Future Generation Computer Systems-The International Journal of Escience最新文献_第4页

Global reduction for geo-distributed MapReduce across cloud federation 跨云联盟的地理分布式 MapReduce 全局缩减

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107492

{"title":"Global reduction for geo-distributed MapReduce across cloud federation","authors":"","doi":"10.1016/j.future.2024.107492","DOIUrl":"10.1016/j.future.2024.107492","url":null,"abstract":"<div><p>Geo-distributed Bigdata processing is increasing day by day, resulting in the origins of data that are geographically distributed in different countries and hold datacenters (DCs) across the globe, and also the applications that use different sites to increase reliability, security, and processing performances. Most popular frameworks like Hadoop and Spark are re-designed to process geographically distributed data at their locations. However, these methods still suffer from a large amount of data transfer over the Internet, which prohibits a high processing time and cost for many applications, and in several cases, the output results of the computation are smaller than its inputs. In this paper, we keep the data locality principle for processing data at different locations but ignore the principle of transferring the entire intermediate results to a single global reducer. We propose Geo-MR, an intelligent geo-distributed MapReduce-based framework across federated cloud based on two heuristic algorithms: (i) chosen the best clusters as global reducers to reduce the communication and optimize the transfer on the bandwidth, GResearch. (ii) The second, Geo-MR, ensures the scheduling of only the relevant data to selected global reducers that process the final results. As a baseline, we propose an exact MapReduce scheduling model for benchmarking and to compare and discuss the Geo-MR heuristic algorithm results. The experimental results show that the proposed algorithm Geo-MR can improve resource (bandwidth and VMs of clusters) utilization of the cloud federation and consequently reduce cost and job response time.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence 战术 6G 网络的网络安全：威胁、架构和情报

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107500

{"title":"Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence","authors":"","doi":"10.1016/j.future.2024.107500","DOIUrl":"10.1016/j.future.2024.107500","url":null,"abstract":"<div><p>Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004643/pdfft?md5=fc9365c86570d800986a175631a6a13d&pid=1-s2.0-S0167739X24004643-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network 基于能量收集的线性无线传感器网络中的有限地平线能量分配方案

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107493

{"title":"Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network","authors":"","doi":"10.1016/j.future.2024.107493","DOIUrl":"10.1016/j.future.2024.107493","url":null,"abstract":"<div><p>Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

READ: Resource efficient authentication scheme for digital twin edge networks 阅读：数字孪生边缘网络的资源高效认证方案

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-26 DOI: 10.1016/j.future.2024.107498

{"title":"READ: Resource efficient authentication scheme for digital twin edge networks","authors":"","doi":"10.1016/j.future.2024.107498","DOIUrl":"10.1016/j.future.2024.107498","url":null,"abstract":"<div><p>In recent vigorous developments, digital twin edge networks (DITEN) have emerged as a network paradigm to improve network communication efficiency. Given that Web 3.0 technologies promise secure decentralized data storage and effective information exchange, it is feasible to construct a wireless edge intelligence-enabled Web 3.0 physical infrastructure through DITEN. However, DITEN encounters various security threats related to communication and authentication, and establishing a secure and cost-effective authentication scheme for confidential access to physical entities poses a significant challenge. To tackle this issue, in this article, we introduce READ, a provably secure multi-factor user authentication scheme tailored for DITEN in industrial applications. Using designed ASCON cryptography primitive cipher suite, physical unclonable functions, extended Chebyshev chaotic maps, one-way secure collision-resistant hash functions, and lightweight bitwise exclusive-or operations, READ enables mutual authentication and session key negotiation among mobile users, smart gateways, and smart industrial devices. Rigorous security assessments, conducted through the real-or-random (ROR) model, the automated validation of internet security-sensitive protocols and applications (AVISPA) simulation tool, and heuristic informal security analysis, confirm that READ meets all 13 security evaluation criteria. Furthermore, compared to other seven advanced multi-factor user authentication schemes, READ excels in security and efficiency, making it ideal for practical multi-factor user authentication scenarios.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small models, big impact: A review on the power of lightweight Federated Learning 小模型，大影响：评述轻量级联合学习的威力

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-23 DOI: 10.1016/j.future.2024.107484

引用次数: 0

SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning SFML：基于分裂学习和相互学习的个性化、高效和保护隐私的协作式流量分类架构

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-23 DOI: 10.1016/j.future.2024.107487

{"title":"SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning","authors":"","doi":"10.1016/j.future.2024.107487","DOIUrl":"10.1016/j.future.2024.107487","url":null,"abstract":"<div><p>Traffic classification is essential for network management and optimization, enhancing user experience, network performance, and security. However, evolving technologies and complex network environments pose challenges. Recently, researchers have turned to machine learning for traffic classification due to its ability to automatically extract and distinguish traffic features, outperforming traditional methods in handling complex patterns and environmental changes while maintaining high accuracy. Federated learning, a distributed learning approach, enables model training without revealing original data, making it appealing for traffic classification to safeguard user privacy and data security. However, applying it to this task poses two challenges. Firstly, common client devices like routers and switches have limited computing resources, which can hinder efficient training and increase time costs. Secondly, real-world applications often demand personalized models and tasks for clients, posing further complexities. To address these issues, we propose Split Federated Mutual Learning (SFML), an innovative federated learning architecture designed for traffic classification that combines split learning and mutual learning. In SFML, each client maintains two models: a privacy model for the local task and a public model for the global task. These two models learn from each other through knowledge distillation. Furthermore, by leveraging split learning, we offload most of the computational tasks to the server, significantly reducing the computational burden on the client. Experimental results demonstrate that SFML outperforms typical training architectures in terms of convergence speed, model performance, and privacy protection. Not only does SFML improve training efficiency, but it also satisfies the personalized needs of clients and reduces their computational workload and communication overhead, providing users with a superior network experience.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system LSTM-Oppurs：移动人群感知系统中基于深度学习的机会性用户招募策略

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107490

{"title":"LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system","authors":"","doi":"10.1016/j.future.2024.107490","DOIUrl":"10.1016/j.future.2024.107490","url":null,"abstract":"<div><p>As the scale of Mobile CrowdSensing (MCS) system expands, effective mobile user allocation and recruitment system design becomes crucial. Mobile users can be divided into opportunistic users and participatory ones. Most of the existing recruitment strategy have neglected some aspects, such as without considering the low-paying opportunistic users, without comprehensively considering users’ attributes and without considering their future location, etc. In this paper, the recruitment scheme is investigated by considering the opportunistic users. Firstly, the User Recruitment problem based on User Comprehensive Capabilities (urUCC) is proposed with the objective of maximizing the total revenue. In addition, this problem is proved to be NP-Hard. Secondly, the Opportunistic Users recruitment strategy based on Deep Learning (OUDL) is designed, which consists of three parts, the user location prediction algorithm based on the Long Short-Term Memory (LSTM), the user evaluation algorithm based on the topsis comprehensive evaluation method and the dynamic user recruitment algorithm. Finally, a large number of simulation experiments are conducted by using real datasets. It is proved that the strategy OUDL can recruit high-quality opportunistic users to participate in the sensing task while guaranteeing the task completion rate. Compared with other strategies, the task coverage of the strategy OUDL increased by more than 5% while the comprehensive quality of users increased by about 10%. Thus, the quality of data can be guaranteed while reducing the cost.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Concurrent vertical and horizontal federated learning with fuzzy cognitive maps 利用模糊认知地图同时进行纵向和横向联合学习

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107482

引用次数: 0

Drug repositioning by collaborative learning based on graph convolutional inductive network 通过基于图卷积归纳网络的协作学习重新定位药物

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107491

{"title":"Drug repositioning by collaborative learning based on graph convolutional inductive network","authors":"","doi":"10.1016/j.future.2024.107491","DOIUrl":"10.1016/j.future.2024.107491","url":null,"abstract":"<div><h3>Motivation:</h3><p>Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction.</p></div><div><h3>Results:</h3><p>To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at <span><span>https://github.com/BioMLab/DRCLN</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004473/pdfft?md5=4a8bc370da7848d41349297209e39e5e&pid=1-s2.0-S0167739X24004473-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud JCDC：基于区块链的联合云数据安全存储和流通框架

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-20 DOI: 10.1016/j.future.2024.107486

{"title":"JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud","authors":"","doi":"10.1016/j.future.2024.107486","DOIUrl":"10.1016/j.future.2024.107486","url":null,"abstract":"<div><p>JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0