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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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The Classification Method of EEG Motor Imagery Based on INFO-LSSVM 11

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

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Wang, Xinrong Belkacem, Abdelkader Nasreddine Li, Penghai Zhang, Zufeng Liang, Jun Du, Dongdong Chen, Chao Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology Uae University P.O. Box 15551 Al Ain United Arab Emirates Tianjin Medical University General Hospital Tianjin300052 China China Academy of Industrial Internet Beijing100102 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. © 2022 ACM.

关键词： Support vector machines

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Prediction Analysis for Diabetic Disease Using Machine Learning Model 22

Prediction Analysis for Diabetic Disease Using Machine Learn...

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22nd OITS International Conference on Information Technology, OCIT 2024

作者： Senthamarai, N. Masood, Jafar Ali Ibrahim Syed Sathya, M. Chakravarthy, N.S. Kalyan Rathore, Bharati Kalith Oli, Mohamed M. Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Llantwit Rd PontypriddCF37 1DL United Kingdom School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331510404

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. Data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy. © 2024 IEEE.

关键词： Random forests

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Fundamentals of Clustered Molecular Nanonetworks

arXiv

引用

arXiv 2022年

作者： Azimi-Abarghouyi, Seyed Mohammad Dhillon, Harpreet S. Tassiulas, Leandros The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States The School of Engineering & Applied Science The Yale Institute for Network Science Yale University New HavenCT United States

We present a comprehensive approach to the modeling, performance analysis, and design of clustered molecular nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed information to their respective fusion centers. The fusion centers decode this information by counting the number of molecules received in the given time slot. Owing to the propagation properties of the biological media, this setup suffers from both inter- and intra-cluster interference that needs to be carefully modeled. To facilitate rigorous analysis, we first develop a novel spatial model for this setup by modeling nanomachines as a Poisson cluster process with the fusion centers forming its parent point process. For this setup, we first derive a new set of distance distributions in the three-dimensional space, resulting in a remarkably simple result for the special case of the Thomas cluster process. Using this, total interference from previous symbols and different clusters is characterized and its expected value and Laplace transform are obtained. The error probability of a simple detector suitable for biological applications is analyzed, and approximate and upper-bound results are provided. The impact of different parameters on the performance is also investigated. © 2022, CC0.

关键词： Molecules

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AoI-Delay Tradeoff in Mobile Edge Caching: A Mixed-Order Drift-Plus-Penalty Algorithm

arXiv

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arXiv 2023年

作者： Li, Ran Huang, Chuan Qin, Xiaoqi Yang, Lei The School of Science and Engineering The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an O(1/V ) versus O(V ) tradeoff for the average AoI and average service delay. Copyright © 2023, The Authors. All rights reserved.

关键词： Quality of service

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A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation

A Hybrid Cross-Scale Transformer Architecture for Robust Med...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ning, Yang Zhang, Shouyi Zhong, Wei Liu, Peide Zhang, Caiming Shandong Jianzhu University School of Computer Science and Technology Jinan China The University of Edinburgh School of Informatics Edinburgh United Kingdom Guangdong Police College Department of Network Information Security Guangzhou China Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shandong University School of Software Jinan China

ISBN: (纸本)9781665468190

Transformer architecture has emerged to be successful in many natural language processing tasks. However, its applications to clinical practice remain largely unexplored. In this study, we propose a Robust Cross-Scale Hybrid Transformer (RCSHT) architecture for medical image segmentation, which can effectively enhance the multi-scale feature representations while integrating local features with global dependencies. Specifically, we propose two new modules based on the self-attention mechanism: PSCM and PCCM, which perform matrix low-rank transformation on cross-scale images in spatial and channel space, respectively, to effectively enhance the discriminant ability of long-range dependencies of multi-scale images and effectively reduce the computational complexity. Meanwhile, we use spatial positional encoding to perform spatial regularization on the two attention modules, so that the model has more opportunities to obtain valuable spatial details and further enhance the spatial retention ability. Besides, by fusing outputs of the two modules, the robustness and discriminant ability of the feature representations are further improved. Note that our hybrid architecture allows transformers to be initialized as convolutional networks without pre-training. Extensive experiments conducted on our newly proposed dataset have demonstrated our RCSHT notably outperforms the state-of-the-art methods by a large margin, holding the promise to generalize well on other medical image segmentation. © 2022 IEEE.

关键词： Signal encoding

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Based-on computer Vision Applications for Bus Stop Passenger Detection System

Based-on Computer Vision Applications for Bus Stop Passenger...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Yu-Cheng Chang Hua-Wen Tsai Chao-Yi Huang Zong-Rong Wu Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Taiwan

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the "Bus Stop Passenger Detection System". Taking the object detection of "Wheelchairs" into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient.

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Optimization of Image Transmission in Semantic Communication networks

Optimization of Image Transmission in Semantic Communication...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wenjing Zhang Yining Wang Mingzhe Chen Tao Luo Dusit Niyato Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and ComputingUniversity of Miami Coral Gables FL USA School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665435413

In this paper, a semantic communication framework for image transmission is investigated. In the framework, a server transmits image data to a set of users utilizing semantic communication techniques, which enable the server to transmit only the semantic information that accurately captures the meaning of an image. To evaluate the performance of the studied semantic communication system, we propose a multimodal metric called image-to-graph semantic similarity (ISS). The significance of this new metric is that it can measure the correlation of the meaning between semantic information and the original image. To meet the ISS requirement of each user, the server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem whose goal is to minimize the average transmission latency while reaching the ISS requirement. To solve this problem, we propose a model-based actor critic deep reinforcement learning (DRL) algorithm. Compared to traditional actor critic DRL, in the proposed algorithm, we design a novel value function to improve the action exploration thus improving the probability of finding an optimal solution. Simulation results show that the proposed method can reduce the transmission delay by 16.4% and improves the convergence speed by up to 50% compared to the traditional actor critic DRL.

关键词： Measurement Wireless communication Image communication Simulation Semantics Reinforcement learning Servers

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Stochastic-Robust Planning of networked Hydrogen-Electrical Microgrids: A Study on Induced Refueling Demand

arXiv

引用

arXiv 2024年

作者： Sun, Xunhang Cao, Xiaoyu Zeng, Bo Zhai, Qiaozhu Başar, Tamer Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Smart Integrated Energy Department Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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