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Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

computer Modeling in Engineering & Sciences 2024年第7期140卷 863-885页

作者： Wanbo Zhang Yuqi Fan Jun Zhang Xu Ding Jung Yoon Kim School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui ProvinceHefei University of TechnologyHefei230601China Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei University of TechnologyHefei230009China College of Future Industry Gachon UniversitySeongnam13120South Korea

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide *** blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the *** is,each edge server runs both users’offloaded tasks and blockchain tasks *** that there is a trade-off between the resource allocation for MEC and blockchain ***,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based *** of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC *** this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing *** the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the *** results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： Mobile edge computing blockchain resource allocation

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3D scene graph prediction from point clouds

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Virtual Reality & Intelligent Hardware 2022年第1期4卷 76-88页

作者： Fanfan WU Feihu YAN Weimin SHI Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background In this study,we propose a novel 3D scene graph prediction approach for scene understanding from point *** It can automatically organize the entities of a scene in a graph,where objects are nodes and their relationships are modeled as *** specifically,we employ the DGCNN to capture the features of objects and their relationships in the scene.A Graph Attention Network(GAT)is introduced to exploit latent features obtained from the initial estimation to further refine the object arrangement in the graph structure.A one loss function modified from cross entropy with a variable weight is proposed to solve the multi-category problem in the prediction of object and *** Experiments reveal that the proposed approach performs favorably against the state-of-the-art methods in terms of predicate classification and relationship prediction and achieves comparable performance on object classification *** The 3D scene graph prediction approach can form an abstract description of the scene space from point clouds.

关键词： Scene understanding 3D scene graph Point cloud DGCNN GAT

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Building an autonomous driving platform based on ROS and its safety analysis 9

Building an autonomous driving platform based on ROS and its...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wei, Zelin Chen, Qiwei Weng, Rui National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350391367

In the new wave of technological development, autonomous driving technologies have become the top priority of the innovation of future transportation. Although many technologies are involved in autonomous driving, it is an autonomous mobile robot in essence, mainly including three parts: perception, positioning, and decision-making. Ensuring its security is one key challenge to realize its widespread applications. Therefore, it is necessary to systematically consider security issues of autonomous driving from the sensor level, the autonomous driving system level, and the underlying communication protocol level. In this thesis, we built an automatic driving platform on the ROS system based on the existing hardware infrastructure - - SiLaR, a landrover of the Simplexity Lab. Furthermore, we analyzed security issues of each module of SiLaR. Especially in the process of map construction, due to the limitation of the working principle of LiDAR, some specular reflective materials in the operating environment will interfere with the construction. We also used the sensor cross fusion to calibrate the environmental information through the depth camera during the composition process to ensure the accuracy of map construction. © 2024 IEEE.

关键词： autonomous driving ROS security SLAM

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems 21

Transaction Data Management Optimization Based on Multi-Part...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： Information management

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A Space-Efficient Digital Wallet Service in Blockchain systems 21

A Space-Efficient Digital Wallet Service in Blockchain Syste...

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作者： Zhang, Jingyu Yang, Jin Wu, Chenghao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

As an indispensable key network service in blockchain systems, digital wallet service is crucial for promoting the widespread application of blockchain technology and the development of the digital economy. However, with the increasing popularity of blockchain technology, the scale of the Unspent Transaction Output (UTXO) dataset continues to increase. A significant number of low-value UTXOs occupied the main dataset space, leading to blockchain dataset expansion. This has affected the performance of digital wallet service and the overall system performance. To address this issue, this paper proposes a Space-Efficient Digital Wallet (SEDW) service in blockchain systems. This strategy employs a Multidimensional Space Simulated Annealing (MSA) algorithm to obtain transaction input UTXOs. MSA can be combined with the UTXO dynamic adjustment mechanism, achieving the rapid consumption of low-value UTXOs without increasing the transaction fees. Experimental results demonstrate that the proposed SEDW strategy effectively optimized the selection of UTXOs in blockchain systems. It alleviated the expansion issue of the UTXO dataset, and controlled the generation of transaction fees, thereby achieving performance enhancement of digital wallet service. © 2023 IEEE.

关键词： Blockchain

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A High-Performance Data Verification Mechanism in Blockchain-Based IoT systems 21

A High-Performance Data Verification Mechanism in Blockchain...

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作者： Zhang, Jingyu Zhou, Pian Sun, Yongtao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification. © 2023 IEEE.

关键词： Efficiency

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Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6752-6766页

作者： Xue, Xingsi Lin, Jerry Chun-Wei Su, Tong Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Silesian University of Technology Department of Distributed Systems and IT Devices Akademicka Gliwice44-100 Poland Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China

Ontology matching (OM) is critical for knowledge integration and system interoperability on the semantic web, tasked with identifying semantically related entities across different ontologies. Despite its importance, the complexity of terminology semantics and the large number of potential matches present significant challenges. Existing methods often struggle to balance between accurately capturing the multifaceted nature of semantic relationships and computational efficiency. This work introduces a novel approach, a compact multitasking multichromosome genetic algorithm for Heuristic selection (HS) in OM, designed to navigate the nuanced hierarchical structure of ontologies and diverse entity mapping preferences. Our method combines compact genetic algorithms with multichromosome optimization for entity sequencing and assigning HS, alongside an adaptive knowledge transfer mechanism to finely balance exploration and exploitation efforts. Evaluated on the ontology alignment evaluation initiative's benchmark, our algorithm demonstrates superior ability to produce high-quality ontology alignments efficiently, surpassing comparative methods in both effectiveness and efficiency. These findings underscore the potential of advanced genetic algorithms in enhancing OM processes, offering significant contributions to the broader AI field by improving the interoperability and knowledge integration capabilities of semantic web technologies. © 2024 IEEE.

关键词： Genetic algorithms

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DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations 24

DualFed: Enjoying both Generalization and Personalization in...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Guogang Liu, Xuefeng Niu, Jianwei Tang, Shaojie Wu, Xinghao Zhang, Jiayuan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States

ISBN: (纸本)9798400706868

In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs. To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://***/GuogangZhu/DualFed. © 2024 ACM.

关键词： Federated learning

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SvaVoting: A Novel Secret Sharing-Based Verifiable and Anonymous E-Voting Scheme in Blockchain systems 29

SvaVoting: A Novel Secret Sharing-Based Verifiable and Anony...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zhang, Jingyu Wu, Chenghao Yang, Jin Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350330717

E-Voting systems play a vital role in various communities, aiming to enhance the efficiency, transparency, and citizen engagement in elections. However, as technology advances, ensuring the security and reliability of e-voting systems becomes profound. While researchers have explored the integration of blockchain technology to improve e-voting systems, there still are technical challenges, particularly regarding voters' privacy, ballots' confidentiality, and system performance. To address these challenges, we propose SvaVoting, an innovative blockchain-based e-voting scheme. SvaVoting utilizes the Borda counting method and the identity-based ring signature based on symmetric primitives to anonymously verify the validity of the voter's identity and ballot format. In addition, we introduce secret sharing and a Cloud Service Provider (CSP) to conduct the final counting while preserving the privacy of the ballots. All users in the system can verify the results of CSP calculations, thereby meeting security goals such as correctness, unforgeability, anonymity and verifiability. The identity-based ring signature in our scheme provides 128-bit security, and even it can work in a quantum computing environment. Experimental results demonstrate that our solution significantly enhances the security and performance of e-voting systems, offering a practical path forward for future e-voting systems. © 2023 IEEE.

关键词： cloud service provider e-voting identity-based ring signatures post-quantum cryptography secret sharing

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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