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Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution
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
ConDTC: Contrastive Deep Trajectory Clustering for Fine-Grained Mobility Pattern Mining
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
Blockchain-Enabled Collaborative Task Offloading for Zero-Trust Vehicular Fog Computing
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
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
Joint Uplink and Downlink NOMA for UAV Relaying Network With Multi-Pair Users
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
Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing
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
A Survey on Federated Learning in Intelligent Transportation Systems
Rongqing Zhang;Jingxin Mao;Hanqiu Wang;Bing Li;Xiang Cheng;Liuqing Yang
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
Reputation-Based Collaborative Decision-Making in Hierarchical Blockchain-Enabled Vehicular Networks
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
Adaptive Security Service Provisioning for Industrial IoT: Harnessing Deep Reinforcement Learning Within a Slice-Specific Framework
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
A Comprehensive Federated Learning Framework for Diabetic Retinopathy Grading and Lesion Segmentation
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
Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving
Jianan Zhang;Zhiwei Wei;Boxun Liu;Xiayi Wang;Yong Yu;Rongqing Zhang
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
Autonomous Intersection Management with Heterogeneous Vehicles: A Multi-Agent Reinforcement Learning Approach
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
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
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
Toward Ever-Evolution Network Threats: A Hierarchical Federated Class-Incremental Learning Approach for Network Intrusion Detection in IIoT
IEEE Internet of Things Journal
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
Robust Multi-UAV Placement Optimization for AOA-Based Cooperative Localization
Lingyun Zhou;Xinzhi Ning;Ming-Yi You;Rongqing Zhang;Qingjiang Shi
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
A Flexible Load Balancing Scheme in Multi-UAV-Enabled Wireless Networks
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
Joint Trajectory Design and Power Allocation in NOMA-Based UAV Networks
Jia Li;Xiangping Bryce Zhai;Hongyan Qian;Rongqing Zhang;Xin Liu
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
UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach
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
Hierarchical Task Offloading for Vehicular Fog Computing Based on Multi-Agent Deep Reinforcement Learning
Yukai Hou;Zhiwei Wei;Rongqing Zhang;Xiang Cheng;Liuqing Yang
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
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
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
Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach
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
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
Joint Uplink and Downlink NOMA for UAV Relaying Network with Multi-Pair Users
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
HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding
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
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
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