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Online Learning in Blockchain-based Energy Trading systems 24

Online Learning in Blockchain-based Energy Trading Systems

2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Pioneering Deterministic Scheduling and network Structure Optimization for Time-Critical Computing Tasks in Industrial IoT

arXiv

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arXiv 2024年

作者： Hu, Yujiao Zhu, Yining Zhang, Huayu Pan, Yan Jia, Qingmin Xie, Renchao Yang, Gang Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi’an710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results. © 2024, CC BY-NC-ND.

关键词： Internet of things

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Privacy-Preserving Computation Tookit on Floating-Point Numbers 14th

Privacy-Preserving Computation Tookit on Floating-Point Numb...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Chen, Zekai Zheng, Zhiwei Liu, Ximeng Guo, Wenzhong College of of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783030898137

Computation outsourcing using virtual environment is getting more and more prevalent in cloud computing, which several parties want to run a joint application and preserves the privacy of input data in secure computation protocols. However, it is still a challenging task to improve the efficiency and speed of secure floating point calculations in computation outsourcing, which has efficient secure integer calculations. Therefore, in this paper, we propose a framework built-up with a privacy-preserving computation toolkit with floating-point numbers (FPN), called PCTF. To achieve the above goal, we provide efficient toolkit to ensure their own data that FPN operations can be securely handled by homomorphic encryption algorithm. Moreover, we provide simulation results to experimentally evaluate the performance of the accuracy and the efficiency of PCTF, which will slowdown with 10x time consumption per in the secure floating-point addition and secure floating-point multiplication. Existing FPN division is constantly approaching result of division, or obtaining the quotient and remainder of division, in terms of precision, it is impossible to guarantee the precise range stably. However, our PCTF has higher precision in secure floating-point division, and the precision can be guaranteed at least 10- 17. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Outsourcing

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Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 27

Self-temperature compensation approach for fiber specklegram...

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27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

We proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis, and designed a fiber specklegram magnetic field sensor with high stability and good r... 详细信息

ISBN: (纸本)9781957171142

关键词： Polarization

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CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

arXiv

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arXiv 2024年

作者： Hu, Yujiao Jia, Qingmin Chen, Jinchao Yao, Yuan Pan, Yan Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi'An710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling. © 2024, CC BY-NC-ND.

关键词： Edge computing

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Privacy-Preserving Property Prediction for New Drugs with MPNN 14th

Privacy-Preserving Property Prediction for New Drugs with MP...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Xue, Jiaye Liao, Xinying Liu, Ximeng Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783030898137

Message passing neural network (MPNN) is one of the excellent deep learning models for drug discovery and development, drug laboratory usually outsource the MPNN model to cloud servers to save the research and development cost. However, drug-related data privacy has become a noticeable hindrance for outsourcing cooperation in drug discovery. In this paper, we propose a lightweight privacy-preserving message passing neural network framework (SecMPNN) for property prediction in new drugs. To implement SecMPNN, we design multiple protocols to perform the three stages of MPNN, namely message function, update function, and readout function. The above new-designed secure protocols enable SecMPNN to adapt to the different numbers of participating servers and different lengths of encryption requirements. Moreover, the accuracy, efficiency, and security of SecMPNN are demonstrated through comprehensive theoretical analysis and a large number of experiments. The experimental results show the communication efficiency in multiplication and comparison increases 27.78% and 58.75%, the computation error decreases to 4.64%. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Message passing

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Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 8

Self-temperature compensation approach for fiber specklegram...

引用

8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

In this letter, we proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis. The temperature compensation is achieved by comparing the variation difference of the two specklegrams with vertical linear polarization state. We designed a fiber specklegram magnetic field sensor employing this method and the magneto-birefringence effect of magnetic fluid. The measurement of magnetic field with high stability and good repeatability has been successfully achieved, which demonstrated the effectiveness of the proposed method. Due to the merits of low cost, ease of implement and system simplicity, the proposed method can promote the practical use of the FSS in long term monitoring. © 2022 SPIE

关键词： Polarization

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SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

arXiv

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arXiv 2024年

作者： Xu, Rongtao Wang, Changwei Zhang, Jiguang Xu, Shibiao Meng, Weiliang Zhang, Xiaopeng The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CASIA Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer’s global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future. Our code is available at https://***/RongtaoXu/SkinFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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New Cryptographic Bent Functions from Permutations and Linear Translator in Cyber Security

New Cryptographic Bent Functions from Permutations and Linea...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Qian Liu Yan Chen Zhixiong Chen Ximeng Liu Hui Lin College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Financial Information Processing Putian University Putian China Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

Cryptographic bent functions are maximally nonlinear Boolean functions with an even number of variables. They are not only closely related to some interesting combinatorial objects, but also have important applications in coding, sequence design, cryptography and cyber security. In this paper, we firstly investigate the Walsh transform of two families of functions via Maiorana-MacFarland's class. Secondly, we present new infinite families of permutations. We show that those families have nice property that one can select three elements among them which can be used to construct bent functions. Finally, by using two linear translators, we construct bent functions from the class of Maiorana-MacFarland.

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