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Rongqing Zhang

Publications
137
Citations
3,290
Publications by Year
20102025
Co-Authors:
Show All Co-Authors (121)
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Rongqing Zhang

Affiliation

School of Computer Science and Technology
Tongji University
Shanghai, China

Publication Topics

Biography

Dr. Rongqing Zhang received the B.S. and Ph.D. degrees (with honors) from Peking University, Beijing, China, in 2009 and 2014, respectively. From 2014 to 2018, he worked as a research fellow at Colorado State University, CO, USA. Since 2019, he has been an Associate Professor at Tongji University, Shanghai, China. His current research interests include vehicular communications and networking, UAV, and connected intelligence. He has authored and co-authored three monographs, two book chapters, and over 150 papers in refereed journals and conference proceedings. Dr. Zhang was the co-recipient of the 2017 First Prize in Natural Science, Ministry of Education of China, the 2023 First Prize in Natural Science, Chinese Association of Automation, ...
Publications
137
Citations
3,290
Publications by Year
20102025
Co-Authors:
Show All Co-Authors (121)
Author's Published Works

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Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution

Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

IEEE Transactions on Services Computing
Year: 2025 | Volume: 18, Issue: 1 | Journal Article |
Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slic...Show More

Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution

Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

ConDTC: Contrastive Deep Trajectory Clustering for Fine-Grained Mobility Pattern Mining

Junjun Si;Jin Yang;Yang Xiang;Li Li;Bo Tu;Rongqing Zhang

IEEE Transactions on Big Data
Year: 2025 | Volume: 11, Issue: 2 | Journal Article |
Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories’ temporal regularities, which are essential for mining fine-grained mobility patterns for applications in...Show More

ConDTC: Contrastive Deep Trajectory Clustering for Fine-Grained Mobility Pattern Mining

Junjun Si;Jin Yang;Yang Xiang;Li Li;Bo Tu;Rongqing Zhang

Year: 2025 | Volume: 11, Issue: 2 | Journal Article |

Blockchain-Enabled Collaborative Task Offloading for Zero-Trust Vehicular Fog Computing

Chenran Huang;Yiting Zhao;Zhiwei Wei;Bing Li;Rongqing Zhang

GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Year: 2024 | Conference Paper |
In this paper, we focus on the task offloading problem for zero-trust vehicular fog computing (VFC) to promote trustworthy collaborative computing among vehicles. We propose a blockchain-enabled zero-trust VFC framework (BlockZT-VFC) to continuously verify and dynamically authorize the vehicle nodes. To overcome the reliability and efficiency issues in BlockZT-VFC, a multi-attribute task offloadin...Show More

Blockchain-Enabled Collaborative Task Offloading for Zero-Trust Vehicular Fog Computing

Chenran Huang;Yiting Zhao;Zhiwei Wei;Bing Li;Rongqing Zhang

Year: 2024 | Conference Paper |

Intelligent Spectrum Management for UAV Swarm with Multiple Combat Missions: A DQN-Based Solution

Yukai Hou;Zhijun Liu;Fan Wang;Lingyun Zhou;Tao Shang;Rongqing Zhang

2024 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Year: 2024 | Conference Paper |
In unmanned aerial vehicles(UAV) swarm combat, efficient management of electromagnetic spectrum resources is crucial to ensure the operational performance of UAV swarm. However, in the presence of external radiations, the spectrum interference and competition between UAV onboard communication, radar, reconnaissance, and jamming services greatly affect the combat effectiveness of UAV swarm. Besides...Show More

Intelligent Spectrum Management for UAV Swarm with Multiple Combat Missions: A DQN-Based Solution

Yukai Hou;Zhijun Liu;Fan Wang;Lingyun Zhou;Tao Shang;Rongqing Zhang

Year: 2024 | Conference Paper |

Joint Uplink and Downlink NOMA for UAV Relaying Network With Multi-Pair Users

Xu Guo;Bing Li;Jianjun Wu;Rongqing Zhang;Xiang Cheng

IEEE Transactions on Wireless Communications
Year: 2024 | Volume: 23, Issue: 12 | Journal Article |
Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-orthogonal multiple access (NOMA) in both uplink and downlink relaying hops in an amplify-and-forward (AF) based UAV relaying network with multiple source...Show More

Joint Uplink and Downlink NOMA for UAV Relaying Network With Multi-Pair Users

Xu Guo;Bing Li;Jianjun Wu;Rongqing Zhang;Xiang Cheng

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing

Zhiwei Wei;Jingxin Mao;Bing Li;Rongqing Zhang

IEEE Open Journal of the Communications Society
Year: 2024 | Early Access Article |
Vehicular Fog Computing (VFC) is a promising paradigm in intelligent transportation systems (ITS), which offloads computation-intensive tasks to mobile fog nodes for real-time and low-latency services. In the forthcoming era of low-altitude economy, Unmanned Aerial Vehicles (UAVs) are being integrated as task-carrying entities into the ITS, and the novel low-altitude VFC is witnessing new challeng...Show More

Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing

Zhiwei Wei;Jingxin Mao;Bing Li;Rongqing Zhang

Year: 2024 | Early Access Article |

A Survey on Federated Learning in Intelligent Transportation Systems

Rongqing Zhang;Jingxin Mao;Hanqiu Wang;Bing Li;Xiang Cheng;Liuqing Yang

IEEE Transactions on Intelligent Vehicles
Year: 2024 | Early Access Article |
Cited by: Papers (10)
The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and ...Show More

A Survey on Federated Learning in Intelligent Transportation Systems

Rongqing Zhang;Jingxin Mao;Hanqiu Wang;Bing Li;Xiang Cheng;Liuqing Yang

Year: 2024 | Early Access Article |

Reputation-Based Collaborative Decision-Making in Hierarchical Blockchain-Enabled Vehicular Networks

Tenghui Peng;Rongqing Zhang;Xiang Cheng;Liuqing Yang

ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
In vehicular networks, collaborative decision-making can improve the recognition capability and driving efficiency of vehicles, while easy to suffer from false information injection from malicious nodes (MNs). Therefore, in this paper, we investigate collaborative decision-making in a hierarchical blockchain-enabled vehicular network in the presence of self-interested MNs. In order to improve the ...Show More

Reputation-Based Collaborative Decision-Making in Hierarchical Blockchain-Enabled Vehicular Networks

Tenghui Peng;Rongqing Zhang;Xiang Cheng;Liuqing Yang

Year: 2024 | Conference Paper |

Adaptive Security Service Provisioning for Industrial IoT: Harnessing Deep Reinforcement Learning Within a Slice-Specific Framework

Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
The Industrial Internet of Things (IIoT) continues to evolve alongside advancements in 5G and beyond 5G communication technologies, and network slicing has emerged as a promising technique to offer isolated slices and tailored services across industrial use cases, which profoundly affects the traditional security solution. As the future IIoT evolves towards the post-5G era, the anticipated growth ...Show More

Adaptive Security Service Provisioning for Industrial IoT: Harnessing Deep Reinforcement Learning Within a Slice-Specific Framework

Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

Year: 2024 | Conference Paper |

A Comprehensive Federated Learning Framework for Diabetic Retinopathy Grading and Lesion Segmentation

Jingxin Mao;Xiaoyu Ma;Yanlong Bi;Rongqing Zhang

IEEE Transactions on Big Data
Year: 2024 | Early Access Article |
Diabetic retinopathy (DR) is a debilitating ocular complication demanding timely intervention and treatment. The rapid evolution of deep learning (DL) has notably enhanced the efficiency of conventional manual diagnosis. However, the scarcity of existing DR datasets hinders the progress of data-driven DL models, especially for pixel-level lesion annotation datasets, which severely impedes the adva...Show More

A Comprehensive Federated Learning Framework for Diabetic Retinopathy Grading and Lesion Segmentation

Jingxin Mao;Xiaoyu Ma;Yanlong Bi;Rongqing Zhang

Year: 2024 | Early Access Article |

Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving

Jianan Zhang;Zhiwei Wei;Boxun Liu;Xiayi Wang;Yong Yu;Rongqing Zhang

IEEE Wireless Communications
Year: 2024 | Volume: 31, Issue: 4 | Magazine Article |
In a dynamic autonomous driving environment, artificial intelligence-generated content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhance motion planning, trajectory prediction, and traffic simulation. This article proposes a cloud-edge-terminal collaborative architecture to support ...Show More

Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving

Jianan Zhang;Zhiwei Wei;Boxun Liu;Xiayi Wang;Yong Yu;Rongqing Zhang

Year: 2024 | Volume: 31, Issue: 4 | Magazine Article |

Autonomous Intersection Management with Heterogeneous Vehicles: A Multi-Agent Reinforcement Learning Approach

Kaixin Chen;Bing Li;Rongqing Zhang;Xiang Cheng

2024 IEEE Intelligent Vehicles Symposium (IV)
Year: 2024 | Conference Paper |
While autonomous intersection management (AIM) emerges to facilitate signal-free scheduling for connected and autonomous vehicles (CAVs), several challenges arise for planning secure and swift trajectories. Existing works mainly focus on addressing the challenge of multi-CAV interaction complexity. In this context, multi-agent reinforcement learning-based (MARL) methods exhibit higher scalability ...Show More

Autonomous Intersection Management with Heterogeneous Vehicles: A Multi-Agent Reinforcement Learning Approach

Kaixin Chen;Bing Li;Rongqing Zhang;Xiang Cheng

Year: 2024 | Conference Paper |

Joint Optimization of UAV Deployment and Directional Antenna Orientation for Multi-UAV Cooperative Sensing System

Lingyun Zhou;Wenqiang Pu;Yixin Jiang;Ming-Yi You;Rongqing Zhang;Qingjiang Shi

IEEE Transactions on Wireless Communications
Year: 2024 | Volume: 23, Issue: 10 | Journal Article |
Unmanned aerial vehicle (UAV) swarm-based sensing technology has become increasingly important due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the sensing accuracy improvement by exploiting resource coordination strategy poses a new challenge on multi-UAV sensing system. In this paper, we consider the problem of coope...Show More

Joint Optimization of UAV Deployment and Directional Antenna Orientation for Multi-UAV Cooperative Sensing System

Lingyun Zhou;Wenqiang Pu;Yixin Jiang;Ming-Yi You;Rongqing Zhang;Qingjiang Shi

Year: 2024 | Volume: 23, Issue: 10 | Journal Article |

Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT

Jingxin Mao;Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 18 | Journal Article |
Cited by: Papers (1)
The rise of collaborative manufacturing, driven by the rapid proliferation of Industrial Internet of Things (IIoT) technologies, has markedly enhanced agility and productivity in industrial environments. However, this advancement has also significantly broadened the attack surface and uncovered unique vulnerabilities intrinsic to these interconnected systems. This article introduces a novel hierar...Show More

Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT

Jingxin Mao;Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

Year: 2024 | Volume: 11, Issue: 18 | Journal Article |

Robust Multi-UAV Placement Optimization for AOA-Based Cooperative Localization

Lingyun Zhou;Xinzhi Ning;Ming-Yi You;Rongqing Zhang;Qingjiang Shi

IEEE Transactions on Intelligent Vehicles
Year: 2024 | Early Access Article |
Cited by: Papers (1)
Unmanned aerial vehicle (UAV) swarm-based localization technology has become increasingly popular due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the accuracy improvement by exploiting UAVs' placement poses a new challenge on multi-UAV localization system. This paper investigates the placement optimization problem for...Show More

Robust Multi-UAV Placement Optimization for AOA-Based Cooperative Localization

Lingyun Zhou;Xinzhi Ning;Ming-Yi You;Rongqing Zhang;Qingjiang Shi

Year: 2024 | Early Access Article |

A Flexible Load Balancing Scheme in Multi-UAV-Enabled Wireless Networks

Yang Shen;Bing Li;Rongqing Zhang;Xiang Cheng;Liuqing Yang

IEEE Transactions on Vehicular Technology
Year: 2024 | Volume: 73, Issue: 6 | Journal Article |
Unmanned aerial vehicles (UAVs) equipped with communication modules can act as mobile base stations. In this paper, we focus on load balancing in multi-UAV-enabled wireless networks, where UAVs serve ground user equipments (UEs) with time division multiple access (TDMA). The UEs have spatiotemporal heterogeneous requests to transfer data which need to be fulfilled in time, the unfinished part call...Show More

A Flexible Load Balancing Scheme in Multi-UAV-Enabled Wireless Networks

Yang Shen;Bing Li;Rongqing Zhang;Xiang Cheng;Liuqing Yang

Year: 2024 | Volume: 73, Issue: 6 | Journal Article |

Joint Trajectory Design and Power Allocation in NOMA-Based UAV Networks

Jia Li;Xiangping Bryce Zhai;Hongyan Qian;Rongqing Zhang;Xin Liu

IEEE Transactions on Vehicular Technology
Year: 2024 | Volume: 73, Issue: 2 | Journal Article |
Cited by: Papers (5)
The employment of the unmanned aerial vehicle (UAV) as communication platform has drawn a lot of interest recently. Although previous studies have presented the performance gain by utilizing the UAV's mobility and communication, they concentrate on the UAV trajectory for homogeneous devices with the same requirements of data size and latency constraints. The UAV trajectory for groups with heteroge...Show More

Joint Trajectory Design and Power Allocation in NOMA-Based UAV Networks

Jia Li;Xiangping Bryce Zhai;Hongyan Qian;Rongqing Zhang;Xin Liu

Year: 2024 | Volume: 73, Issue: 2 | Journal Article |

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

Yukai Hou;Jin Zhao;Rongqing Zhang;Xiang Cheng;Liuqing Yang

IEEE Transactions on Intelligent Vehicles
Year: 2024 | Volume: 9, Issue: 1 | Journal Article |
Cited by: Papers (11)
The development of machine learning and artificial intelligence algorithms, as well as the progress of unmanned aerial vehicle swarm technology, has significantly enhanced the intelligence and autonomy of unmanned aerial vehicles in search missions, resulting in greater efficiency when searching unknown areas. However, as search scenarios become more complex, the existing unmanned aerial vehicle s...Show More

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

Yukai Hou;Jin Zhao;Rongqing Zhang;Xiang Cheng;Liuqing Yang

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Hierarchical Task Offloading for Vehicular Fog Computing Based on Multi-Agent Deep Reinforcement Learning

Yukai Hou;Zhiwei Wei;Rongqing Zhang;Xiang Cheng;Liuqing Yang

IEEE Transactions on Wireless Communications
Year: 2024 | Volume: 23, Issue: 4 | Journal Article |
Cited by: Papers (12)
Vehicular fog computing (VFC) has been expected as a promising architecture that can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal heterogeneity of computing resources, resulting in that some regions have idle computing resources while others cannot satisf...Show More

Hierarchical Task Offloading for Vehicular Fog Computing Based on Multi-Agent Deep Reinforcement Learning

Yukai Hou;Zhiwei Wei;Rongqing Zhang;Xiang Cheng;Liuqing Yang

Year: 2024 | Volume: 23, Issue: 4 | Journal Article |

TrajBERT: BERT-Based Trajectory Recovery With Spatial-Temporal Refinement for Implicit Sparse Trajectories

Junjun Si;Jin Yang;Yang Xiang;Hanqiu Wang;Li Li;Rongqing Zhang;Bo Tu;Xiangqun Chen

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 5 | Journal Article |
Cited by: Papers (8)
In the realm of human mobility data analysis, a multitude of constraints result in the publication of sparse, non-uniform implicit trajectories without explicit location information, such as coordinates. Researchers have dedicated substantial efforts towards trajectory recovery, aiming to densify trajectories and gain a more comprehensive understanding of human mobility. However, existing trajecto...Show More

TrajBERT: BERT-Based Trajectory Recovery With Spatial-Temporal Refinement for Implicit Sparse Trajectories

Junjun Si;Jin Yang;Yang Xiang;Hanqiu Wang;Li Li;Rongqing Zhang;Bo Tu;Xiangqun Chen

Year: 2024 | Volume: 23, Issue: 5 | Journal Article |

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Zhiwei Wei;Bing Li;Rongqing Zhang;Xiang Cheng;Liuqing Yang

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 3 | Journal Article |
Cited by: Papers (30)
Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead ...Show More

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Zhiwei Wei;Bing Li;Rongqing Zhang;Xiang Cheng;Liuqing Yang

Year: 2024 | Volume: 23, Issue: 3 | Journal Article |

Transfer Learning-Based UAV Swarm Threat Perception and Evaluation Scheme in Air-Ground-Integrated Networks

Yukai Hou;Shiyang Liu;Lingyun Zhou;Rongqing Zhang;Qingjiang Shi

2023 China Automation Congress (CAC)
Year: 2023 | Conference Paper |
With the advancement of 5G and artificial intelligence technologies, unmanned aerial vehicle (UAV) has emerged as crucial elements in modern warfare. However, their small size and slow speed make them difficult to detect and easily mistaken for birds or other flying objects, posing challenges for defenders to respond promptly. Moreover, existing methods for evaluating UAV threats heavily rely on s...Show More

Transfer Learning-Based UAV Swarm Threat Perception and Evaluation Scheme in Air-Ground-Integrated Networks

Yukai Hou;Shiyang Liu;Lingyun Zhou;Rongqing Zhang;Qingjiang Shi

Year: 2023 | Conference Paper |

Joint Uplink and Downlink NOMA for UAV Relaying Network with Multi-Pair Users

Xu Guo;Bing Li;Jianjun Wu;Rongqing Zhang;Xiang Cheng

GLOBECOM 2023 - 2023 IEEE Global Communications Conference
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Unmanned aerial vehicle (UAV) relays are increasingly playing a significant role with their advantages of quick establishment for emergency communication, expansion of communication coverage, and increase of system capacity. As demand for massive simultaneous connections is ever-increasing, UAV relaying communication aided by non-orthogonal multiple access (NOMA) can more efficiently utilize spect...Show More

Joint Uplink and Downlink NOMA for UAV Relaying Network with Multi-Pair Users

Xu Guo;Bing Li;Jianjun Wu;Rongqing Zhang;Xiang Cheng

Year: 2023 | Conference Paper |

HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding

Zhaoyi Song;Rongqing Zhang;Xiang Cheng

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The Multi-Agent Path Finding (MAPF) problem is a critical challenge in dynamic multi-robot systems. Recent studies have revealed that multi-agent reinforcement learning (MARL) is a promising approach to solving MAPF problems in a fully decentralized manner. However, as the size of the multi-robot system increases, sample inefficiency becomes a major impediment to learning-based methods. This paper...Show More

HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding

Zhaoyi Song;Rongqing Zhang;Xiang Cheng

Year: 2023 | Conference Paper |

A Dual Self-supervised Deep Trajectory Clustering Method

Junjun Si;Yang Xiang;Jin Yang;Li Li;Bo Tu;Xiangqun Chen;Rongqing Zhang

2023 IEEE Symposium on Computers and Communications (ISCC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Trajectory clustering is a cornerstone task in trajectory mining. Sparse and noisy trajectories like Call Detail Records (CDR) have become popular with the rapid development of mobile applications. However, existing trajectory clustering methods' performance is limited on these trajectories. Therefore, we propose a dual Self-supervised Deep Trajectory Clustering (SDTC) method, to optimize trajecto...Show More

A Dual Self-supervised Deep Trajectory Clustering Method

Junjun Si;Yang Xiang;Jin Yang;Li Li;Bo Tu;Xiangqun Chen;Rongqing Zhang

Year: 2023 | Conference Paper |

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