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Grainbee: A Quantum-Resistant Blockchain-Based Ration Distribution System with Hardware Security Modules

Grainbee: A Quantum-Resistant Blockchain-Based Ration Distri...

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2024 IEEE international Conference on computing, applications and Systems, COMPAS 2024

作者： Joni, Sohel Ahmed Rahat, Rabiul Tasnin, Nishat Ghose, Partho Mahbub-Or-Rashid, Md. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In developing countries, subsidized food programs aimed at supporting the poor are often undermined by corruption and mismanagement, leading to food waste and unequal distribution. To overcome these challenges, we introduce GrainBee, a blockchain-powered ration distribution system designed to ensure secure, transparent, and efficient food supply distribution. By leveraging blockchain's inherent security and immutability, GrainBee addresses the major security and transparency issues that plague traditional and existing blockchain-based *** proposed system employs a post-quantum secure, blockchain-based approach to combat corruption and fraudulent invoicing, guaranteeing integrity through a distributed, immutable ledger. To further fortify security, we integrate Hardware Security Modules (HSMs), providing an additional layer of protection for sensitive data and transactions. A built-in reservation and scheduling system streamlines the distribution process, while a user-friendly interface and machine translation services bridge language gaps, enhancing usability and accessibility. By harnessing the power of blockchain technology, GrainBee offers a robust solution to the complex challenges of food distribution. © 2024 IEEE.

关键词： Blockchain

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A Decentralized Vehicle-to-Vehicle Energy Trading System Based on Efficient Sharding Services 21

A Decentralized Vehicle-to-Vehicle Energy Trading System Bas...

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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： Meng, Kun Sun, Lijun Chen, Xiao Wu, Haiqin Li, Shuaiyong Qingdao University of Science and Technology College of Information Science and Technology Shandong China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China

ISBN: (纸本)9798350329223

Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two substantial challenges require attention: 1) Creating a standardized protocol universally adoptable by EV manufacturers and energy trading participants to establish a common secure and reliable platform for real-time energy trading between EVs. 2) Determining a fair and transparent pricing and transaction management mechanism, accounting for factors such as energy demand and supply, location, and user preferences. To tackle these hurdles, this paper proposes a decentralized V2V energy trading system (VETS) based on a novel high-performance sharding blockchain framework. VETS incorporates a standard energy trading management protocol, achieving trustless and transparent transaction processing. It employs a blockchain sharding and parallel architecture for high transaction throughput, catering to a large number of EVs. Furthermore, an efficient consensus algorithm is developed to minimize service delays and adapt to real-time transaction demands. A practical testbed operationalizes VETS, facilitating experimental analysis within a real-world cloud environment. © 2023 IEEE.

关键词： Blockchain

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P-RECS '22: 5th international Workshop on Practical Reproducible Evaluation of Computer Systems 31

P-RECS '22: 5th International Workshop on Practical Reproduc...

引用

31st international symposium on High-Performance Parallel and distributed computing, HPDC 2022

作者： Liem, Radita Solorzano, Ana Veroneze Scully-Allison, Connor RWTH Aachen University Aachen Germany Northeastern University BostonMA United States University of Arizona TucsonAZ United States

ISBN: (纸本)9781450391993

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. The ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. The fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together. © 2022 Owner/Author.

关键词： Software engineering

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Partial Deep Domain Adaptation for Non-Intrusive Load Monitoring 21

Partial Deep Domain Adaptation for Non-Intrusive Load Monito...

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作者： Li, Yuying Yang, Yang Wang, Chen Liu, Runqin Jiang, Hongbo Liu, Wenping Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China Huazhong University of Science and Technology Wuhan China China Unicom Central South Research Institute Changsha China Hunan University Changsha China Hubei University of Economics Wuhan China

ISBN: (纸本)9798350329223

With the widespread deployment of smart meters and the burgeoning integration of the Ubiquitous Power Internet of Things (UPIoT), Non-Intrusive Load Monitoring (NILM) has emerged as an essential technology, providing granular insights into device-level energy consumption. However, NILM model generalization is impeded by user and appliance heterogeneity. Domain adaptation has been leveraged to enhance NILM models, but it typically necessitates consistent appliance categorization across households. Addressing this, our study presents Weighted Adaptation NILM (WeAd-NILM), a partial deep domain adaptation model designed for Partial Domain Adaptation (PDA) scenarios that accommodate diverse appliance categories, aligning with real-world load decomposition tasks. WeAd-NILM introduces a sample weighting mechanism tailored for PDA, mitigating negative transfer from outliers. Through distribution analysis and multi-objective regression, WeAd-NILM effectively performs load decomposition, fostering knowledge transfer. Verified on UK-DALE and REFIT datasets, WeAd-NILM demonstrates exceptional transferability and load decomposition accuracy. © 2023 IEEE.

关键词： Internet of things

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Cooperative Completing Tasks of Charging WRSNs with Multiple MUVs and Laser-charged UAVs 21

Cooperative Completing Tasks of Charging WRSNs with Multiple...

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作者： Luo, Chuanwen Liu, Ning Chen, Ting Cao, Jia Hong, Yi Chen, Zhibo Li, Deying Beijing Forestry University Information Science and Technology Beijing China University of Electronic Science and Technology of China Chengdu China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350329223

With the rapid development of unmanned aerial vehicles (UAVs), they can not only carry communication equipment for intercepting and interfering with signals to protect communication security, but also carry energy transmission devices to transfer energy to wireless sensor networks and enhance network lifespan. This paper investigates the network framework of multiple UAVs-based wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs). On the basis of this network, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the maximum time consumption of UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the MUTSP problem to balance the charging tasks assigned to each UAV. Then, according to the MUTSP problem, we propose the TOUM algorithm(TOUMA) to design the detailed travel plan of UAVs and MUVs. We have validated the effectiveness of our algorithm through extensive simulation experiments. The experimental results demonstrate that the TOUMA algorithm outperforms both the solar charging method and the base station charging method in terms of time efficiency. Furthermore, under various parameter settings, the ratio of the TOUMA algorithm consistently remains below 1.56. © 2023 IEEE.

关键词： Wireless sensor networks

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Adaboost-based Graph Convolutional Networks with Channel Attention for Intrusion Detection in Internet-of-Things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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作者： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of Things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional Networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. This leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural Networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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Continuous Complex Relational Community Detection on Multi-layer Networks 21

Continuous Complex Relational Community Detection on Multi-l...

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作者： Zong, Chuanyu Cai, Ziyi Li, Wenju Qiu, Tao Li, Jiajia Xia, Xiufeng Shenyang Aerospace University School of Computer Science Shenyang China Shenyang Aircraft Corporation Shenyang China

ISBN: (纸本)9798350329223

Detecting communities in multi-layer heterogeneous information networks (HINs) is an important problem in graph analysis such as social network analysis and genome element analysis. Relational community detection on single-layer networks cannot reflect the actual complex requirements of users who find the desired communities from HINs, the reason is that single-layer networks can only reflect the relational communities in the current context. To overcome the limitations of relational communities on single-layer networks, we propose the problem of Continuous Complex Relational Community Detection (CCRCD) on multi-layer networks, which aims to find a maximum complex relational community that satisfies relational and temporal constraints on multi-layer networks. CCRCD allows users to specify more fine-grained connection requirements by limiting the number of meta-paths between vertices in communities and the duration time of communities. In this case, the problem of detecting continuous complex relational communities is defined over multi-layer networks in this paper. To solve this problem, we explore two exact algorithms based on the idea of sliding window and multi-branch detection, respectively. We also propose a heuristic algorithm to solve the CCRCD problem. Finally, we conduct comprehensive experimental studies on four real HINs to demonstrate that our algorithms can effectively find continuous complex relational communities on multi-layer networks. © 2023 IEEE.

关键词： Heuristic algorithms

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A Write-Optimized PM-oriented B+-tree with Aligned Flush and Selective Migration 21

A Write-Optimized PM-oriented B+-tree with Aligned Flush and...

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作者： He, Xianyu Zhang, Runyu Tian, Pengpeng Zhou, Lening Lian, Min Yang, Chaoshu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350329223

Emerging Persistent Memories (PMs) usually have the severe drawback of expensive writes. Therefore, existing PM-oriented B+-trees mainly concentrate on alleviating the write overhead (i.e., reducing the writes to PM and the flush instructions). Unfortunately, these schemes still employ improper data organization in leaf nodes, which can lead to massive data migration during insertions or node splits. This paper proposes a write-optimized PM-oriented B+ -tree (WPB+ -tree) to solve the above problems. WPB+ -tree first adopts a buffer-assisted mechanism that temporarily stores the newly inserted data to reduce the overhead of data migration and flush. Second, WPB+ tree employs a selective migration of node entries to achieve less data migration when splitting a node. We implement the proposed WPB+ -tree with actual persistent memory. Compared with state-of-the-art solutions (i.e., FAST&FAIR and SSB-Tree), WPB+ -tree achieves up to 17%, 40%, and 20% higher throughput in the insert, search, and scan operations, respectively. © 2023 IEEE.

关键词： Cost reduction

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Parallel Assembly Sequence Planning Based on Sparrow Search Algorithms 22

Parallel Assembly Sequence Planning Based on Sparrow Search ...

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22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： He, Haichen Liu, Weibin Zhang, Yinan Song, Shasha Cheng, Yue Liu, Ziheng Xing, Weiwei Beijing Jiaotong University School of Computer Science and Technology China Crrc Information Technology Co. Ltd. China Beijing Jiaotong University School of Software Engineering China

ISBN: (纸本)9798331509712

Assembly sequence planning (ASP) is the most important process in the product lifecycle. This paper aims to propose a parallel assembly sequence planning method based on sparrow search algorithm (SSA) to improve the assembly quality and efficiency. The proposed method divides the problem into assembly unit division (AUD) part and ASP part. In AUD part, we use the markov clustering algorithm (MCA) to solve the AUD problem. In ASP part, the ASP evaluation system is designed for the assembly part sequence planning (APSP) problem and assembly unit sequence planning (AUSP) problem, and the discrete artificial sparrow search algorithm (DASSA) is proposed for solving the discrete APSP problem. The results of comparison experiment verify the effectiveness of the proposed method. Compared with genetic algorithm (GA) and particle swarm optimization (PSO), the proposed DASSA gets the optimal results and fastest convergence. The method proposed solves ASP problem to efficiently perform parallel assembly sequence planning and reduce production time, making it cost-effective in intelligent manufacturing. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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PFedSKD: Personalized Federated Learning via Self-Knowledge Distillation 22

PFedSKD: Personalized Federated Learning via Self-Knowledge ...

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22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Zheng, Meiguang Liu, Zhengjie Chen, Bo Hu, Zijie Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798331509712

In federated learning(FL), clients involved in training initialize their local models with the latest global model at the start of each communication round. This initialization discards previous local knowledge, leading to the loss of personalized insights. To tackle this issue, personalized federated learning (PFL) methods based on self-knowledge distillation aim to preserve effective personalized knowledge while reducing communication overhead and ensuring data privacy. However, current studies on knowledge distillation(KD) for PFL often focus solely on transferring label knowledge, neglecting other latent aspects. Meanwhile, introducing self-knowledge distillation into PFL may lead to unreliable teacher models, thereby misleading local training. To address these concerns, we propose PFedSKD, a novel self-knowledge distillation method for PFL. PFedSKD stratifies neural network intermediate layers based on their structural position and network characteristics. It further introduces three distinct auxiliary classifiers for these layers to effectively transfer their respective features. During the self-knowledge distillation training process, PFedSKD selectively discards negative personalized knowledge to mitigate issues with unreliable teacher models. Extensive experiments across multiple datasets and diverse heterogeneous settings demonstrate that PFedSKD achieves higher accuracy by 0.09%-7.7% and improved personalized learning capabilities compared to existing advanced FL methods. © 2024 IEEE.

关键词： Federated learning

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