generation Computer Systems (FGCS), 2023, Available: https://dl.acm.org/doi/10.1016/j.future.2023.03.009.

[28] Guanjie Cheng, Junqin Huang, Yewei Wang, Jun Zhao, Linghe Kong, Shuiguang Deng, “Conditional Privacy-Preserving Multi-Domain Authentication and Pseudonym Management for 6G-Enabled IoV,” IEEE Transactions on Information Forensics and Security (TIFS), 2023, Available: https://doi.org/10.1109/TIFS.2023.3314211

2022

[1] Jun Zhao, “On secure communication in sensor networks under q-composite key predistribution with unreliable links,” IEEE Transactions on Communications (TCOM), vol. 70, no. 2, pp. 1085–1095, 2022, Available: https://arxiv.org/abs/1911.00718

[2] Tao Bai, Jinqi Luo, and Jun Zhao, “Inconspicuous adversarial patches for fooling image-recognition systems on mobile devices,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 12, pp. 9515–9524, 2022, Available: https://arxiv.org/abs/2106.15202

[3] Ziyao Liu, Jiale Guo, Wenzhuo Yang, Jiani Fan, Kwok-Yan Lam, and Jun Zhao, “Privacy-preserving aggregation in federated learning: A survey,” IEEE Transactions on Big Data (TBD), 2022, Available: https://arxiv.org/abs/2203.17005

[4] Ziyao Liu, Jiale Guo, Kwok-Yan Lam, and Jun Zhao, “Efficient dropout-resilient aggregation for privacy-preserving machine learning,” IEEE Transactions on Information Forensics and Security (TIFS), 2022, Available: https://arxiv.org/abs/2203.17044

[5] Mengmeng Yang, Ivan Tjuawinata, Kwok-Yan Lam, Tianqing Zhu, and Jun Zhao, “Differentially private distributed frequency estimation,” IEEE Transactions on Dependable and Secure Computing (TDSC), 2022, Available: https://arxiv.org/abs/2104.05974

[6] Yang Liu, Qingjiang Shi, Qingqing Wu, Jun Zhao, and Ming Li, “Joint node activation, beamforming and phase-shifting control in IoT sensor network assisted by reconfigurable intelligent surface,” IEEE Transactions on Wireless Communications (TWC), vol. 21, no. 11, pp. 9325–9340, 2022, Available: https://ieeexplore.ieee.org/document/9780895

[7] Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and S. Yu Philip, “Privacy and robustness in federated learning: Attacks and defenses,” IEEE transactions on neural networks and learning systems (TNNLS), 2022, Available: https://arxiv.org/abs/2012.06337

[8] Sun Mao, Lei Liu, Ning Zhang, Mianxiong Dong, Jun Zhao, Jinsong Wu, and Victor CM Leung, “Reconfigurable intelligent surface-assisted secure mobile edge computing networks,” IEEE Transactions on Vehicular Technology (TVT), vol. 71, no. 6, pp. 6647–6660, 2022, Available: https://ieeexplore.ieee.org/document/9741383

[9] Wanting Lyu, Yue Xiu, Jun Zhao, and Zhongpei Zhang, “Optimizing the age of information in RIS-aided SWIPT networks,” IEEE Transactions on Vehicular Technology (TVT), 2022, Available: https://arxiv.org/abs/2111.07318

[10] Helin Yang, Jun Zhao, Kwok-Yan Lam, Zehui Xiong, Qingqing Wu, and Liang Xiao, “Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks,” IEEE Transactions on Wireless Communications (TWC), vol. 21, no. 9, pp. 6935–6948, 2022, Available: https://ieeexplore.ieee.org/document/9725256

[11] Donghaisheng Liu, Shoudong Han, Yang Chen, Chenfei Xia, and Jun Zhao, “Foreground-guided textural-focused person re-identification,” Neurocomputing, vol. 483, pp. 235–248, 2022, Available: https://arxiv.org/abs/2009.11425

[12] Ziqing Yang, Shoudong Han, and Jun Zhao, “Poisson kernel: Avoiding self-smoothing in graph convolutional networks,” Pattern Recognition, vol. 124, p. 108443, 2022, doi: https://doi.org/10.1016/j.patcog.2021.108443.

[13] Jianhang Tang, Jiangtian Nie, Jun Zhao, Yi Zhou, Zehui Xiong, and Mohsen Guizani, “Slicing-based software-defined mobile edge computing in the air,” IEEE Wireless Communications, vol. 29, no. 1, pp. 119–125, 2022, Available: https://ieeexplore.ieee.org/document/9749187

[14] Xiaolun Jia, Xiangyun Zhou, Dusit Niyato, and Jun Zhao, “Intelligent reflecting surface-assisted bistatic backscatter networks: Joint beamforming and reflection design,” IEEE Transactions on Green Communications and Networking (TGCN), vol. 6, no. 2, pp. 799–814, 2022, Available: https://arxiv.org/abs/2010.08947

2021

[1] Huimei Han, Jun Zhao, Wenchao Zhai, Zehui Xiong, Dusit Niyato, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu, and Kwok-Yan Lam, “Reconfigurable intelligent surface aided power control for physical-layer broadcasting,” IEEE Transactions on Communications (TCOM), vol. 69, no. 11, pp. 7821–7836, 2021, Available: https://arxiv.org/abs/1912.03468

[2] Huimei Han, Lushun Fang, Weidang Lu, Kaikai Chi, Wenchao Zhai, and Jun Zhao, “A novel grant-based pilot access scheme for crowded massive MIMO systems,” IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 10, pp. 11111–11115, 2021, Available: https://ieeexplore.ieee.org/document/9535251

[3] Huimei Han, Lushun Fang, Weidang Lu, Wenchao Zhai, Ying Li, and Jun Zhao, “A gcica grant-free random access scheme for m2m communications in crowded massive mimo systems,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 8, pp. 6032–6046, 2021, Available: https://ieeexplore.ieee.org/document/9530567

[4] Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Sumei Sun, and Liang Xiao, “Privacy-preserving federated learning for UAV-enabled networks: Learning-based joint scheduling and resource management,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 39, no. 10, pp. 3144–3159, 2021, Available: https://arxiv.org/abs/2011.14197

[5] Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Chau Yuen, and Ruilong Deng, “Deep reinforcement learning based massive access management for ultra-reliable low-latency communications,” IEEE Transactions on Wireless Communications (TWC), vol. 20, no. 5, pp. 2977–2990, 2021, Available: https://ieeexplore.ieee.org/document/9311792

[6] Zehui Xiong, Jun Zhao, Yang Zhang, Dusit Niyato, and Junshan Zhang, “Contract design in hierarchical game for sponsored content service market,” IEEE Transactions on Mobile Computing (TMC), vol. 20, no. 9, pp. 2763–2778, 2021, Available: https://ieeexplore.ieee.org/document/9079925

[7] Yang Liu, Jun Zhao, Ming Li, and Qingqing Wu, “Intelligent reflecting surface aided MISO uplink communication network: Feasibility and power minimization for perfect and imperfect CSI,” IEEE Transactions on Communications (TCOM), vol. 69, no. 3, pp. 1975–1989, 2021, Available: https://ieeexplore.ieee.org/document/9270033

[8] Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, and Alex Kot, “Toward efficiently evaluating the robustness of deep neural networks in IoT systems: A GAN-based method,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 3, pp. 1875–1884, 2021, Available: https://arxiv.org/abs/2111.10055

[9] Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Yong Liang Guan, Chau Yuen, Sumei Sun, Kwok-Yan Lam, and Leong Hai Koh, “Blockchain for the internet of vehicles towards intelligent transportation systems: A survey,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 6, pp. 4157–4185, 2021, Available: https://ieeexplore.ieee.org/document/9211724

[10] Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Kwok-Yan Lam, Xin Zhang, Amer MYM Ghias, Leong Hai Koh, and Lei Yang, “Blockchain for future smart grid: A comprehensive survey,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 1, pp. 18–43, 2021, Available: https://ieeexplore.ieee.org/document/9090812

[11] Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, and Kwok-Yan Lam, “Local differential privacy-based federated learning for internet of things,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 11, pp. 8836–8853, 2021, Available: https://arxiv.org/abs/2004.08856

[12] Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu, “Privacy-preserving blockchain-based federated learning for IoT devices,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 3, pp. 1817–1829, 2021, Available: https://ieeexplore.ieee.org/document/9170559

[13] Yang Zhao, Jun Zhao, Jiawen Kang, Zehang Zhang, Dusit Niyato, Shuyu Shi, and Kwok-Yan Lam, “A blockchain-based approach for saving and tracking differential-privacy cost,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 11, pp. 8865–8882, 2021, Available: https://ieeexplore.ieee.org/document/9351532

[14] Yue Xiu, Jun Zhao, Wei Sun, Marco Di Renzo, Guan Gui, Zhongpei Zhang, and Ning Wei, “Reconfigurable intelligent surfaces aided <span class="nocase">mmWave NOMA: Joint power allocation, phase shifts, and hybrid beamforming optimization,” IEEE Transactions on Wireless Communications (TWC), vol. 20, no. 12, pp. 8393–8409, 2021, Available: https://ieeexplore.ieee.org/document/9472958

[15] Ge Song, Xiaoyang Tan, Jun Zhao, and Ming Yang, “Deep robust multilevel semantic hashing for multi-label cross-modal retrieval,” Pattern Recognition, vol. 120, p. 108084, 2021, Available: https://www.sciencedirect.com/science/article/abs/pii/S0031320321002715

[16] Cong Wang, Witold Pedrycz, ZhiWu Li, MengChu Zhou, and Jun Zhao, “Residual-sparse fuzzy c-means clustering incorporating morphological reconstruction and wavelet frame,” IEEE Transactions on Fuzzy Systems (TFS), vol. 29, no. 12, pp. 3910–3924, 2021, Available: https://ieeexplore.ieee.org/abstract/document/9216162

[17] Wei Sun, Qingyang Song, Jun Zhao, Lei Guo, and Abbas Jamalipour, “Adaptive resource allocation in SWIPT-enabled cognitive IoT networks,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 1, pp. 535–545, 2021, Available: https://ieeexplore.ieee.org/document/9442810

[18] Teng Wang, Jun Zhao, Zhi Hu, Xinyu Yang, Xuebin Ren, and Kwok-Yan Lam, “Local differential privacy for data collection and analysis,” Neurocomputing, vol. 426, pp. 114–133, 2021, Available: https://www.sciencedirect.com/science/article/abs/pii/S0925231220316064

[19] Chaoyu Dong, Xiangke Li, Wentao Jiang, Yunfei Mu, Jun Zhao, and Hongjie Jia, “Cyber-physical modelling operator and multimodal vibration in the integrated local vehicle-grid electrical system,” Applied Energy, vol. 286, p. 116432, 2021, Available: https://www.sciencedirect.com/science/article/abs/pii/S0306261921000015

[20] Huizi Xiao, Jun Zhao, Qingqi Pei, Jie Feng, Lei Liu, and Weisong Shi, “Vehicle selection and resource optimization for federated learning in vehicular edge computing,” IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 23, no. 8, pp. 11073–11087, 2021.

[21] Jianhang Tang, Jiangtian Nie, Zehui Xiong, Jun Zhao, Yang Zhang, and Dusit Niyato, “Slicing-based reliable resource orchestration for secure software-defined edge-cloud computing systems,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 4, pp. 2637–2648, 2021, Available: https://ieeexplore.ieee.org/document/9521992

[22] Weiheng Jiang, Yu Zhang, Jun Zhao, Zehui Xiong, and Zhiguo Ding, “Joint transmit precoding and reflect beamforming design for IRS-assisted MIMO cognitive radio systems,” IEEE Transactions on Wireless Communications (TWC), vol. 21, no. 6, pp. 3617–3631, 2021, Available: https://ieeexplore.ieee.org/document/9599553

[23] Weiheng Jiang, Bolin Chen, Jun Zhao, Zehui Xiong, and Zhiguo Ding, “Joint active and passive beamforming design for the IRS-assisted MIMOME-OFDM secure communications,” IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 10, pp. 10369–10381, 2021, Available: https://ieeexplore.ieee.org/document/9520295

[24] Thippa Reddy Gadekallu, Quoc-Viet Pham, Dinh C. Nguyen, Praveen Kumar Reddy Maddikunta, Natarajan Deepa, B. Prabadevi, Pubudu N. Pathirana, Jun Zhao, and Won-Joo Hwang, “Blockchain for edge of things: Applications, opportunities, and challenges,” IEEE Internet of Things Journal (IoT-J), vol. 9, no. 2, pp. 964–988, 2021, Available: https://arxiv.org/abs/2110.05022

[25] Yulan Gao, Chao Yong, Zehui Xiong, Jun Zhao, Yue Xiao, and Dusit Niyato, “Reflection resource management for intelligent reflecting surface aided wireless networks,” IEEE Transactions on Communications (TCOM), vol. 69, no. 10, pp. 6971–6986, 2021, Available: https://ieeexplore.ieee.org/document/9467371

[26] Meng Hua, Qingqing Wu, Derrick Wing Kwan Ng, Jun Zhao, and Luxi Yang, “Intelligent reflecting surface-aided joint processing coordinated multipoint transmission,” IEEE Transactions on Communications (TCOM), vol. 69, no. 3, pp. 1650–1665, 2021, Available: https://arxiv.org/abs/2003.13909

[27] Deyou Zhang, Jun Zhao, Ang Li, Jun Li, Branka Vucetic, and Yonghui Li, “Mobile user trajectory tracking for IRS enabled wireless networks,” IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 8, pp. 8331–8336, 2021, Available: https://ieeexplore.ieee.org/document/9479771

[28] Hangyu Tian, Kaiping Xue, Xinyi Luo, Shaohua Li, Jie Xu, Jianqing Liu, Jun Zhao, and David SL Wei, “Enabling cross-chain transactions: A decentralized cryptocurrency exchange protocol,” IEEE Transactions on Information Forensics and Security (TIFS), vol. 16, pp. 3928–3941, 2021, Available: https://ieeexplore.ieee.org/document/9478888

[29] Ali Hussain Khan, Naveed UL Hassan, Chau Yuen, Jun Zhao, Dusit Niyato, Yan Zhang, and H. Vincent Poor, “Blockchain and 6G: The future of secure and ubiquitous communication,” IEEE Wireless Communications, vol. 29, no. 1, pp. 194–201, 2021, Available: https://ieeexplore.ieee.org/document/9508931

[30] Xuebin Ren, Chia-Mu Yu, Wei Yu, Xinyu Yang, Jun Zhao, and Shusen Yang, “Dpcrowd: Privacy-preserving and communication-efficient decentralized statistical estimation for real-time crowdsourced data,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 4, pp. 2775–2791, 2021, Available: https://arxiv.org/abs/2009.14125

[31] Yue Xiu, Jun Zhao, Ertugrul Basar, Marco Di Renzo, Wei Sun, Guan Gui, and Ning Wei, “Uplink achievable rate maximization for reconfigurable intelligent surface aided millimeter wave systems with resolution-adaptive ADCs,” IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1608–1612, 2021, Available: https://ieeexplore.ieee.org/document/9390351

[32] Ergute Bao, Xiaokui Xiao, Jun Zhao, Dongping Zhang, and Bolin Ding, “Synthetic data generation with differential privacy via bayesian networks,” Journal of Privacy and Confidentiality, vol. 11, no. 3, 2021, Available: https://journalprivacyconfidentiality.org/index.php/jpc/article/view/776

[33] Huimei Han, Wenchao Zhai, Ying Li, Weidang Lu, and Jun Zhao, “A novel random access scheme for M2M communication in crowded asynchronous massive MIMO systems,” IET Communications, vol. 15, no. 12, pp. 1597–1605, 2021, Available: https://arxiv.org/abs/2007.06370

[34] Yue Xiu, Jun Zhao, Wei Sun, and Zhongpei Zhang, “Secrecy rate maximization for reconfigurable intelligent surface aided millimeter wave system with low-resolution DACs,” IEEE Communications Letters, vol. 25, no. 7, pp. 2166–2170, 2021, Available: https://ieeexplore.ieee.org/document/9348933

2020

[1] Helin Yang, Arokiaswami Alphones, Zehui Xiong, Dusit Niyato, Jun Zhao, and Kaishun Wu, “Artificial-intelligence-enabled intelligent 6G networks,” IEEE Network, vol. 34, no. 6, pp. 272–280, 2020, Available: https://ieeexplore.ieee.org/document/9237460

[2] Xi Xiong, Fei Xiong, Jun Zhao, Shaojie Qiao, Yuanyuan Li, and Ying Zhao, “Dynamic discovery of favorite locations in spatio-temporal social networks,” Information Processing & Management, vol. 57, no. 6, p. 102337, 2020, Available: https://www.sciencedirect.com/science/article/abs/pii/S0306457320308323

[3] Mengmeng Yang, Ivan Tjuawinata, Kwok Yan Lam, Jun Zhao, and Lin Sun, “Secure hot path crowdsourcing with local differential privacy under fog computing architecture,” IEEE Transactions on Services Computing (TSC), vol. 15, no. 4, pp. 2188–2201, 2020, Available: https://arxiv.org/abs/2012.13807

[4] Yue Xiu, Jun Zhao, Chau Yuen, Zhongpei Zhang, and Guan Gui, “Secure beamforming for multiple intelligent reflecting surfaces aided <span class="nocase">mmWave systems,” IEEE Communications Letters, vol. 25, no. 2, pp. 417–421, 2020, Available: https://ieeexplore.ieee.org/document/9210742

[5] Quoc-Viet Pham, Thien Huynh-The, Mamoun Alazab, Jun Zhao, and Won-Joo Hwang, “Sum-rate maximization for UAV-assisted visible light communications using NOMA: Swarm intelligence meets machine learning,” IEEE Internet of Things Journal (IoT-J), vol. 7, no. 10, pp. 10375–10387, 2020, Available: https://ieeexplore.ieee.org/document/9075277

[6] Kentaroh Toyoda, Jun Zhao, Allan Neng Sheng Zhang, and P. Takis Mathiopoulos, “Blockchain-enabled federated learning with mechanism design,” IEEE Access, vol. 8, pp. 219744–219756, 2020, Available: https://ieeexplore.ieee.org/document/9285269

[7] Zehui Xiong, Jun Zhao, Dusit Niyato, Ruilong Deng, and Junshan Zhang, “Reward optimization for content providers with mobile data subsidization: A hierarchical game approach,” IEEE Transactions on Network Science and Engineering (TNSE), vol. 7, no. 4, pp. 2363–2377, 2020, Available: https://ieeexplore.ieee.org/document/9169846

2019

[1] Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao, “Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory,” IEEE Transactions on Vehicular Technology (TVT), vol. 68, no. 3, pp. 2906–2920, 2019, Available: https://ieeexplore.ieee.org/document/8624307

[2] Binbin Huang, Zhongjin Li, Peng Tang, Shangguang Wang, Jun Zhao, Haiyang Hu, Wanqing Li, and Victor Chang, “Security modeling and efficient computation offloading for service workflow in mobile edge computing,” Future Generation Computer Systems (FGCS), vol. 97, pp. 755–774, 2019, Available: https://www.sciencedirect.com/science/article/abs/pii/S0167739X18326773

2018

[1] Jun Zhao, “Analyzing connectivity of heterogeneous secure sensor networks,” IEEE Transactions on Control of Network Systems (TCNS), vol. 5, no. 1, pp. 618–628, 2018, Available: https://arxiv.org/abs/1911.01890

[2] Jun Zhao, “Transitional behavior of q-composite random key graphs with applications to networked control,” IEEE Transactions on Control of Network Systems (TCNS), vol. 5, no. 4, pp. 1741–1751, 2018, Available: https://arxiv.org/abs/1708.08313

[3] Songjun Ma, Ge Chen, Luoyi Fu, Weijie Wu, Xiaohua Tian, Jun Zhao, and Xinbing Wang, “Seeking powerful information initial spreaders in online social networks: A dense group perspective,” Wireless Networks, vol. 24, pp. 2973–2991, 2018, Available: https://link.springer.com/article/10.1007/s11276-017-1478-1

2017 & previous

[1] Jun Zhao, “Topological properties of secure wireless sensor networks under the q-composite key predistribution scheme with unreliable links,” IEEE/ACM Transactions on Networking (TON), vol. 25, no. 3, pp. 1789–1802, 2017, Available: https://ieeexplore.ieee.org/document/7859356

[2] Jun Zhao, “Probabilistic key predistribution in mobile networks resilient to node-capture attacks,” IEEE Transactions on Information Theory (TIT), vol. 63, no. 10, pp. 6714–6734, 2017, Available: https://ieeexplore.ieee.org/document/7962239

[3] Jun Zhao, Osman Yağan, and Virgil Gligor, “On connectivity and robustness in random intersection graphs,” IEEE Transactions on Automatic Control (TAC), vol. 62, no. 5, pp. 2121–2136, 2017, Available: https://arxiv.org/abs/1911.01822

[4] Jun Zhao, “On resilience and connectivity of secure wireless sensor networks under node capture attacks,” IEEE Transactions on Information Forensics and Security (TIFS), vol. 12, no. 3, pp. 557–571, 2017, Available: https://arxiv.org/abs/1911.00725

[5] Faruk Yavuz, Jun Zhao, Osman Yağan, and Virgil Gligor, “k-connectivity in random k-out graphs intersecting erd\textbackslashHos-rényi graphs,” IEEE Transactions on Information Theory (TIT), vol. 63, no. 3, pp. 1677–1692, 2017, Available: https://users.ece.cmu.edu/~oyagan/Journals/ICC15Long.pdf

[6] Xiaoying Liu, Kechen Zheng, Jun Zhao, Xiao-Yang Liu, Xinbing Wang, and Xin Di, “Information-centric networks with correlated mobility,” IEEE Transactions on Vehicular Technology (TVT), vol. 66, no. 5, pp. 4256–4270, 2017, Available: https://ieeexplore.ieee.org/document/7551158

[7] Jun Zhao, “Analyzing resilience of interest-based social networks against node and link failures,” IEEE Transactions on Signal and Information Processing over Networks (TSIPN), vol. 3, no. 2, pp. 252–267, 2017, Available: https://arxiv.org/abs/1911.11068

[8] Jun Zhao and Junshan Zhang, “Preserving privacy enables ‘coexistence equilibrium’ of competitive diffusion in social networks,” IEEE Transactions on Signal and Information Processing over Networks (TSIPN), vol. 3, no. 2, pp. 282–297, 2017, Available: https://ieeexplore.ieee.org/document/7911323

[9] Jun Zhao, Osman Yağan, and Virgil Gligor, “k-connectivity in random key graphs with unreliable links,” IEEE Transactions on Information Theory (TIT), vol. 61, no. 7, pp. 3810–3836, 2015, Available: https://arxiv.org/abs/1206.1531

[10] Faruk Yavuz, Jun Zhao, Osman Yağan, and Virgil Gligor, “Toward k-connectivity of the random graph induced by a pairwise key predistribution scheme with unreliable links,” IEEE Transactions on Information Theory (TIT), vol. 61, no. 11, pp. 6251–6271, 2015, Available: https://arxiv.org/abs/1405.5193

Journal

Conference

2024

[1] Wenhan Yu, Liangxin Qian, Terence Chua, and Jun Zhao, “Counterfactual Reward Estimation for Credit Assignment in Multi-agent Deep Reinforcement Learning over Wireless Video Transmission,” in IEEE ICDCS, 2024.

[2] Chang Liu, Terence Jie Chua, and Jun Zhao, “Optimization for the Metaverse over Mobile Edge Computing with Play to Earn,” in IEEE INFOCOM, 2024.

[3] Renyang Liu, Wei Zhou, Sixin Wu, Jun Zhao, and Kwok-Yan Lam, “SSTA: Salient Spatially Transformed Attack”. in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.

[4] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach”. in IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 2024.

[5] Liangxin Qian, and Jun Zhao, “User Association and Resource Allocation in Large Language Model Based Mobile Edge Computing System over Wireless Communications”. in IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 2024.

[6] Mohamed R. Shoaib, Zefan Wang, and Jun Zhao, “The Convergence of Artificial Intelligence (AI) Foundation Models and 6G Wireless Communication Networks”. in IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 2024.

[7] Yitong Wang, Chang Liu, and Jun Zhao, “Offloading and Quality Control for AI Generated Content Services in Edge Computing Networks”. in IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 2024.

[8] Chang Liu, and Jun Zhao, “Resource Allocation in Large Language Model-Integrated Vehicular Networks”. in IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 2024.

2023

[1] Jun Zhao, Xinyu Zhou, Yang Li, and Liangxin Qian, “Optimizing Utility-Energy Efficiency for the Metaverse over Wireless Networks under Physical Layer Security,” in ACM MobiHoc, 2023. Available: https://doi.org/10.1145/3565287.3610271

[2] Xinyu Zhou, Yang Li, and Jun Zhao, “Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse,” in 2023 IEEE International Conference on Communications (ICC), 2023. Available: https://arxiv.org/abs/2211.08705

[3] Mohamed R. Shoaib, Zefan Wang, Milad Taleby Ahvanooey, and Jun Zhao, “Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models”. International Conference on Computer and Applications (ICCA), 2023. Available: https://arxiv.org/abs/2311.17394

[4] Renyang Liu, Jinhong Zhang, Kwok-Yan Lam, Jun Zhao, Wei Zhou, “SCME: A Self-contrastive Method for Data-Free and Query-Limited Model Extraction Attack”. in International Conference on Neural Information Processing (ICONIP) , 2023.

[5] Wenhan Yu and Jun Zhao. “Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions”. in International Conference on Computer and Applications (ICCA), 2023. Available: https://www.techrxiv.org/doi/full/10.36227/techrxiv.24563293.v1

[6] Mohamed R. Shoaib, Heba M. Emara, and Jun Zhao. “A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems”. in International Conference on Computer and Applications (ICCA), 2023.

[7] Yang Li, Xinyu Zhou, and Jun Zhao, “Resource Allocation for Semantic Communication under Physical-layer Security,” in IEEE Global Communications Conference (GlobeCom), 2023.

[8] Peiyuan Si, Jun Zhao, Kwok-Yan Lam, and Qing Yang, “UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning,” in IEEE Global Communications Conference (GlobeCom), 2023.

[9] Haonan Tong, Sihua Wang, Zhaohui Yang, Jun Zhao, Mehdi Bennis, and Changchuan Yin, “Semantic-aware Remote State Estimation in Digital Twin with Minimizing Age of Incorrect Information,” in IEEE Global Communications Conference (GlobeCom), 2023.

[10] Tianming Lan and Jun Zhao, “Optimization in Mobile Augmented Reality Systems for the Metaverse over Wireless Communications,” in IEEE Global Communications Conference (GlobeCom), 2023.

[11] Wenhan Yu and Jun Zhao, “Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches,” in IEEE Global Communications Conference (GlobeCom), 2023.

[12] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning,” in 2023 IEEE International Conference on Communications (ICC), 2023.

[13] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “Semantic communications, semantic edge computing, and semantic caching with applications to the Metaverse and 6G mobile networks,” in ICDCS 2023 PhD symposium, 2023.

[14] Wenhan Yu, Terence Jie Chua, and Jun Zhao, “Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach,” in IEEE Vehicular Technology Conference (VTC), 2023.

[15] Terence Jie Chua, Wenhan YU, and Jun Zhao, “Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning,” in 2023 IEEE International Conference on Communications (ICC), 2023.

[16] Yidong Lu and Jun Zhao, “Reconfigurable Intelligent Surface aided Wireless Powered Mobile Edge Computing,” in IEEE 20th Consumer Communications & Networking Conference (CCNC), 2023. Available: https://ieeexplore.ieee.org/document/9741383

[17] Tao Bai, Chen Chen, Lingjuan Lyu, Jun Zhao, and Bihan Wen, “Towards Adversarially Robust Continual Learning,” in International Conference on Acoustics, Speech (ICASSP), 2023.

[18] Zefan Wang and Jun Zhao, “Utility-Oriented Wireless Communications for 6G Networks: Semantic Information Transfer for IRS aided Vehicular Metaverse,” in IEEE Vehicular Technology Conference (VTC), 2023.

[19] Mohamed R. Shoaib, Mohamed Taher, Taher Abdelhameed, Dahab Tarek, and Jun Zhao, “Unmasking Fraud in Food Delivery Business: Harnessing Amazon Web Services' Random Cut Forest for Efficient Detection,” in IEEE 3rd International Conference on Electrical, Computer, Communications, and Mechatronics Engineering (ICECCME), 2023.

[20] Yuyan Zhou, Yang Liu, Qingqing Wu, and Jun Zhao, “Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS,” in IEEE International Conference on Communications (ICC), 2023.

[21] Zimo Ma and Jun Zhao, “When Generative AI Meets Mobile Edge Computing Enabled Artificial Intelligence of Things (AIoT): Resource Management for AI Generative Content (AIGC) Services,” in 19th International Conference on Mobility, Sensing and Networking (MSN), 2023.

2022

[1] Xinyu Zhou, Jun Zhao, Huimei Han, and Claude Guet, “Joint optimization of energy consumption and completion time in federated learning,” in 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS), 2022, pp. 1005–1017. Available: https://arxiv.org/abs/2209.14900

[2] Wenchao Zhai, Shuailei Zhu, Huimei Han, and Jun Zhao, “Reconfigurable Intelligent Surfaces Aided Uplink NOMA for Digital Federated Learning,” in IEEE the 8th International Conference on Computer and Communications (ICCC), 2022.

[3] Terence Jie Chua, Wenhan Yu, and Jun Zhao, “Resource allocation for mobile metaverse with the Internet of Vehicles over 6G wireless communications: A deep reinforcement learning approach,” in IEEE World Forum on the Internet of Things (WF-IoT), 2022. Available: https://arxiv.org/abs/2209.13425

[4] Yitong Wang and Jun Zhao, “A Survey of Mobile Edge Computing for the Metaverse: Architectures, Applications, and Challenges,” in 8th IEEE International Conference on Collaboration and Internet Computing (CIC), 2022. Available: https://arxiv.org/abs/2212.00481

[5] Aichen Li, Yang Liu, Qingqing Wu, Qingjiang Shi, and Jun Zhao, “Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network,” in 2022 IEEE Globecom Workshops (GC Wkshps), 2022, pp. 1646–1651. Available: https://ieeexplore.ieee.org/document/10008642

[6] Chang Liu, Terence Jie Chua, and Jun Zhao, “Time Minimization in Hierarchical Federated Learning,” in 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), 2022, pp. 96–106. Available: https://arxiv.org/abs/2210.04689

[7] Qiao Han, Jun Zhao, and Kwok-Yan Lam, “Facial Landmark Predictions with Applications to Metaverse,” in IEEE World Forum on Internet of Things (WF-IoT), 2022. Available: https://arxiv.org/abs/2209.14698

[8] Terence Jie Chua, Wenhan YU, Chang Liu, and Jun Zhao, “Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer,” in IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) (Best Paper Award), 2022.

[9] Peiyuan Si, Jun Zhao, Huimei Han, Kwok-Yan Lam, and Yang Liu, “Resource Allocation and Resolution Control in the Metaverse with Mobile Augmented Reality,” in GLOBECOM 2022-2022 IEEE Global Communications Conference, 2022, pp. 3265–3271. Available: https://arxiv.org/abs/2209.13871

[10] Yitong Wang and Jun Zhao, “Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence,” in IEEE World Forum on Internet of Things (WF-IoT), 2022. Available: https://arxiv.org/abs/2209.14147

[11] Tinghao Zhang, Zhijun Li, Yongrui Chen, Kwok-Yan Lam, and Jun Zhao, “Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning,” in 2022 IEEE International Conferences on Internet of Things (<span class="nocase">iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2022, pp. 77–84. Available: https://arxiv.org/abs/2210.05182

2021

[1] Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang, “Recent advances in adversarial training for adversarial robustness,” in The 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021, pp. 4312–4321. Available: https://arxiv.org/abs/2102.01356

[2] Helin Yang, Kwok-Yan Lam, Jiangtian Nie, Jun Zhao, Sahil Garg, Liang Xiao, Zehui Xiong, and Mohsen Guizani, “3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks,” in 2021 IEEE Globecom Workshops (GC Wkshps), 2021, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9681960

[3] Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, and Alex Kot, “Ai-gan: Attack-inspired generation of adversarial examples,” in 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2543–2547. Available: https://ieeexplore.ieee.org/abstract/document/9506278

[4] Xiaoyu Wang, Tao Bai, and Jun Zhao, “A Data-Free Approach for Targeted Universal Adversarial Perturbation,” in Science of Cyber Security: Third International Conference, SciSec 2021, Virtual Event, August 13–15, 2021, Revised Selected Papers 4, 2021, pp. 126–138. Available: https://link.springer.com/chapter/10.1007/978-3-030-89137-4_9

[5] Helin Yang, Jun Zhao, Kwok-Yan Lam, Sahil Garg, Qingqing Wu, and Zehui Xiong, “Deep reinforcement learning based resource allocation for heterogeneous networks,” in 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2021, pp. 253–258. Available: [Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks](https://Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks)

[6] Yanze Zhu, Yang Liu, Jun Zhao, Ming Li, and Qingqing Wu, “Joint time allocation and beamforming design for IRS-aided coexistent cellular and sensor networks,” in 2021 IEEE global communications conference (GLOBECOM), 2021, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9685970

[7] Weiheng Jiang, Bolin Chen, Sahil Garg, Jiangtian Nie, Jun Zhao, and Zehui Xiong, “Joint transmit precoding and reflect beamforming for IRS-assisted MIMO-OFDM secure communications,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9685734

[8] Jiale Guo, Ziyao Liu, Kwok-Yan Lam, Jun Zhao, and Yiqiang Chen, “Privacy-enhanced federated learning with weighted aggregation,” in Security and Privacy in Social Networks and Big Data: 7th International Symposium, SocialSec 2021, Fuzhou, China, November 19–21, 2021, Proceedings 7, 2021, pp. 93–109. Available: https://link.springer.com/chapter/10.1007/978-981-16-7913-1_7

[9] Yang Zhao, Jun Zhao, Wenchao Zhai, Sumei Sun, Dusit Niyato, and Kwok-Yan Lam, “A survey of 6G wireless communications: Emerging technologies,” in Advances in Information and Communication: Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 1, 2021, pp. 150–170. Available: https://link.springer.com/chapter/10.1007/978-3-030-73100-7_12

[10] Jinqi Luo, Tao Bai, and Jun Zhao, “Generating adversarial yet inconspicuous patches with a single image (student abstract),” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, pp. 15837–15838. Available: https://arxiv.org/abs/2009.09774

2020

[1] Jun Zhao, Jing Tang, Zengxiang Li, Huaxiong Wang, Kwok-Yan Lam, and Kaiping Xue, “An analysis of blockchain consistency in asynchronous networks: Deriving a neat bound,” in 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), 2020, pp. 179–189. Available: https://arxiv.org/abs/1909.06587

[2] Zhiying Xu, Shuyu Shi, Alex X. Liu, Jun Zhao, and Lin Chen, “An adaptive and fast convergent approach to differentially private deep learning,” in IEEE Conference on Computer Communications (INFOCOM), 2020, pp. 1867–1876. Available: https://arxiv.org/abs/1912.09150

[3] Zehui Xiong, Jun Zhao, Jiawen Kang, Dusit Niyato, Ruilong Deng, and Shengli Xie, “Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization,” in 6GN for Future Wireless Networks: Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings 3, 2020, pp. 173–189. Available: https://link.springer.com/chapter/10.1007/978-3-030-63941-9_13

[4] Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, and Stefano Secci, “Intelligent reflecting surface assisted anti-jamming communications based on reinforcement learning,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 2020, pp. 1–6. Available: https://arxiv.org/abs/2012.12761

[5] Wei Sun, Qingyang Song, Lei Guo, and Jun Zhao, “Secrecy rate maximization for intelligent reflecting surface aided SWIPT systems,” in 2020 IEEE/CIC International Conference on Communications in China (ICCC), 2020, pp. 1276–1281. Available: https://ieeexplore.ieee.org/document/9238963

[6] Huaqiang Xu, Guodong Zhang, Jun Zhao, and Quoc-Viet Pham, “Intelligent reflecting surface aided wireless networks: Harris hawks optimization for beamforming design,” in 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 200–205. Available: https://arxiv.org/abs/2010.01900

[7] Hans Albert Lianto, Yang Zhao, and Jun Zhao, “Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients,” in ACM Asia Conference on Computer and Communications Security, 2020, pp. 901–903. Available: https://arxiv.org/abs/2006.04695

[8] Leong Mei Han, Yang Zhao, and Jun Zhao, “Blockchain-Based Differential Privacy Cost Management System,” in ACM Asia Conference on Computer and Communications Security, 2020. Available: https://arxiv.org/abs/2006.04693

[9] Huimei Han, Jun Zhao, Dusit Niyato, Marco Di Renzo, and Quoc-Viet Pham, “Intelligent reflecting surface aided network: Power control for physical-layer broadcasting,” in ICC 2020-2020 IEEE International Conference on Communications (ICC), 2020, pp. 1–7. Available: https://ieeexplore.ieee.org/document/9148827

[10] Lin Sun, Xiaojun Ye, Jun Zhao, Chenhui Lu, and Mengmeng Yang, “Bisample: Bidirectional sampling for handling missing data with local differential privacy,” in Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part I 25, 2020, pp. 88–104. Available: https://arxiv.org/abs/2002.05624

[11] Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, and Jun Zhao, “A Stackelberg Game Approach to Resource Allocation for IRS-aided Communications,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 2020, pp. 1–6. Available: https://ieeexplore.ieee.org/abstract/document/9322649

[12] Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, and Jun Zhao, “Resource allocation for intelligent reflecting surface aided cooperative communications,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 2020, pp. 1–6. Available: https://arxiv.org/abs/2012.10229

[13] Xiaolun Jia, Jun Zhao, Xiangyun Zhou, and Dusit Niyato, “Intelligent reflecting surface-aided backscatter communications,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 2020, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9348003

[14] Lingjuan Lyu, Yee Wei Law, Kee Siong Ng, Shibei Xue, Jun Zhao, Mengmeng Yang, and Lei Liu, “Towards distributed privacy-preserving prediction,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, pp. 4179–4184. Available: https://ieeexplore.ieee.org/document/9283102

[15] Yunchuan Liu, Amir Ghasemkhani, Lei Yang, Jun Zhao, Junshan Zhang, and Vijay Vittal, “Seasonal self-evolving neural networks based short-term wind farm generation forecast,” in 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2020, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9303002

[16] Huy T. Nguyen, Nguyen Cong Luong, Jun Zhao, Chau Yuen, and Dusit Niyato, “Resource allocation in mobility-aware federated learning networks: A deep reinforcement learning approach,” in IEEE 6th World Forum on Internet of Things (WF-IoT), 2020. Available: https://arxiv.org/abs/1910.09172

2019

[1] Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, and Ge Yu, “Collecting and analyzing multidimensional data with local differential privacy,” in 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019, pp. 638–649. Available: https://arxiv.org/abs/1907.00782

[2] Shuyu Shi, Yaxiong Xie, Mo Li, Alex X. Liu, and Jun Zhao, “Synthesizing wider WiFi bandwidth for respiration rate monitoring in dynamic environments,” in IEEE Conference on Computer Communications (INFOCOM), 2019, pp. 181–189. Available: https://ieeexplore.ieee.org/document/8737553

[3] Renchi Yang, Xiaokui Xiao, Zhewei Wei, Sourav S. Bhowmick, Jun Zhao, and Rong-Hua Li, “Efficient Estimation of Heat Kernel PageRank for Local Clustering,” in Proceedings of the 2019 International Conference on Management of Data, 2019, pp. 1339–1356. doi: 10.1145/3299869.3319886.

[4] Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, and Shuyu Shi, “Privacy-preserving crowd-guided AI decision-making in ethical dilemmas,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019, pp. 1311–1320. Available: https://arxiv.org/abs/1906.01562

[5] Teng Wang, Xinyu Yang, Xuebin Ren, Jun Zhao, and Kwok-Yan Lam, “Adaptive differentially private data stream publishing in spatio-temporal monitoring of IoT,” in 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), 2019, pp. 1–8. Available: https://ieeexplore.ieee.org/document/8958751

[6] Zehui Xiong, Jun Zhao, Dusit Niyato, Ping Wang, and Yang Zhang, “Design of contract-based sponsorship scheme in stackelberg game for sponsored content market,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1–6. Available: https://ieeexplore.ieee.org/document/9013234

[7] Yutao Jiao, Ping Wang, Dusit Niyato, Jun Zhao, Bin Lin, and Dong In Kim, “Task Allocation and Mobile Base Station Deployment in Wireless Powered Spatial Crowdsourcing,” in 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2019, pp. 1–7. Available: https://ieeexplore.ieee.org/document/8909703

2018

[1] Jun Zhao, “Analyzing the robustness of deep learning against adversarial examples,” in 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2018, pp. 1060–1064. Available: https://ieeexplore.ieee.org/document/8636048

[2] Wubing Chen, Zhiying Xu, Shuyu Shi, Yang Zhao, and Jun Zhao, “A survey of blockchain applications in different domains,” in Proceedings of the 2018 International Conference on Blockchain Technology and Application, 2018, pp. 17–21. Available: https://arxiv.org/abs/1911.02013

2017

[1] Jun Zhao, “Secure connectivity of wireless sensor networks under key predistribution with on/off channels,” in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, pp. 889–899. Available: https://arxiv.org/abs/1911.00745

[2] Jun Zhao, “Modeling interest-based social networks: Superimposing Erdos-Renyi graphs over random intersection graphs,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 3704–3708. Available: https://dl.acm.org/doi/abs/10.1109/ICASSP.2017.7952848

[3] Jun Zhao, “Relations among different privacy notions,” in 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017, pp. 798–805. Available: https://arxiv.org/pdf/1911.00761.pdf

[4] Jun Zhao, “Composition properties of bayesian differential privacy,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1–5. Available: https://arxiv.org/abs/1911.00763

[5] Jun Zhao, “Designing secure networks with q-composite key predistribution under different link constraints,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2077–2081. Available: https://ieeexplore.ieee.org/document/7952522

2016 & previous

[1] Jun Zhao, “A comprehensive guideline for choosing parameters in the Eschenauer-Gligor key predistribution,” in 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016, pp. 1267–1273. Available: https://ieeexplore.ieee.org/document/7852380

[2] Faruk Yavuz, Jun Zhao, Osman Yağan, and Virgil Gligor, “Designing secure and reliable wireless sensor networks under a pairwise key predistribution scheme,” in 2015 IEEE International Conference on Communications (ICC), 2015, pp. 6277–6283. Available: https://ieeexplore.ieee.org/document/7249324

[3] Jun Zhao, “On the resilience to node capture attacks of secure wireless sensor networks,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 887–893. Available: https://ieeexplore.ieee.org/document/7447100

[4] Jun Zhao, Osman Yağan, and Virgil Gligor, “On k-connectivity and minimum vertex degree in random s-intersection graphs,” in 2015 Proceedings of the Twelfth Workshop on Analytic Algorithmics and Combinatorics (ANALCO), 2014, pp. 1–15. Available: https://arxiv.org/abs/1409.6021

[5] Jun Zhao, “Sharp transitions in random key graphs,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 1182–1188. Available: https://ieeexplore.ieee.org/document/7447142

[6] Jun Zhao, “Threshold functions in random s-intersection graphs,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 1358–1365. Available: https://arxiv.org/abs/1502.00395

[7] Jun Zhao, “The absence of isolated node in geometric random graphs,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 881–886. Available: https://ieeexplore.ieee.org/document/7447099

[8] Jun Zhao, “Critical behavior in heterogeneous random key graphs,” in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 868–872. Available: https://ieeexplore.ieee.org/document/7418321

[9] Jun Zhao, “Parameter control in predistribution schemes of cryptographic keys,” in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 863–867. Available: https://ieeexplore.ieee.org/document/7418320

[10] Jun Zhao, Osman Yağan, and Virgil Gligor, “Exact analysis of k-connectivity in secure sensor networks with unreliable links,” in 2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2015, pp. 191–198. Available: https://arxiv.org/abs/1409.6022

[11] Jun Zhao, Osman Yağan, and Virgil Gligor, “On topological properties of wireless sensor networks under the q-composite key predistribution scheme with on/off channels,” in 2014 IEEE International Symposium on Information Theory, 2014, pp. 1131–1135. Available: https://arxiv.org/abs/1408.5082

[12] Jun Zhao, “Minimum node degree and k-connectivity in wireless networks with unreliable links,” in 2014 IEEE International Symposium on Information Theory, 2014, pp. 246–250. Available: https://ieeexplore.ieee.org/abstract/document/6874832

[13] Faruk Yavuz, Jun Zhao, Osman Yağan, and Virgil Gligor, “On secure and reliable communications in wireless sensor networks: Towards k-connectivity under a random pairwise key predistribution scheme,” in 2014 IEEE International Symposium on Information Theory, 2014, pp. 2381–2385. Available: https://ieeexplore.ieee.org/document/6875260

[14] Jun Zhao, Osman Yağan, and Virgil Gligor, “On the strengths of connectivity and robustness in general random intersection graphs,” in 53rd IEEE Conference on Decision and Control, 2014, pp. 3661–3668. Available: https://arxiv.org/abs/1409.5995

[15] Jun Zhao, Osman Yağan, and Virgil Gligor, “Connectivity in secure wireless sensor networks under transmission constraints,” in 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2014, pp. 1294–1301. Available: https://ieeexplore.ieee.org/document/7028605

[16] Jun Zhao, Osman Yağan, and Virgil Gligor, “Secure k-connectivity in wireless sensor networks under an on/off channel model,” in 2013 IEEE International Symposium on Information Theory, 2013, pp. 2790–2794. Available: https://ieeexplore.ieee.org/document/6620734

[17] Xiao Wang, Xinbing Wang, and Jun Zhao, “Impact of mobility and heterogeneity on coverage and energy consumption in wireless sensor networks,” in 2011 31st International Conference on Distributed Computing Systems, 2011, pp. 477–487. Available: https://ieeexplore.ieee.org/document/5961702

[18] Chenhui Hu, Xinbing Wang, Ding Nie, and Jun Zhao, “Multicast scaling laws with hierarchical cooperation,” in 2010 Proceedings IEEE INFOCOM, 2010, pp. 1–9. Available: https://ieeexplore.ieee.org/document/5462000