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New Two-Stage Deep Reinforcement Learning for Task Admission and Channel Allocation of Wireless-Powered Mobile Edge computing

New Two-Stage Deep Reinforcement Learning for Task Admission...

引用

IEEE international conference on Communications (ICC)

作者： Chen, Xiaojing Dai, Weiheng Ni, Wei Wang, Xin Zhang, Shunqing Xu, Shugong Sun, Yanzan Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai Peoples R China Commonwealth Sci & Ind Res Org CSIRO Canberra ACT Australia Fudan Univ Dept Commun Sci & Amp Engn Key Lab EMW Informat MoE Shanghai Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

关键词： Mobile edge computing wireless power transfer energy-efficient resource allocation deep Q-network

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

引用

2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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Key Analysis of Integer Factorizing based Public-Key Cryptosystems Using Machine Learning 7

Key Analysis of Integer Factorizing based Public-Key Cryptos...

引用

7th international conference on Advances in Artificial Intelligence, ICAAI 2023

作者： Alia, Mohammad Ahmad Jaradat, Yousef Alshehadeh, Abdul Razzak Faculty of Science and It Al-Zaytoonah University of Jordan Amman Jordan Faculty of Engineering and Technology Al-Zaytoonah University of Jordan Amman Jordan Faculty of Business Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798400708985

This paper discusses the possibilities of using machine learning models to analyze the keys in public key encryption algorithms that are based on various mathematical hard problems. Given their distinct domains within computer science, machine learning and public key cryptosystems may appear unrelated at first glance. Nonetheless, there are intriguing correlations and potential points of convergence between these two domains. In general, machine learning models have the potential to help with differential cryptanalysis, a technique that uses statistical differences between plaintext-ciphertext pairs to deduce parts of the key. As a result, breaking encryption keys using machine learning is a complex and evolving research area that investigates cryptanalysis machine learning techniques. While public key encryption algorithms were designed to be secure against various attacks such as brute force, advances in computing power and machine learning have raised concerns about the potential emergence of new attack vectors. © 2023 ACM.

关键词： Public key cryptography

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Circuit Implementation of Quantum Error Correction Codes Using Qiskit

Circuit Implementation of Quantum Error Correction Codes Usi...

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international conference on Information and Communication technology convergence (ICTC)

作者： Shengyue Heng Jun Heo Korea University School of Electrical Engineering Seoul Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Quantum error correction (QEC) is a fundamental aspect of quantum computing, addressing one of the biggest challenges in building reliable quantum computers, which has been developing rapidly these days. In this paper, we introduce some QEC codes, including CSS code and surface code, and make the implementation of CSS code on the symmetric quantum channel using qiskit. Besides, we also checked the logical error rates of both CSS code and surface code.

关键词： Computers Fault tolerance Codes Quantum computing Error analysis Noise Quantum channels Fault tolerant systems Error correction codes Information and communication technology

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Distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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In-Sensor Learning on Magnetic Tunnel Junction Devices Using the Forward-Forward Algorithm

In-Sensor Learning on Magnetic Tunnel Junction Devices Using...

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Sensors and Information technology (ICSI), international conference on

作者： Jiayuan Wang Minhui Ji Liyuan Yang Yueguo Hu Mengchun Pan Jiafei Hu Peisen Li College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Stomatology The Fourth Military Medical University Xian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the real-time processing requirements in edge computing devices, we construct in-sensor computing units based on magnetic tunnel junction (MTJ) arrays, which realizes in-situ computation of vector-matrix multiplication with parameters dynamically programmed to adapt to different tasks. Different from the previously used back-propagation (BP) algorithm, we innovatively introduce the forward-forward (FF) algorithm, which utilizes the positive and negative samples “Goodness” optimization mechanism to reduce the computational complexity. In the noisy image classification task, the FF algorithm reduces the training time by more than 90% compared to the BP algorithm and significantly improves the convergence speed when perfect classification results are achieved.

关键词： Training Heuristic algorithms Real-time systems Classification algorithms Computational efficiency Noise measurement Junctions Magnetic tunneling Image classification convergence

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SimRank on Data Streams

SimRank on Data Streams

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IEEE international conference on High Performance computing Workshops (HiPCW)

作者： Dhyan Yajnik Huzefa Aiyub Ansari Prajjwal Nijhara Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

This paper introduces a novel approach that integrates machine learning techniques to optimize SimRank convergence on streaming data. Our approach utilizes the structural properties of the graph, such as edge features, to train a linear regression model that predicts SimRank values for newly added nodes. These predictions serve as effective initializations, ensuring faster convergence of the SimRank algorithm. By avoiding full recomputation and providing a new direction for research, our method offers a promising solution for similarity computation in evolving networks.

关键词： Machine learning algorithms High performance computing conferences Computational modeling Linear regression Machine learning Predictive models Prediction algorithms Streams convergence

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

引用

40th IEEE international conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Offloading Utility Optimization in Parked Vehicular Edge computing

Offloading Utility Optimization in Parked Vehicular Edge Com...

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international conference on Information and Communication technology convergence (ICTC)

作者： Xuan-Qui Pham Dong-Seong Kim ICT Convergence Research Center Kumoh National Institute of Technology Korea

Parked vehicular edge computing (PVEC) has recently been introduced as a new paradigm that makes use of parked vehicles to support edge servers in computation offloading. However, most existing research does not consider the local computing capacity of requesting devices and/or the resource cost for offloading tasks. In this paper, we propose a partial computation offloading scheme that allows mobile devices (MDs) to utilize both local and remote resources for parallel task execution in PVEC. The goal is to maximize the total offloading utility of MDs, which is defined as the difference between the benefit of latency reduction and the cost of using computing and networking resources. Simulation results show that the proposed scheme can improve the total offloading utility compared to conventional schemes.

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Algorithm for EEG Signal Recognition Based on Unsupervised Pretraining and Transformer Attention Network 13

Algorithm for EEG Signal Recognition Based on Unsupervised P...

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13th international conference of Information and Communication technology, ICTech 2024

作者： Zhao, Kui Li, Zhen Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376258

This article proposes a Vision Transformer network based on pre-trained ResNet, a convolutional neural network, for automatic recognition of epilepsy EEG signals. An autoencoder model is constructed for unsupervised pre-training, obtaining weights on an unlabeled dataset. This approach differs from the lengthy and complex training of recurrent neural networks and multilayer perceptrons. ResNet, as a classic convolutional neural network, possesses strong feature learning capabilities for input data like images. Through unsupervised pre-training, ResNet can extract informative representations from large-scale unlabeled data, which helps improve the performance of subsequent classification tasks. EEG signals are time series data that require more modeling of temporal correlations compared to fixed-size inputs like images. EEG signals typically exhibit long-term temporal dependencies, and Transformers, through self-attention mechanisms, can capture long-range dependencies in time. Compared to recurrent neural networks and multilayer perceptrons, Transformer-based networks can better model global relationships in sequential data, thereby enhancing classification performance. Unsupervised pre-training eliminates the need for data annotation, greatly reducing manual labeling efforts. After the pre-training phase, the pretrained weights are saved as the initial weights for the subsequent network. A neural network based on Vision Transformer attention is constructed to enhance the network's feature extraction and learning capabilities. The pre-trained weights are iteratively fine-tuned, combining both steps. This approach accelerates the training and convergence speed of the Vision Transformer network, reduces computational costs, and improves the accuracy of the final classification results. Compared to other models such as LSTM, CNN, and SVM, various performance metrics show significant improvements. ©2024 IEEE.

关键词： Recurrent neural networks

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