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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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Exploring the Dynamics of Dietary Self-Monitoring Adherence Among Participants in a Digital Behavioral Weight Loss Program: Model Development Study

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Journal of Medical Internet Research 2025年 27卷

作者： Lin, Hui Yang, Min Zhou, Zhiheng Zhang, Yu Deng, Ning Pingshan Hospital Southern Medical University Shenzhen China Pingshan District Peoples' Hospital of Shenzhen Shenzhen China School of Public Health Zhejiang University School of Medicine Hangzhou China Center of Clinical Big Data and Analytics The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China School of Biomedical Engineering Southern Medical University Guangzhou China Ministry of Education Key Laboratory of Biomedical Engineering College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Background: Self-monitoring of dietary behaviors is typically a central component of behavioral weight loss programs, and it is widely recognized for its effectiveness in promoting healthy behavior changes and improving health outcomes. However, understanding the adherence dynamics of self-monitoring of dietary behaviors remains a challenge. Objective: We aimed to develop a prognostic model for adherence to self-monitoring of dietary behaviors using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture and to qualitatively investigate adherence dynamics and the impact of various interventions through model-based analyses. Methods: The modeling data were derived from a digital behavioral weight loss program targeting adults who expressed a willingness to improve their lifestyle. Participants were assigned to 1 of 3 intervention groups: self-management, tailored feedback, and intensive support. ACT-R, a cognitive architecture simulating human cognitive processes, was used to model adherence to self-monitoring of dietary behaviors over 21 days, focusing on the mechanisms of goal pursuit and habit formation. Predictor and outcome variables were defined as adjacent elements in the sequence of self-monitoring of dietary behaviors. Model performance was evaluated using mean square error, root mean square error (RMSE), and goodness of fit. Mechanistic contributions were visualized to analyze adherence patterns and the impacts of different interventions. Results: The total sample size for modeling was 97, with 49 in the self-management group, 23 in the tailored feedback group, and 25 in the intensive support group. The ACT-R model effectively captured the adherence trends of self-monitoring of dietary behaviors, with RMSE values of 0.099 for the self-management group, 0.084 for the tailored feedback group, and 0.091 for the intensive support group. The visualized results revealed that, across all groups, the goal pursuit mechanism remained dominant througho

关键词： ACT-R architecture adherence dynamics computational behavioral science digital health interventions goal pursuit habit formation self-monitoring of dietary behavior tailored feedback interventions weight loss program

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Prediction of Estimated Time of Arrival for Multi-Airport Systems Via "Bubble" Mechanism

SSRN

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SSRN 2022年

作者： Wang, Lechen Mao, Jianfeng Li, Lishuai Li, Xuechun Tu, Yilei School of Data Science The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Guangdong China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Faculty of Aerospace Engineering Delft University of Technology Delft2600 AA Netherlands School of Data Science City University of Hong Kong Hong Kong

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel Bubble mechanism to accurately and e ciently predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin-destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a ight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long-Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in China, Guangdong-Hong Kong-Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Toward PTNET Network Topology Analysis and Routing Algorithm Design 6th

Toward PTNET Network Topology Analysis and Routing Algorithm...

引用

6th International Conference on data Science, ICDS 2019

作者： Han, Zhijie Zhang, Qingfang Du, Xiaoyu Guo, Kun He, Mingshu Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China School of Computer and Information Engineering Henan University KaifengHenan475004 China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811528095

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm. © Springer Nature Singapore Pte Ltd 2020.

关键词： Network topology

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A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

SSRN

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SSRN 2024年

作者： Chen, Huangxin Dong, Piaopiao Wang, Dong Wang, Xiao-Ping School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong 518172 China

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM. © 2024, The Authors. All rights reserved.

关键词： Convolution

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GF-2 Image Blue Roof Building Extraction Method Based on Object-Oriented Classification Technology 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhang, Dezheng Yin, Xinyu Xie, Yonghong He, Zheyu Wang, Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

Remote sensing image classification method is the focus of current academic field. Most studies use complex machine learning and deep learning methods to extract ground object, but lack simple and effective methods to extract ground object with distinctive features. In this paper, using rule-based object-oriented classification technology, through remote sensing image preprocessing, multi-scale segmentation, extraction rule formulation and other steps, using object-oriented classification method, taking Jinfeng District, Xixia District and Xingqing District of Yinchuan City as an example, The blue roof buildings in the range were extracted, and the results showed that the method can effectively extract the target features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Roofs

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition

引用

Frontiers of Information Technology & Electronic engineering 2023年第2期24卷 299-313页

作者： Chunxi LI Yingying FU Xiangke CUI Quanbo GE Logistics Engineering College Shanghai Maritime UniversityShanghai200135China School of Economics and Management Beijing Jiaotong UniversityBeijing102603China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT) Nanjing University of Information Science and TechnologyNanjing210044China

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety *** is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from *** to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low *** response to the above problems,data drivers and machine learning theories are *** the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is ***,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging ***,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking ***,the data run by the actual engineering system are verified for the proposed charging time prediction *** results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.

关键词： Charging mode Charging time Random forest Long short-term memory(LSTM) Simplified particle swarm optimization(SPSO)

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Chain-Based Outlier Detection for Complex data Scenarios

Chain-Based Outlier Detection for Complex Data Scenarios

引用

IEEE International Conference on Big data

作者： Huiwen Dong Qing-Guo Wang Wei Ding Institute of Artificial Intelligence and Future Networks Beijing Normal University Beijing China Guangdong Key Lab of AI and MM Data Processing Guangdong Provincial Key Laboratory IRADS IAS DST BNU-HKBU United International College Institute for Advanced Study Zhuhai China Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu China

Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.

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