Cluster Computing最新文献_第8页

Connecting the dots between stance and fake news detection with blockchain, proof of reputation, and the Hoeffding bound 用区块链、声誉证明和霍夫丁约束连接立场与假新闻检测之间的联系

Cluster Computing Pub Date : 2024-06-27 DOI: 10.1007/s10586-024-04637-7

Ilhem Salah, Khaled Jouini, Cyril-Alexandre Pachon, Ouajdi Korbaa

{"title":"Connecting the dots between stance and fake news detection with blockchain, proof of reputation, and the Hoeffding bound","authors":"Ilhem Salah, Khaled Jouini, Cyril-Alexandre Pachon, Ouajdi Korbaa","doi":"10.1007/s10586-024-04637-7","DOIUrl":"https://doi.org/10.1007/s10586-024-04637-7","url":null,"abstract":"Combating fake news is a crucial endeavor, yet the complexity of the task requires multifaceted approaches that transcend singular technological solutions. Traditional fact-checking, often centralized and human-dependent, faces scalability and bias challenges. This paper introduces a novel blockchain-based framework that leverages the wisdom of the crowd for an authority-free, scalable, automated and reputation-driven fact-checking. Within this framework, stance detection acts as an automated means of opinion retrieval, while the Proof of Reputation consensus mechanism fosters an environment where reputable contributors have greater influence in shaping news credibility. Concurrently, the Hoeffding bound is used to allow the system to adapt to evolving contexts. In contrast to Machine Learning—based approaches, our framework limits the need for periodic retraining to update a model’s frozen knowledge of the world. The experimental study conducted on real-world data demonstrates that the proposed framework offers a promising and efficient solution to combat the spread of fake news.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environment RAPTS：异构雾计算环境中的资源感知优先任务调度技术

Cluster Computing Pub Date : 2024-06-26 DOI: 10.1007/s10586-024-04612-2

Mazhar Hussain, Said Nabi, Mushtaq Hussain

{"title":"RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environment","authors":"Mazhar Hussain, Said Nabi, Mushtaq Hussain","doi":"10.1007/s10586-024-04612-2","DOIUrl":"https://doi.org/10.1007/s10586-024-04612-2","url":null,"abstract":"The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel real-time object detection method for complex road scenes based on YOLOv7-tiny 基于 YOLOv7-tiny 的新型复杂道路场景实时物体检测方法

Cluster Computing Pub Date : 2024-06-26 DOI: 10.1007/s10586-024-04595-0

Yunfa Li, Hui Li

{"title":"A novel real-time object detection method for complex road scenes based on YOLOv7-tiny","authors":"Yunfa Li, Hui Li","doi":"10.1007/s10586-024-04595-0","DOIUrl":"https://doi.org/10.1007/s10586-024-04595-0","url":null,"abstract":"Road object detection is a key technology in intelligent transportation systems, playing a crucial role in ensuring driving safety and enhancing driving experience. However, due to factors such as weather and visual occlusions, particularly in complex traffic scenes, the recognition rate and accuracy of object detection are often less than satisfactory, far from meeting the application demands of intelligent driving. In order to address the issues of weak generalization and low regression accuracy of image similarity evaluation metrics, we propose a new anchor box calculation algorithm. Building upon this, to tackle the problem of weak graphic attention and feature capture capabilities in the backbone network,We propose an improved CA attention mechanism. In addition, to address the issues of low detection accuracy and imprecise positioning of the model in complex traffic scenarios, we propose a new image enhancement module. we select the road traffic dataset BDD(Berkeley Deep Drive)100K as the benchmark evaluation dataset and divide the training and validation sets into six new categories. Through this series of strategies, a new real-time road object detection method suitable for complex traffic scenes is formed. To validate the effectiveness of this method, we conducted a series of experiments. The experimental results demonstrate that our proposed method achieves a mean average precision improvement of 3.61% compared to the YOLOv7-tiny method.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A revolutionary approach to use convolutional spiking neural networks for robust intrusion detection 利用卷积尖峰神经网络进行鲁棒入侵检测的革命性方法

Cluster Computing Pub Date : 2024-06-26 DOI: 10.1007/s10586-024-04603-3

Yongxing Lin, Xiaoyan Xu, Hongyun Xu

{"title":"A revolutionary approach to use convolutional spiking neural networks for robust intrusion detection","authors":"Yongxing Lin, Xiaoyan Xu, Hongyun Xu","doi":"10.1007/s10586-024-04603-3","DOIUrl":"https://doi.org/10.1007/s10586-024-04603-3","url":null,"abstract":"In an era dominated by network connectivity, the reliance on robust and secure networks has become paramount. With the advent of 5G and the Internet of Things, networks are expanding in both scale and complexity, rendering them susceptible to a myriad of cyber threats. This escalating risk encompasses potential breaches of user privacy, unauthorized access to transmitted data, and targeted attacks on the underlying network infrastructure. To safeguard the integrity and security of modern networked societies, the deployment of Network Intrusion Detection Systems is imperative. This paper presents a novel lightweight detection model that seamlessly integrates Spiking Neural Networks and Convolutional Neural Networks with advanced algorithmic frameworks. Leveraging this hybrid approach, the proposed model achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. This paper presents a new style recognition model that seamlessly integrates spiking neural networks and convolutional neural networks with advanced algorithmic frameworks. We call this combined method Spiking-HCCN. Using this hybrid approach, Spiking-HCCN achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. Comparative evaluations against state-of-the-art models, including Spiking GCN and Spike-DHS, demonstrate significant performance advantages. Spiking-HCCN outperforms these benchmarks by 24% in detection accuracy, 21% in delay, and 29% in energy efficiency, underscoring its efficacy in fortifying network security in the face of evolving cyber threats.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HOGWO: a fog inspired optimized load balancing approach using hybridized grey wolf algorithm HOGWO：使用混合灰狼算法的雾启发优化负载平衡方法

Cluster Computing Pub Date : 2024-06-25 DOI: 10.1007/s10586-024-04625-x

Debashreet Das, Sayak Sengupta, Shashank Mouli Satapathy, Deepanshu Saini

{"title":"HOGWO: a fog inspired optimized load balancing approach using hybridized grey wolf algorithm","authors":"Debashreet Das, Sayak Sengupta, Shashank Mouli Satapathy, Deepanshu Saini","doi":"10.1007/s10586-024-04625-x","DOIUrl":"https://doi.org/10.1007/s10586-024-04625-x","url":null,"abstract":"A distributed archetype, the concept of fog computing relocates the storage, computation, and services closer to the network’s edge, where the data is generated. Despite these advantages, the users expect proper load management in the fog environment. This has expanded the Internet of Things (IoT) field, increasing user requests for the fog computing layer. Given the growth, Virtual Machines (VMs) in the fog layer become overburdened due to user demands. In the fog layer, it is essential to evenly and fairly distribute the workload among the segment’s current VMs. Numerous load-management strategies for fog environments have been implemented up to this point. This study aims to create a hybridized and optimized approach for load management (HOGWO), in which the population set is generated using the Invasive Weed Optimisation (IWO) algorithm. The rest of the functional part is done with the help of the Grey Wolf Optimization (GWO) algorithm. This process ensures cost optimization, increased performance, scalability, and adaptability to any domain, such as healthcare, vehicular traffic management, etc. Also, the efficiency of the enhanced approach is analyzed in various scenarios to provide a more optimal solution set. The proposed approach is well illustrated and outperforms the existing algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc., in terms of cost and load management. It was found that more than 97% jobs were completed on time, according to the testing data, and the hybrid technique outperformed all other approaches in terms of fluctuation of load and makespan.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems 捕鱼优化算法：解决聚类问题的新型人类行为算法

Cluster Computing Pub Date : 2024-06-25 DOI: 10.1007/s10586-024-04618-w

Heming Jia, Qixian Wen, Yuhao Wang, Seyedali Mirjalili

{"title":"Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems","authors":"Heming Jia, Qixian Wen, Yuhao Wang, Seyedali Mirjalili","doi":"10.1007/s10586-024-04618-w","DOIUrl":"https://doi.org/10.1007/s10586-024-04618-w","url":null,"abstract":"This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA’s overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction 具有注意力机制的自适应时空图卷积网络用于移动边缘网络流量预测

Cluster Computing Pub Date : 2024-06-25 DOI: 10.1007/s10586-024-04577-2

Ning Sha, Xiaochun Wu, Jinpeng Wen, Jinglei Li, Chuanhuang Li

{"title":"Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction","authors":"Ning Sha, Xiaochun Wu, Jinpeng Wen, Jinglei Li, Chuanhuang Li","doi":"10.1007/s10586-024-04577-2","DOIUrl":"https://doi.org/10.1007/s10586-024-04577-2","url":null,"abstract":"In the current era of mobile edge networks, a significant challenge lies in overcoming the limitations posed by limited edge storage and computational resources. To address these issues, accurate network traffic prediction has emerged as a promising solution. However, due to the intricate spatial and temporal dependencies inherent in mobile edge network traffic, the prediction task remains highly challenging. Recent spatio-temporal neural network algorithms based on graph convolution have shown promising results, but they often rely on pre-defined graph structures or learned parameters. This approach neglects the dynamic nature of short-term relationships, leading to limitations in prediction accuracy. To address these limitations, we introduce Ada-ASTGCN, an innovative attention-based adaptive spatio-temporal graph convolutional network. Ada-ASTGCN dynamically derives an optimal graph structure, considering both the long-term stability and short-term bursty evolution. This allows for more precise spatio-temporal network traffic prediction. In addition, we employ an alternative training approach during optimization, replacing the traditional end-to-end training method. This alternative training approach better guides the learning direction of the model, leading to improved prediction performance. To validate the effectiveness of Ada-ASTGCN, we conducted extensive traffic prediction experiments on real-world datasets. The results demonstrate the superior performance of Ada-ASTGCN compared to existing methods, highlighting its ability to accurately predict network traffic in mobile edge networks.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An authentication mechanism based on blockchain for IoT environment 基于区块链的物联网环境认证机制

Cluster Computing Pub Date : 2024-06-24 DOI: 10.1007/s10586-024-04565-6

Gholam Reza Zargar, Hamid Barati, Ali Barati

引用次数: 0

Unmanned aerial vehicle assisted communication: applications, challenges, and future outlook 无人飞行器辅助通信：应用、挑战和未来展望

Cluster Computing Pub Date : 2024-06-23 DOI: 10.1007/s10586-024-04631-z

Yilin Li, Yanxian Bi, Jian Wang, Zhiqiang Li, Hongxia Zhang, Peiying Zhang

引用次数: 0

A hybrid northern goshawk optimization algorithm based on cluster collaboration 基于集群协作的混合北方大鹰优化算法

Cluster Computing Pub Date : 2024-06-23 DOI: 10.1007/s10586-024-04571-8

Changjun Wu, Qingzhen Li, Qiaohua Wang, Huanlong Zhang, Xiaohui Song

{"title":"A hybrid northern goshawk optimization algorithm based on cluster collaboration","authors":"Changjun Wu, Qingzhen Li, Qiaohua Wang, Huanlong Zhang, Xiaohui Song","doi":"10.1007/s10586-024-04571-8","DOIUrl":"https://doi.org/10.1007/s10586-024-04571-8","url":null,"abstract":"To address the problems that the northern goshawk optimization algorithm (NGO) has a slow convergence speed and is highly susceptible to fall into local optimal solutions, this paper proposes a hybrid northern goshawk optimization algorithm based on cluster collaboration (HHNGO), which effectively improves the convergence speed and alleviates the problem of falling into the local optimum. Firstly, piecewise chaotic mapping is used to initialize the population, which makes the initial population more evenly distributed in the search space and improves the quality of the initial solution. Secondly, the prey recognition position update formula in the harris hawk optimization algorithm is introduced to improve the exploration phase. Meanwhile, a nonlinear factor can be added to accelerate the process which reaches the minimum difference between the prey best position and the average position of the eagle group. Thus the iteration number is reduced during the search process, and the convergence speed of the algorithm is improved. Finally, the Cauchy variation strategy is used to perturb the optimal solution of the algorithm. Then, its probability jumping out of the local optimal solution is increased, and the global search capability is enhanced. The experimental comparison is carried out to analyze the 12 standard functions, CEC-2019 and CEC-2021 test functions in HHNGO and PSO, GWO, POA, HHO, NGO, INGO, DFPSO, MGLMRFO, GMPBSA algorithms, and HHNGO is applied in PID parameter rectification. The results prove the feasibility and superiority of the proposed method.","PeriodicalId":501576,"journal":{"name":"Cluster Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0