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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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YM-NET: A New Network Structure for License Plate Detection in Complex Scenarios 2020

YM-NET: A New Network Structure for License Plate Detection ...

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2020 International Conference on Aviation Safety and Information Technology, ICASIT 2020

作者： Shu, Changsi Xu, Ke Sun, Tanfeng Jiang, Xinghao Wang, Renfang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9781450375764

Although a large number of current deep learning methods for license plates detection, most of them mainly focus on frontal images. The study of license plate detection focused on the images in complex scenarios is rare, and most of them didn't work well. In this study, we design a novel neural network architecture named YM-NET which is used for license plate detection in complex scenarios. YM-NET consists of a feature extraction network based on linear bottleneck and an improved multiscale detection network for reuse of deep features. Our experiment results indicate that the designed deep learning network achieved detection accuracy comparable to other deep learning methods in the frontal license plate images datasets, and much better in the complex scenarios such as tilt or blur. © 2020 ACM.

关键词： Feature extraction

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Deep Reinforcement Learning for Dynamic Demand in Shared Bicycle Scheduling Problem

Deep Reinforcement Learning for Dynamic Demand in Shared Bic...

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International Conference on big data and Information Analytics (bigDIA)

作者： Zixiao Jiao Wenhua Xiao Hailu Xin Daqian Liu Hui Yan Chao Chen Laboratory for Big Data and Decision National University of Defense Technology Changsha China Academy of Military Sciences Beijing China College of Systems Engineering National University of Defense Technology Changsha China Software College Liaoning Technical University Liaoning China School of Traffic and Transportation Engineering Central South University Changsha China

In contemporary shared bicycle scheduling systems, a common occurrence is a severe imbalance between regions, with one area suffering from a dearth of bicycles while another area is burdened with an excess. The task of balancing the distribution of vehicles can be abstracted as The Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPSPDTW). The primary objective is to efficiently schedule vehicles between multiple sites to minimize the overall transportation cost. However, in practical scheduling scenarios, the demand at each site undergoes dynamic fluctuations over time, thereby presenting a formidable challenge for problem-solving. Hence, this study employs a deep reinforcement learning algorithm equipped with a pointer network, constructing a computational environment capable of accommodating the dynamic nature of demand and lifting the constraints on the number of scheduled vehicles. Furthermore, the integration of a node masking mechanism enables the acquisition of high-quality scheduling strategies for addressing the shared bicycle scheduling predicament. Two distinct scheduling strategies are proposed, one prioritizing cost optimization and the other emphasizing user demand fulfillment. Empirical findings substantiate that the proposed model surpasses other conventional algorithms in terms of performance efficacy and computational efficiency.

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Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors

Predicting Fall Events by a Spatio-Temporal Topological Netw...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaohu Li Jiawei Liu Guorui Liao Mingrui Yin Shu Wang Guoxin Su Jun Liao Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Aerospace Rocket Electronic Technology Co. Ltd Chongqing China College of Computer Science & Technology Zhejiang University Zhejiang China School of Materials & Energy Southwest University Chongqing China School of Computing and Information Technology University of Wollongong NSW Australia

A key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to capture human low limbs information, and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.

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Towards Dynamic and Safe Configuration Tuning for Cloud databases

arXiv

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arXiv 2022年

作者： Zhang, Xinyi Wu, Hong Li, Yang Tan, Jian Li, Feifei Cui, Bin School of CS Key Laboratory of High Confidence Software Technologies Peking University China Database and Storage Laboratory Damo Academy Alibaba Group China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China China

Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation. We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4%∼165.3% improvement on cumulative performance while reducing 91.0%∼99.5% unsafe configuration recommendations. Copyright © 2022, The Authors. All rights reserved.

关键词： database systems

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation

arXiv

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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution

arXiv

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arXiv 2024年

作者： Zou, Yang Chen, Zhixin Zhang, Zhipeng Li, Xingyuan Ma, Long Liu, Jinyuan Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710129 China Graduate School of Information Production and Systems Waseda University Fukuoka8080135 Japan School of Software Dalian University of Technology Dalian116024 China School of Mechanical Engineering Dalian University of Technology Dalian116024 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://***/hey-it-s-me/*** Codes 68T45 Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

引用

arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Anchor-Based Discriminative Dual Distribution Calibration for Transductive Zero-Shot Learning

SSRN

引用

SSRN 2023年

作者： Zhang, Yi Huang, Sheng Yang, Wanli Tang, Wenhao Zhang, Xiaohong Yang, Dan School of Big Data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Chongqing Shapingba401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Chongqing Shapingba400044 China

Zero-shot learning (ZSL) is machine learning task to recognize samples from classes that are not observed during training. Transductive ZSL (TZSL) is a more realistic and effective paradigm that leverages unlabeled unseen data during training to reduce the bias towards seen classes. However, most existing TZSL methods neglect the information gap between visual and semantic spaces, and thus fail to generate distribution-consistent unseen features. To address this issue, we propose a novel TZSL approach named Anchorbased Discriminative Dual Distribution Calibrated Feature Generative Network (AD3C-FGN), which performs anchor-based distribution calibration in both visual and semantic spaces for improving generalization ability of the model. In AD3C-FGN, we adopt conditional generative adversarial network with an unseen discriminator to construct a Y-shape generation model that mitigates the domain shift problem. Moreover, an AD3C module is elaborated for calibrating the distribution of generated and real samples in both visual and semantic spaces with real sample anchors, and also enhance the discriminability of the generated samples. AD3C enforces the generated sample to be closer to its homogenous anchor but farther away from inhomogeneous anchors in both spaces. Extensive experimental results on six popular ZSL benchmarks demonstrate that our method achieves promising performances in different settings. © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems

Decentralized Linear MMSE Equalizer Under Colored Noise for ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaotong Zhao Xin Guan Mian Li Qingjiang Shi School of Software Engineering Tongji University Shanghai China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781728181059

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antenna increases, centralized baseband processing architectures encounter two bot-tlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear mini-mum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.

关键词： Baseband Equalizers Computer architecture Massive MIMO Mean square error methods Interference Large-scale systems

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