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Delay Constraint Energy Efficient Cooperative Offloading in MEC for iot 16th

Delay Constraint Energy Efficient Cooperative Offloading in ...

16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Sun, Haifeng Wang, Jun Peng, Haixia Song, Lili Qin, Mingwei School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada School of Information Engineering Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783030675363

Mobile edge computing (MEC) is a promising approach to execute delay-sensitive and computation-intensive applications in the resource-limited iot mobile devices (IMDs) by offloading computing tasks to MEC servers. In this paper, we propose a neighbor-aided cooperative offloading scheme with delay constraint to improve the energy efficiency in MEC-based Internet of Things (iot) networks. The network consists of a target IMD with some IMD neighbors, and an access point integrated with an MEC server. The latency-constrained tasks in the IMD can be partially offloaded to and executed by the selected neighboring IMD or the MEC server. Different from other works, our proposed offloading scheme selects the most energy-efficient one from all the neighboring IMDs as the offloading helper. Specifically, we formulate an optimization problem to minimize the total energy consumption while satisfying the computation delay constraint of each task, and obtain the most energy-efficient neighbor with the optimized division of tasks by solving the formulated problem. Moreover, we design an easy neighbor selection scheme with lower time complexity by the weighted value of the transmission rate for each neighbor. Numerical results show that the proposed scheme outperforms benchmark schemes significantly in terms of energy consumption and the supported maximum task length. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy efficiency

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Energy Efficiency Maximization for Backscatter-Enabled Coded-Cooperative NOMA Under Imperfect SIC

arXiv

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arXiv 2022年

作者： Asif, Muhammad Ihsan, Asim Khan, Wali Ullah Zhang, Shengli Wu, Sissi Xiaoxiao The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China The School of Computer Science and Electronic Engineering Bangor University BangorLL57 1UT United Kingdom University of Luxembourg Luxembourg City1855 Luxembourg

The beyond fifth-generation (B5G) communication networks would require efficient utilization of available resources in terms of spectrum and energy to support billion of internet of things (iot) devices. In this regard, the backscatter communication (BSC) and power-domain non-orthogonal multiple access (PD-NOMA) has become the focal choice to fulfill the spectrum, massive connectivity, and energy efficiency requirements for B5G systems. In this manuscript, we propose an optimization framework to maximize the energy efficiency of the BSC-enabled cooperative NOMA system under imperfect successive interference cancellation (SIC) decoding at the receiver. Specifically, the energy efficiency of the system is maximized by optimizing the transmit power of the source, power allocation coefficients (PAC) of NOMA users, and power of the relay node. A low-complexity energy-efficient alternating optimization framework is introduced which simultaneously optimizes the transmit power of the source, PAC, and power of the relay node by considering the quality of service (QoS), power budget, and cooperation constraints under the imperfect SIC decoding. Subsequently, a joint channel coding framework is provided to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes obtained from cyclic balanced sampling plans excluding contiguous units (CBSEC). Simulation results evince that the proposed BSC-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system w

关键词： Energy efficiency

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Performance analysis and comparison of PoW,PoS and DAG based blockchains

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Digital Communications and Networks 2020年第4期6卷 480-485页

作者： Bin Cao Zhenghui Zhang Daquan Feng Shengli Zhang Lei Zhang Mugen Peng Yun Li The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China The State Key Laboratory of Integrated Services Networks(Xidian University) Xian710000China School of Communications and Information Engineering and Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and TelecommunicationsChongqing400065China The Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China College of Information Engineering Shenzhen UniversityShenzhen518060China School of Engineering University of GlasgowGlasgowG128QQUK

In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the *** blockchain consensus mechanisms have been ***,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/*** this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure *** results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load *** addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.

关键词： Blockchain Proof of work Proof of stake Direct acyclic graph Performance comparison

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Analysis of non-Markovian passive quantum linear systems’ response to single-photon input fields*

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IFAC-PapersOnLine 2023年第2期56卷 5179-5184页

作者： Zhengyi Sun Shibei Xue Zibo Miao Zhiyuan Dong Dewei Li Lulu Pan Min Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 P. R. China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen 518055 P. R. China School of Electronics and Information Engineering Soochow University Suzhou 215006 P. R. China

In this paper, we analyze the response of passive quantum linear systems in a non-Markovian environment to single photon input fields. Based on an augmented modelling method for non-Markovian quantum systems, analytic forms of output intensity and output covariance function in steady-state are derived. These results allow us to quantitatively analyze the influence of non-Markovian environment on system response under a framework of linear system theory.

关键词： Non-Markovian quantum systems single photon state passive quantum linear system response theory

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Gated SwitchGAN for multi-domain facial image translation

arXiv

引用

arXiv 2021年

作者： Zhang, Xiaokang Zhu, Yuanlue Chen, Wenting Liu, Wenshuang Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Rec...

引用

International Conference on Pattern Recognition

作者： Wenshuang Liu Wenting Chen Yuanlue Zhu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR). The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-lM) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

关键词： Training Face recognition Generators

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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

arXiv

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Cheng, Jiayi Gao, Can Zhou, Jie Wen, Jiajun Dai, Tao Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1% and 9.3% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The source code will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Domain generalization for document authentication against practical recapturing attacks

arXiv

引用

arXiv 2021年

作者： Chen, Changsheng Zhang, Shuzheng Lan, Fengbo Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision *** Codes 94A08 Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

引用

arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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