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Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

Digital Communications and networks 2023年第4期9卷 836-845页

作者： Siguang Chen Bei Tang Kun Wang School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu Engineering Research Center of Communications and Network Technology Nanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA

In view of the randomness distribution of multiple users in the dynamic large-scale internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great *** a result,this paper studies an efficient and intelligent computation offloading mechanism with resource ***,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission ***,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading(TD3PG-ICO)algorithm is proposed to solve this optimization *** combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading ***,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random *** available resources of the target node are dynamically allocated to improve the model decision-making ***,the simulation results show that this proposed algorithm has faster convergence speed and good *** can always approach the greedy algorithm with respect to the lowest total energy ***,compared with full local and Deep Q-learning network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.

关键词： Fog computing Computation offloading Deep reinforcement learning Resource allocation

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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Block-based fine-grained and publicly verifiable data deletion for cloud storage: Block-based fine-grained and publicly verifiable..: C. Yang et al.

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Soft Computing 2024年第21期28卷 12491-12506页

作者： Yang, Changsong Liu, Yueling Ding, Yong Wu, Yongqiang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education 999077 Hong Kong Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China School of Computer Guangdong University of Science & Technology Dongguan523083 China South China Technical Support Department Topsec Network Technology Inc. Guangzhou510000 China

One of the most important services provided by cloud service providers (CSPs), cloud storage is economically attractive and can provide on-demand data storage service to resource-constrained tenants in the manner of pay-per-use. Therefore, by embracing cloud storage service, tenants can store their large-scale data on the remote cloud host to tremendously reduce local storage burden and computational overhead. However, due to the separation of cloud data ownership and management, cloud storage inevitably suffers from a few new security challenges, such as fine-grained cloud data deletion. To achieve data deletion in cloud storage, we design a vector-commitment-based publicly verifiable data deletion scheme, which can achieve data integrity auditing and block-based fine-grained data deletion. Specifically, our proposed scheme disguises the data deletion operation as data update operation. Therefore, the tenants can achieve data deletion by updating the cloud data blocks with some unrelated data blocks. Meanwhile, the tenants can prevent cloud data from being polluted by periodically performing data integrity auditing operation, thus guaranteeing that the cloud host sincerely maintains the data. Moreover, we formally analyze the security, which shows that our proposed scheme can satisfy all of the anticipative security requirements without interaction with a centralized third party. Finally, we develop a prototype system and evaluate the performance of our proposed scheme, which can intuitively demonstrate the high-efficiency and practicality of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Data integrity

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP-PP页

作者： Wang, Muyu Zhao, Sanyuan Dong, Xingping Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China Beijing Institute of Technology School of Computer Science Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://***/muyuWang/HHNeRF. © 1995-2012 IEEE.

关键词： Rendering (computer graphics)

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Deep Cut-informed Graph Embedding and Clustering

arXiv

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arXiv 2025年

作者： Ning, Zhiyuan Wang, Zaitian Zhang, Ran Xu, Ping Liu, Kunpeng Wang, Pengyang Ju, Wei Wang, Pengfei Zhou, Yuanchun Cambria, Erik Chen, Chong Computer Network Information Center CAS China University of Chinese Academy of Sciences China Department of Computer Science Portland State University United States Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau China College of Computer Science Sichuan University China School of Computer Science and Engineering Nanyang Technological University Singapore Future City Lab Terminus Group Singapore

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there is a common issue of representation collapse in existing GNN-based deep graph clustering algorithms. We attribute two main reasons for such issues: (i) the inductive bias of GNN models: GNNs tend to generate similar representations for proximal nodes. Since graphs often contain a non-negligible amount of inter-cluster links, the bias results in error message passing and leads to biased clustering;(ii) the clustering guided loss function: most traditional approaches strive to make all samples closer to pre-learned cluster centers, which causes a degenerate solution assigning all data points to a single label thus making all samples similar and less discriminative. To address these challenges, we investigate graph clustering from a graph cut perspective and propose an innovative and non-GNN-based Deep Cut-informed Graph embedding and Clustering framework, namely DCGC. This framework includes two modules: (i) cut-informed graph encoding;(ii) self-supervised graph clustering via optimal transport. For the encoding module, we derive a cut-informed graph embedding objective to fuse graph structure and attributes by minimizing their joint normalized cut. For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments, which can balance the guidance of "proximity to the pre-learned cluster center". With the above two tailored designs, DCGC is more suitable for the graph clustering task, which can effectively alleviate the problem of representation collapse and achieve better performance. We conduct extensive experiments to demonstrate that our method is simple but effective compared with benchmarks. © 2025, CC BY.

关键词： Graph embeddings

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The Classification Method of EEG Motor Imagery Based on INFO-LSSVM 22

The Classification Method of EEG Motor Imagery Based on INFO...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Xinrong Wang Abdelkader Nasreddine Belkacem Penghai Li Zufeng Zhang Jun Liang Dongdong Du Chao Chen Tianjin University of Technology China Department of Computer and Network Engineering College of Information Technology UAE University UAE Tianjin Medical University General Hospital China China Academy of Industrial Internet China

ISBN: (纸本)9781450397056

For the current situation that the classification accuracy of EEG motor image data is not high in the BCI system, a vector weighted average algorithm optimization algorithm is proposed, and the optimized least squares support vector machine algorithm is proposed to classify the EEG motor image data. A motor imagination EEG experimental paradigm was designed and compared with the unoptimized LSSVM and three other typical classification methods on the same dataset. The experimental data were band-pass filtered by the fourth-order Butterworth filter of 0.5-30Hz, and the electrical interference was removed by independent component analysis. The HHT features obtained by empirical mode decomposition (EMD) and Hilbert Yellow transform (HHT) in the time-frequency domain were input into INFO-LSSVM for classification. Compared with dense feature fusion convolutional neural network (DFFN), Restricted Boltzmann machine optimized support vector Machine classifier (RBM-SVM) and public space pattern based artificial Neural network (CSP-ANN) classification algorithm, the highest classification accuracy of the proposed algorithm is 92.13%, and the average accuracy is 90.325%. It can be seen that compared with the existing algorithms with higher performance, the proposed algorithm effectively improves the classification accuracy and can better classify and identify EEG signals, which provides a new optimization idea for people's EEG signal classification.

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Characterization of Embedded and Thinned RF Chips

Characterization of Embedded and Thinned RF Chips

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European Microelectronics and Packaging Conference, EMPC

作者： Ran Yin Helmuth P. E. Morath Christian Hoyer Krzysztof Nieweglowski Karsten Meier Jens Wagner Frank Ellinger Karlheinz Bock Centre for Tactile Internet with Human-in-the-Loop (CeTI) Institute of Electronic Packaging Technology Technische Universität Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Chair of Circuit Design and Network Theory Technische Universität Dresden Germany Department of Electrical and Computer Engineering Chair of Circuit Design and Network Theory Technische Universität Dresden Germany Department of Electrical and Computer Engineering Institute of Electronic Packaging Technology Technische Universität Dresden Germany

This work studies the effect of thinning down chips with transmission line structures for flexible embedding technology. Test chips with 90 Ω grounded coplanar waveguide (G-CPW) transmission lines have been designed and manufactured to characterize the high-frequency performance before and after embedding in sheet molding compound (SMC), and thinning down from 300 µm to 20 µm, Molding first technology enables single-die or multiple-die thinning with the embedding material while being held in the exact position, where the subsequent fan-out copper (Cu) redistribution layer (RDL) structuring realizes the direct contacts to the embedded chip, followed by semi-additive manufacturing of traces. The embedded and thinned chips are characterized by S-parameter measurements in the frequency range from 20 MHz to 65 GHz using a vector network analyzer (VNA). The results show the feasibility of the examined embedding and thinning processes which are suitable for millimeter wave (mmWave) components as shown by the example of a G-CPW transmission line without degrading its RF performance in terms of characteristic impedance and attenuation.

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DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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