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学者姓名：程红举
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In Internet-of-Vehicles (IoV), Unmanned Aerial Vehicles (UAVs) assisted Mobile Edge Computing (MEC) can improve the system performance and communication range of Intelligent Transportation Systems (ITS). However, the resource allocation and computation offloading in UAVs-assisted IoV systems still face huge challenges due to the growing number of Vehicle Terminals (VTs), potential privacy leakage, and inefficient problem-solving. Existing solutions cannot adapt to such dynamic multi-UAV scenarios and meet the real-time requirements of VTs. To address these challenges, we propose RACOMU, a novel Resource Allocation and Collaborative Offloading framework for Multi-UAV-assisted IoV. First, we introduce the convex optimization theory to decouple the original problem and then obtain the near-optimal allocation of transmission power and computing resources by solving the Karush-Kuhn-Tucker (KKT) condition. Next, we design a new collaborative offloading strategy with Federated Deep Reinforcement Learning (FDRL), where the offloading requests from VTs are processed in a distributed manner to approach the global optimum while preserving data privacy. Extensive experiments verify the effectiveness of the proposed RACOMU. Compared to benchmark methods, RACOMU achieves better performance in terms of task processing latency, decision-making time, and load balancing degree under various scenarios. © 2014 IEEE.
Keyword ：
computation offloading computation offloading convex optimization convex optimization Federated Deep Reinforcement Learning Federated Deep Reinforcement Learning Internet-of-Vehicles Internet-of-Vehicles resource allocation resource allocation
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| GB/T 7714 | Chen, Z. , Huang, Z. , Zhang, J. et al. Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV with Federated Deep Reinforcement Learning [J]. | IEEE Internet of Things Journal , 2024 , 12 (5) : 4629-4640 . |
| MLA | Chen, Z. et al. "Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV with Federated Deep Reinforcement Learning" . | IEEE Internet of Things Journal 12 . 5 (2024) : 4629-4640 . |
| APA | Chen, Z. , Huang, Z. , Zhang, J. , Cheng, H. , Li, J. . Resource Allocation and Collaborative Offloading in Multi-UAV-Assisted IoV with Federated Deep Reinforcement Learning . | IEEE Internet of Things Journal , 2024 , 12 (5) , 4629-4640 . |
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Sentiment analysis plays an indispensable part in human-computer interaction. Multimodal sentiment analysis can overcome the shortcomings of unimodal sentiment analysis by fusing multimodal data. However, how to extracte improved feature representations and how to execute effective modality fusion are two crucial problems in multimodal sentiment analysis. Traditional work uses simple sub-models for feature extraction, and they ignore features of different scales and fuse different modalities of data equally, making it easier to incorporate extraneous information and affect analysis accuracy. In this paper, we propose a Multimodal Sentiment Analysis model based on Multi-scale feature extraction and Multi-task learning (M(3)SA). First, we propose a multi-scale feature extraction method that models the outputs of different hidden layers with the method of channel attention. Second, a multimodal fusion strategy based on the key modality is proposed, which utilizes the attention mechanism to raise the proportion of the key modality and mines the relationship between the key modality and other modalities. Finally, we use the multi-task learning approach to train the proposed model, ensuring that the model can learn better feature representations. Experimental results on two publicly available multimodal sentiment analysis datasets demonstrate that the proposed method is effective and that the proposed model outperforms baselines.
Keyword ：
multimodal data fusion multimodal data fusion Multimodal sentiment analysis Multimodal sentiment analysis multi-scale feature extraction multi-scale feature extraction Multitasking Multitasking multi-task learning multi-task learning
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| GB/T 7714 | Lin, Changkai , Cheng, Hongju , Rao, Qiang et al. M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning [J]. | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 : 1416-1429 . |
| MLA | Lin, Changkai et al. "M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning" . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 32 (2024) : 1416-1429 . |
| APA | Lin, Changkai , Cheng, Hongju , Rao, Qiang , Yang, Yang . M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 , 1416-1429 . |
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Edge computing is an emerging promising computing paradigm, which can significantly reduce the service latency by moving computing and storage demands to the edge of the network. Resource -constrained edge servers may fail to process multiple tasks simultaneously when several time -delay -sensitive and computationally demanding tasks are offloaded to only one edge server, and results in some issues such as high task processing costs. In this paper, we introduce a novel idea by dividing one task into several sub -tasks via the dependencies within the task and then offloading the sub -tasks to other edge servers in light of high concurrency for synchronization to minimize the total cost of task processing. To address the challenge of task dependencies and adaptation to dynamic scenes, we propose a Multi -Task Dependency Offloading Algorithm (MTDOA) based on deep reinforcement learning. The task offloading decision is modeled as a Markov decision process, and then a graph attention network is applied to extract the dependency information of different tasks, while LSTM and DQN are combined to deal with sequential problems. The simulation results show that the proposed MTDOA has better convergence ability compared with the baseline algorithms.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Edge computing Edge computing Graph attention network Graph attention network Multi-task dependency Multi-task dependency Task offloading Task offloading
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| GB/T 7714 | Zhang, Xiaoqi , Lin, Tengxiang , Lin, Cheng-Kuan et al. Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency [J]. | THEORETICAL COMPUTER SCIENCE , 2024 , 993 . |
| MLA | Zhang, Xiaoqi et al. "Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency" . | THEORETICAL COMPUTER SCIENCE 993 (2024) . |
| APA | Zhang, Xiaoqi , Lin, Tengxiang , Lin, Cheng-Kuan , Chen, Zhen , Cheng, Hongju . Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency . | THEORETICAL COMPUTER SCIENCE , 2024 , 993 . |
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More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. Firstly, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Secondly, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a semi-trusted environment and encourages witnesses to respond to requests for traffic information. Additionally, PIAS uses blockchain accounts as identities to participate in the system with incentives, ensuring privacy in anonymous announcements. PIAS successfully protects the privacy of participants and motivates witnesses to respond to requests. Furthermore, our assessment of security and compatibility shows that PIAS can maintain privacy and incentivization while being compatible with both the Bitcoin and Ethereum blockchains. Further evaluation has confirmed the system's efficiency in terms of performance. IEEE
Keyword ：
Authentication Authentication Bitcoin Bitcoin Blockchain Blockchain Blockchains Blockchains Fair Payment Fair Payment Incentive Mechanism Incentive Mechanism Internet of Vehicles Internet of Vehicles Privacy Privacy Privacy Preservation Privacy Preservation Protocols Protocols Reliability Reliability
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| GB/T 7714 | Zhan, Y. , Yang, Y. , Cheng, H. et al. PIAS: Privacy-Preserving Incentive Announcement System based on Blockchain for Internet of Vehicles [J]. | IEEE Transactions on Services Computing , 2024 , 17 (5) : 1-14 . |
| MLA | Zhan, Y. et al. "PIAS: Privacy-Preserving Incentive Announcement System based on Blockchain for Internet of Vehicles" . | IEEE Transactions on Services Computing 17 . 5 (2024) : 1-14 . |
| APA | Zhan, Y. , Yang, Y. , Cheng, H. , Luo, X. , Guan, Z. , Deng, R.H. . PIAS: Privacy-Preserving Incentive Announcement System based on Blockchain for Internet of Vehicles . | IEEE Transactions on Services Computing , 2024 , 17 (5) , 1-14 . |
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With flexible mobility and broad communication coverage, unmanned aerial vehicles (UAVs) have become an important extension of multiaccess edge computing (MEC) systems, exhibiting great potential for improving the performance of federated graph learning (FGL). However, due to the limited computing and storage resources of UAVs, they may not well handle the redundant data and complex models, causing the inference inefficiency of FGL in UAV-assisted MEC systems. To address this critical challenge, we propose a novel LightWeight FGL framework, named LW-FGL, to accelerate the inference speed of classification models in UAV-assisted MEC systems. Specifically, we first design an adaptive information bottleneck (IB) principle, which enables UAVs to obtain well-compressed worthy subgraphs by filtering out the information that is irrelevant to downstream classification tasks. Next, we develop improved tiny graph neural networks (GNNs), which are used as the inference models on UAVs, thus reducing the computational complexity and redundancy. Using real-world graph data sets, extensive experiments are conducted to validate the effectiveness of the proposed LW-FGL. The results show that the LW-FGL achieves higher classification accuracy and faster inference speed than state-of-the-art methods.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Biological system modeling Biological system modeling Classification inference Classification inference Computational modeling Computational modeling Data models Data models federated graph learning (FGL) federated graph learning (FGL) Graph neural networks Graph neural networks lightweight model lightweight model multiaccess edge computing (MEC) multiaccess edge computing (MEC) Task analysis Task analysis Training Training unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Zhong, Luying , Chen, Zheyi , Cheng, Hongju et al. Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) : 21180-21190 . |
| MLA | Zhong, Luying et al. "Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems" . | IEEE INTERNET OF THINGS JOURNAL 11 . 12 (2024) : 21180-21190 . |
| APA | Zhong, Luying , Chen, Zheyi , Cheng, Hongju , Li, Jie . Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) , 21180-21190 . |
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As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies.
Keyword ：
cache space partitioning cache space partitioning content popularity prediction content popularity prediction Multi-edge collaborative caching Multi-edge collaborative caching proactive cache replacement proactive cache replacement robust federated deep learning robust federated deep learning
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| GB/T 7714 | Chen, Zheyi , Liang, Jie , Yu, Zhengxin et al. Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 . |
| MLA | Chen, Zheyi et al. "Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning" . | IEEE-ACM TRANSACTIONS ON NETWORKING (2024) . |
| APA | Chen, Zheyi , Liang, Jie , Yu, Zhengxin , Cheng, Hongju , Min, Geyong , Li, Jie . Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 . |
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Abstract ：
More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. First, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Second, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a semi-trusted environment and encourages witnesses to respond to requests for traffic information. Additionally, PIAS uses blockchain accounts as identities to participate in the system with incentives, ensuring privacy in anonymous announcements. PIAS successfully protects the privacy of participants and motivates witnesses to respond to requests. Furthermore, our assessment of security and compatibility shows that PIAS can maintain privacy and incentivization while being compatible with both the Bitcoin and Ethereum blockchains. Further evaluation has confirmed the system's efficiency in terms of performance.
Keyword ：
Authentication Authentication Bitcoin Bitcoin blockchain blockchain Blockchains Blockchains fair payment fair payment incentive mechanism incentive mechanism Internet of Vehicles Internet of Vehicles Internet of Vehicles (IoV) Internet of Vehicles (IoV) Privacy Privacy privacy preservation privacy preservation Protocols Protocols Reliability Reliability
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| GB/T 7714 | Zhan, Yonghua , Yang, Yang , Cheng, Hongju et al. PIAS: Privacy-Preserving Incentive Announcement System Based on Blockchain for Internet of Vehicles [J]. | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2024 , 17 (5) : 2762-2775 . |
| MLA | Zhan, Yonghua et al. "PIAS: Privacy-Preserving Incentive Announcement System Based on Blockchain for Internet of Vehicles" . | IEEE TRANSACTIONS ON SERVICES COMPUTING 17 . 5 (2024) : 2762-2775 . |
| APA | Zhan, Yonghua , Yang, Yang , Cheng, Hongju , Luo, Xiangyang , Guan, Zhangshuang , Deng, Robert H. . PIAS: Privacy-Preserving Incentive Announcement System Based on Blockchain for Internet of Vehicles . | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2024 , 17 (5) , 2762-2775 . |
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To address the issue of poor detection performance for multi-scale objects in multi-view pedestrian detection algorithms, a dilated encoder method is proposed for aggregating multi-view information. The dilated encoder utilizes dilated convolutions with different dilation rates in a single layer to generate receptive fields of different scales, thus covering the entire scale range of the targets and improving the detection capability for multi-scale objects. Experimental results on the Wildtrack dataset show that the algorithm achieves a maximum multiple object detection accuracy of 90. 7% . © 2023 Beijing University of Posts and Telecommunications. All rights reserved.
Keyword ：
crowded scene crowded scene dilated convolution dilated convolution feature fusion feature fusion multi-scale detection multi-scale detection multi-view data multi-view data pedestrian detection pedestrian detection
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| GB/T 7714 | Ye, H. , Lin, Z. , Cheng, H. . Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法] [J]. | Journal of Beijing University of Posts and Telecommunications , 2023 , 46 (5) : 66-71 . |
| MLA | Ye, H. et al. "Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法]" . | Journal of Beijing University of Posts and Telecommunications 46 . 5 (2023) : 66-71 . |
| APA | Ye, H. , Lin, Z. , Cheng, H. . Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法] . | Journal of Beijing University of Posts and Telecommunications , 2023 , 46 (5) , 66-71 . |
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In this article, we propose dual traceable distributed attribute based encryption with subset keyword search system (DTDABE-SKS, abbreviated as DT) to simultaneously realize data source trace (secure provenance) and user trace (traitor trace) and flexible subset keyword search from polynomial interpolation. Leveraging non-interactive zero-knowledge proof technology, DT preserves privacy for both data providers and users in normal circumstances, but a trusted authority can disclose their real identities if necessary, such as the providers deceitfully uploading false data or users maliciously leaking secret attribute key. Next, we introduce the new conception of updatable and transferable message-lock encryption (UT-MLE) for block-level dynamic encrypted file update, where the owner does not have to download the whole ciphertext, decrypt, re-encrypt and upload for minor document modifications. In addition, the owner is permitted to transfer file ownership to other system customers with efficient computation in an authenticated manner. A nontrivial integration of DT and UT-MLE lead to the distributed ABSE with ownership transfer system (DTOT) to enjoy the above merits. We formally define DT, UT-MLE, and their security model. Then, the instantiations of DT and UT-MLE, and the formal security proof are presented. Comprehensive comparison and experimental analysis based on real dataset affirm their feasibility.
Keyword ：
ABE ABE Cloud computing Cloud computing Data privacy Data privacy Distributed databases Distributed databases Dual traceability Dual traceability Encryption Encryption Hospitals Hospitals Keyword search Keyword search Maximum likelihood estimation Maximum likelihood estimation ownership transferable ownership transferable searchable encryption searchable encryption updatable MLE updatable MLE
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| GB/T 7714 | Yang, Yang , Deng, Robert H. , Guo, Wenzhong et al. Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer [J]. | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2023 , 11 (1) : 247-262 . |
| MLA | Yang, Yang et al. "Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer" . | IEEE TRANSACTIONS ON CLOUD COMPUTING 11 . 1 (2023) : 247-262 . |
| APA | Yang, Yang , Deng, Robert H. , Guo, Wenzhong , Cheng, Hongju , Luo, Xiangyang , Zheng, Xianghan et al. Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer . | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2023 , 11 (1) , 247-262 . |
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By effectively assigning and migrating tasks based on service requirements, the success rate of task execution in cloud-edge-end collaborative computing can be significantly enhanced, thereby ensuring the provision of high-quality services for users. The majority of conventional cloud-edge-end task offloading approaches primarily focus on static scenarios, posing challenges in ensuring the success rate of task execution in mobile scenarios. It is imperative to address the problem of constructing a joint optimization scheme for task allocation and migration that is suitable for mobile scenarios. This paper re-define the latency, energy, and migration model for task processing in mobile scenarios. Furthermore, we propose a Deep Reinforcement learning (DRL)-based Task allocation and Migration optimization algorithm (DRTM) to enhance the efficiency of task completion and minimize the total cost. DRTM introduces the traditional Actor-Critic with a mirror deep deterministic policy gradient (DDPG) and establishes a duel Q-network to update parameters on respective gradients for optimal policy acquisition. DRTM incorporates two target networks to effectively improve stability and convergence speed during training while reducing computational complexity. The experimental results demonstrate that DRTM can offer a high-performance task assignment and migration scheme in mobile scenarios, thereby significantly reducing the total cost of the task execution life cycle. © 2023 IEEE.
Keyword ：
Computation offloading Computation offloading Deep learning Deep learning Life cycle Life cycle Reinforcement learning Reinforcement learning
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| GB/T 7714 | Zhang, Xiaoqi , Cheng, Hongju . Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL [C] . 2023 : 265-270 . |
| MLA | Zhang, Xiaoqi et al. "Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL" . (2023) : 265-270 . |
| APA | Zhang, Xiaoqi , Cheng, Hongju . Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL . (2023) : 265-270 . |
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