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Cost-effective data classification storage through text seasonal features Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-05-06 Zhu Yuan, Xueqiang Lv, Yunchao Gong, Ping Xie, Taifu Yuan, Xindong You
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Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-05-01 Khanh Quan Pham, Taehong Kim
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Digital twin for credit card fraud detection: opportunities, challenges, and fraud detection advancements Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Pushpita Chatterjee, Debashis Das, Danda B. Rawat
Credit cards are widely used for payments due to their convenience and broad acceptance. Their popularity comes with the critical challenge of safeguarding personal and payment information from fraud and unauthorized access. Robust security measures are crucial to maintaining trust and confidence among users. In response to this pressing issue, this paper focuses on credit card fraud detection, its
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Advancements in remote sensing for invasive plant mapping along the Guadiana River: The role of CNN2D Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Elena C. Rodríguez-Garlito, Abel Paz-Gallardo, Antonio Plaza
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CPU–GPU heterogeneous code acceleration of a finite volume Computational Fluid Dynamics solver Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Weicheng Xue, Hongyu Wang, Christopher J. Roy
This research focuses on accelerating the finite-volume Computational Fluid Dynamics (CFD) solver, SENSEI, through concurrent CPU–GPU heterogeneous computing, leveraging multiple CPUs and GPUs. An overview of SENSEI, its discretization, and the heterogeneous computing workflow utilizing MPI and OpenACC are provided. A performance model for CPU–GPU heterogeneous computing, incorporating ghost cell exchange
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Local perturbation-based black-box federated learning attack for time series classification Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Shengbo Chen, Jidong Yuan, Zhihai Wang, Yongqi Sun
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End to End secure data exchange in value chains with dynamic policy updates Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Aintzane Mosteiro-Sanchez, Marc Barcelo, Jasone Astorga, Aitor Urbieta
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Parallel approaches for a decision tree-based explainability algorithm Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-30 Daniela Loreti, Giorgio Visani
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CoLLaRS : A cloud–edge–terminal collaborative lifelong learning framework for AIoT Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-29 Shijing Hu, Junxiong Lin, Zhihui Lu, Xin Du, Qiang Duan, Shih-Chia Huang
AIoT applications often encounter challenges such as terminal resource constraints, data drift, and data heterogeneity in real world, leading to problems such as catastrophic forgetting, low generalization ability, and low accuracy during model training. To address these challenges, we proposed CoLLaRS, a cloud–edge–terminal collaborative lifelong learning framework for AIoT applications. In the CoLLaRS
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Task graph offloading via deep reinforcement learning in mobile edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-29 Jiagang Liu, Yun Mi, Xinyu Zhang, Xiaocui Li
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Distributed realtime rendering in decentralized network for mobile web augmented reality Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-27 Huabing Zhang, Liang Li, Qiong Lu, Yi Yue, Yakun Huang, Schahram Dustdar
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Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-27 Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Kuan-Ching Li, Albert Y. Zomaya
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Communication efficient federated learning with data offloading in fog-based IoT environment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-27 Nidhi Kumari, Prasanta K. Jana
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Adaptive Scheduling of Continuous Operators for IoT Edge Analytics Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-27 Patient Ntumba, Nikolaos Georgantas, Vassilis Christophides
In this paper, we address the problem of adaptive scheduling of data stream processing and analytics (DSPA) applications in a shared edge fog cloud (EFC) continuum with response time constraints. The focus is on handling the dynamic workload of DSPA applications caused by the variability of their input data stream rates generated by mobile IoT devices, and the dynamically available resource capacity
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Towards machine-readable semantic-based E-business contract representations using Network of Timed Automata (NTA) Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-25 Peng Qin, Quanyi Hu, Menglin Cui
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Understanding insiders in cloud adopted organizations: A survey on taxonomies, incident analysis, defensive solutions, challenges Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-25 Asha S., Shanmugapriya D.
In cybersecurity, one of the most significant challenges is an insider threat, in which existing researchers must provide an extensive solution aiming at an enhanced security network. This study proposes a comprehensive taxonomy as well as a state-of-the-art research categorization according to the contribution of insider threat incidents and the defensive mechanism utilized against such insiders.
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Blockchain-based secure communication of internet of things in space–air–ground integrated network Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-25 Yi Zhang, Peiying Zhang, Mohsen Guizani, Jianyong Zhang, Jian Wang, Hailong Zhu, Kostromitin Konstantin Igorevich, Huiling Shi
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Estimation of realized volatility of cryptocurrencies using CEEMDAN-RF-LSTM Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-23 Huiqing Wang, Yongrong Huang, Zhide Chen, Xu Yang, Xun Yi, Hai Dong, Xuechao Yang
Predicting cryptocurrency volatility is crucial for investors, traders, and decision-makers but is complicated by the market’s high non-linearity, volatility, and noise. This paper presents a novel approach, the CEEMDAN-RF-LSTM hybrid model, which is the first to combine the strengths of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Random Forest (RF), and Long Short-Term
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CABC: A Cross-Domain Authentication Method Combining Blockchain with Certificateless Signature for IIoT Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-22 Libo Feng, Fei Qiu, Kai Hu, Bei Yu, Junyu Lin, Shaowen Yao
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Paving the way to hybrid quantum–classical scientific workflows Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-22 Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman
The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scientific workflows. As the need for computing power increases, quantum computing has been proposed as a way to deliver it. It may provide significant theoretical speedups for many scientific applications (i.e., molecular dynamics
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An efficient computation offloading in edge environment using genetic algorithm with directed search techniques for IoT applications Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Ezhilarasie Rajapackiyam, Anousouya Devi, Mandi Sushmanth Reddy, Umamakeswari Arumugam, Subramaniyaswamy Vairavasundaram, Indragandhi Vairavasundaram, Vishnu Suresh
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A lattice-based efficient certificateless public key encryption for big data security in clouds Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Juyan Li, Mingyan Yan, Jialiang Peng, Haodong Huang, Ahmed A. Abd El-Latif
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Low dimensional secure federated learning framework against poisoning attacks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Eda Sena Erdol, Beste Ustubioglu, Hakan Erdol, Guzin Ulutas
Federated learning (FL) is a type of distributed learning that can perform model training without exposing end users' data from end-user devices to increase security. Although it is one step ahead of other learning approaches thanks to this feature, studies have also proven that malicious users can reduce the success of the FL model. In this study, it is proven that the accuracy of the FL model is
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Multi-task learning for IoT traffic classification: A comparative analysis of deep autoencoders Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Huiyao Dong, Igor Kotenko
As a system allowing intra-network devices to automatically communicate over the Internet, the Internet of Things (IoT) faces increasing popularity in modern applications and security threats — particularly network intrusions that target both networks and devices. A major threat is network attacks that attempt to obtain unauthorised access and damage the networks or systems. To effectively safeguard
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ADS-CNN: Adaptive Dataflow Scheduling for lightweight CNN accelerator on FPGAs Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Yi Wan, Xianzhong Xie, Junfan Chen, Kunpeng Xie, Dezhi Yi, Ye Lu, Keke Gai
Lightweight convolutional neural networks (CNNs) enable lower inference latency and data traffic, facilitating deployment on resource-constrained edge devices such as field-programmable gate arrays (FPGAs). However, CNNs inference requires access to off-chip synchronous dynamic random-access memory (SDRAM), which significantly degrades inference speed and system power efficiency. In this paper, we
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Industrial federated learning algorithm (P-PFedSGD) for tool wear estimation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-19 Guan-Ying Huang, Ching-Hung Lee
In recent years, machine learning has been challenged by the growing concerns regarding data privacy. This has led to the emergence of federated learning, which aims to train a model across distributed clients without sharing their data, thereby resolving the data privacy issues. However, this scheme may not generalize well to the heterogeneous data of distributed clients, particularly in industrial
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Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-18 Shizhen Bai, Songlin Shi, Chunjia Han, Mu Yang, Brij B. Gupta, Varsha Arya
The advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining
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Enhancing security in e-business processes: Utilizing dynamic slicing of Colored Petri Nets for logical vulnerability detection Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-18 Wangyang Yu, Jie Feng, Lu Liu, Xiaojun Zhai, Yumeng Cheng
The field of e-business covers multiple aspects and has undergone rapid development, profoundly changing our transaction methods and shopping experiences. However, with the increasing complexity of its business processes, logical vulnerabilities have become an inevitable issue. These logical vulnerabilities can lead to a range of security problems, seriously threatening business stability and consumer
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Evolutionary multitasking for multiobjective optimization based on hybrid differential evolution and multiple search strategy Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-18 Ya-Lun Li, Yan-Yang Cheng, Zheng-Yi Chai, Xu Liu, Hao-Le Hou, Guoqiang Chen
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Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-17 Kan Wang, Peng Yuan, Mian Ahmad Jan, Fazlullah Khan, Thippa Reddy Gadekallu, Saru Kumari, Hao Pan, Lei Liu
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SkySwapping: Entanglement resupply by separating quantum swapping and photon exchange Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-17 Alin-Bogdan Popa, Bogdan-Călin Ciobanu, Voichiţa Iancu, Florin Pop, Pantelimon George Popescu
We propose a fast, satellite-based, on-demand entanglement resupply protocol which leverages entangled pairs exchanged in advance between satellites and ground stations. At the request time, the protocol does not require any quantum information exchange between ground and satellite, by performing the quantum entanglement swapping on satellite-level only, thus separating the particle exchange phase
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A Blockchain-based Digital Twin for IoT deployments in logistics and transportation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-17 Salvador Cuñat Negueroles, Raúl Reinosa Simón, Matilde Julián, Andreu Belsa, Ignacio Lacalle, Raúl S-Julián, Carlos E. Palau
Digital Twins are software technologies that enable the modelling of real-world phenomena in digitised environments, representing and monitoring the reality of various processes, including IoT deployments. Since 2017, the use of Digital Twins has been increasing. However, in the road transport and logistics realm, the adoption rate remains low, primarily due to the costs of processing and validating
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Region-based compressive distributed storage in Mobile CrowdSensing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Xingting Liu, Siwang Zhou, Jie Luo, Jianping Yu, Wei Zhang
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TMHD: Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Wei Liang, Jiahong Xiao, Yuxiang Chen, Chaoyi Yang, Kun Xie, Kuan-Ching Li, Beniamino Di Martino
As an efficient computing paradigm, Mobile Edge Computing (MEC) is essential in assisting mobile devices with real-time complex tasks such as big data analytics. In MEC, application tasks consist of multiple dependent subtasks, and the way to process tasks ensuring lower response latency through efficient scheduling orders is of relevant importance. Most existing research on scheduling sorts the dependent
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Sort-then-insert: A space efficient and oblivious model aggregation algorithm for top-k sparsification in federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Yongzhi Wang, Pengfei Gui, Mehdi Sookhak
Federated Learning (FL) allows multiple clients to collaboratively train machine learning models while preserving the model privacy of the clients. However, when generating a global model during the aggregation process, a malicious FL server could derive clients’ local model weights. Such a threat cannot be completely eliminated, even if model aggregation is performed in the Trusted Execution Environment
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A general framework and decentralised algorithms for collective computational processes Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Giorgio Audrito, Roberto Casadei, Gianluca Torta
Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is
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Enabling high fault-tolerant embedding capability of alternating group graphs Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Hongbin Zhuang, Xiao-Yan Li, Dajin Wang, Cheng-Kuan Lin, Kun Zhao
The Hamiltonian path/cycle serves as a robust tool for transmitting messages within parallel and distributed systems. However, the prevalent device-intensive nature of these systems often leads to the occurrence of faults. Tackling the critical challenge of tolerating numerous faults when constructing Hamiltonian paths and cycles in these systems is of utmost significance. The alternating group graph
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Enabling performance portability on the LiGen drug discovery pipeline Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Luigi Crisci, Lorenzo Carpentieri, Biagio Cosenza, Gianmarco Accordi, Davide Gadioli, Emanuele Vitali, Gianluca Palermo, Andrea Rosario Beccari
In recent years, there has been a growing interest in developing high-performance implementations of drug discovery processing software. To target modern GPU architectures, such applications are mostly written in proprietary languages such as CUDA or HIP. However, with the increasing heterogeneity of modern HPC systems and the availability of accelerators from multiple hardware vendors, it has become
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DID-HVC-based Web3 healthcare data security and privacy protection scheme Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Xiaoling Song, Guangxia Xu, Yongfei Huang, Jingnan Dong
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Blockchain-based cooperative game bilateral matching architecture for shared storage Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Guanjie Lin, Mingyuan Zeng, Zhiguang Shan, Kaishun Wu, Guan Wang, Kai Lei
The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered
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Online RL-based cloud autoscaling for scientific workflows: Evaluation of Q-Learning and SARSA Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Yisel Garí, Elina Pacini, Luciano Robino, Cristian Mateos, David A. Monge
Q-Learning and SARSA are two well-known reinforcement learning (RL) algorithms that have shown promising results in several application domains. However, their approach to build solutions is quite different. For example, SARSA tends to be more conservative than Q-Learning while exploring the solution space. Motivated by such differences, in this paper, we conducted an evaluation of both algorithms
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CANDIL: A federated data fabric for network analytics Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-13 Ignacio D. Martinez-Casanueva, Luis Bellido, Daniel González-Sánchez, Diego Lopez
The availability of data sources during the Big Data era provides the opportunity for new analytical applications in the networking domain, which are envisioned as one of the main enablers of the future autonomous networks. But the proliferation of heterogeneous data sources has resulted into a sea of data silos, in which finding data, understanding data, and dealing with the complexities of each data
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Enhancing federated learning robustness through randomization and mixture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-13 Seyedsina Nabavirazavi, Rahim Taheri, Sundararaja Sitharama Iyengar
Protecting data privacy is a significant challenge in machine learning (ML), and federated learning (FL) has emerged as a decentralized learning solution to address this issue. However, FL is vulnerable to poisoning attacks, which control and interrupt the learning process to substantially increase the error rate of the system. The aggregation algorithm’s robustness is crucial to prevent such attacks;
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Principled and automated system of systems composition using an ontological architecture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-10 Abdessalam Elhabbash, Yehia Elkhatib, Vatsala Nundloll, Vicent Sanz Marco, Gordon S. Blair
A distributed system’s functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity
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Quantum particle swarm optimization algorithm based on diversity migration strategy Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Chen Gong, Nanrun Zhou, Shuhua Xia, Shuiyuan Huang
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Pro-active component image placement in Edge computing environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Antonios Makris, Evangelos Psomakelis, Emanuele Carlini, Matteo Mordacchini, Theodoros Theodoropoulos, Patrizio Dazzi, Konstantinos Tserpes
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A survey on blockchain technology in the maritime industry: Challenges and future perspectives Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Mohamed Ben Farah, Yussuf Ahmed, Haithem Mahmoud, Syed Attique Shah, M. Omar Al-kadri, Sandy Taramonli, Xavier Bellekens, Raouf Abozariba, Moad Idrissi, Adel Aneiba
Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges
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Multi-objective optimization-based workflow scheduling for applications with data locality and deadline constraints in geo-distributed clouds Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Dongkuo Wu, Xingwei Wang, Xueyi Wang, Min Huang, Rongfei Zeng, Kaiqi Yang
Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow
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An assignment mechanism for workflow scheduling in Function as a Service edge environment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Samaneh Hajy Mahdizadeh, Saeid Abrishami
Serverless computing has revolutionized cloud-based software development for software developers, addressing many of the associated challenges. With resource management and infrastructure provisioning handled by the provider, developers can focus on deploying services at the application level, which has gained significant popularity. Edge computing, with its proximity to end-users and ability to offer
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A sustainable smart IoT-based solid waste management system Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Amira Henaien, Hadda Ben Elhadj, Lamia Chaari Fourati
In this paper, we present a sustainable Smart City Solid Waste Management System (SCSWMS) that integrates trending technologies such as Internet of Things (IoT), Low Power Wide Area Networks (LPWANs), and Intelligent Traffic Systems (ITS) to improve solid garbage management from its inception through disposal. The Proposed SCSWMS involves three main subsystems: Smart Garbage Bins (SGBs), Smart Garbage
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Enabling DevOps for Fog Applications in the Smart Manufacturing domain: A Model-Driven based Platform Engineering approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Julen Cuadra, Ekaitz Hurtado, Isabel Sarachaga, Elisabet Estévez, Oskar Casquero, Aintzane Armentia
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Towards energy and QoS aware dynamic VM consolidation in a multi-resource cloud Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Sounak Banerjee, Sarbani Roy, Sunirmal Khatua
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Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Satveer Singh, Eht E Sham, Deo Prakash Vidyarthi
Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability
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Mobility-aware personalized handover function provisioning system in B5G networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Haneul Ko, Yeunwoong Kyung, Jaewook Lee, Sangheon Pack, Namseok Ko
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Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Melis İlayda Bal, Hüseyin Aydın, Cem İyigün, Faruk Polat
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An automated framework for selectively tolerating SDC errors based on rigorous instruction-level vulnerability assessment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hussien Al-haj Ahmad, Yasser Sedaghat
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Faster or Cheaper: A Q-learning based cost-effective mixed cluster scaling method for achieving low tail latencies Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hao Yang, Li Pan, Shijun Liu
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Finding community structure in Bayesian networks by heuristic K-standard deviation method Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-04 Chenfeng Wang, Xiaoguang Gao, Xinyu Li, Bo Li, Kaifang Wan
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kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud–edge environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-04 Juan Marcelo Parra-Ullauri, Hari Madhukumar, Adrian-Cristian Nicolaescu, Xunzheng Zhang, Anderson Bravalheri, Rasheed Hussain, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou
Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given
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A new approach to Mergesort algorithm: Divide smart and conquer Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-03 Sahin Emrah Amrahov, Yilmaz Ar, Bulent Tugrul, Bekir Emirhan Akay, Nermin Kartli