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UAV-IRS-assisted energy harvesting for edge computing based on deep reinforcement learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-12 Shanchen Pang, Luqi Wang, Haiyuan Gui, Sibo Qiao, Xiao He, Zhiyuan Zhao
In the internet of everything (IoE) era, the proliferation of internet of things (IoT) devices is accelerating rapidly. Particularly, smaller devices are increasingly constrained by hardware limitations that impact their computational capacity, communication bandwidth, and battery longevity. Our research explores a multi-device, multi-access edge computing (MEC) environment within small cells to address
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Split ways: Using GAN watermarking for digital image protection with privacy-preserving split model training Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-12 Himanshu Kumar Singh, Kedar Nath Singh, Amit Kumar Singh
In recent years, the significant importance of digital data in the Industrial Internet of Things (IIoT) is receiving more and more attention, followed by more copyright violation challenges to the transmission and storage of sensitive data. To address this issue, we propose a generative adversarial network (GAN)-based image watermarking in privacy-preserving split model training. In the first stage
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Expanding SafeSU capabilities by leveraging security frameworks for contention monitoring in complex SoCs Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-11 Pablo Andreu, Sergi Alcaide, Pedro Lopez, Jaume Abella, Carles Hernandez
The increased performance requirements of applications running on safety-critical systems have led to the use of complex platforms with several CPUs, GPUs, and AI accelerators. However, higher platform and system complexity challenge performance verification and validation since timing interference across tasks occurs in unobvious ways, hence defeating attempts to optimize application consolidation
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Bodyless block propagation: TPS fully scalable blockchain with pre-validation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-07 Chonghe Zhao, Shengli Zhang, Taotao Wang, Soung Chang Liew
Despite numerous prior attempts to boost transaction per second (TPS) of blockchain system, most of them were at a price of degraded decentralization and security. In this paper, we propose a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during the block propagation process, to increase TPS without compromising security. Rather, the nodes in the blockchain
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Intelligent transportation system for automated medical services during pandemic Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-05 Amit Kumar Singh, Rajendra Pamula, Nasrin Akhter, Sudheer Kumar Battula, Ranesh Naha, Abdullahi Chowdhury, Shahriar Kaisar
Infectious viruses are spread during human-to-human contact and can cause worldwide pandemics. We have witnessed worldwide disasters during the COVID-19 pandemic because of infectious viruses, and these incidents often unfold in various phases and waves. During this pandemic, so many deaths have occurred worldwide that they cannot even be counted accurately. The biggest issue that comes to the forefront
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Olsync: Object-level tiering and coordination in tiered storage systems based on software-defined network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-04 Zhike Li, Yong Wang, Shiqiang Nie, Jinyu Wang, Chi Zhang, Fangxing Yu, Zhankun Zhang, Song Liu, Weiguo Wu
With the adoption of new storage technologies like NVMs, tiered storage has gained popularity in large-scale, hyper-converged clusters. The storage back-end of hyper-converged systems supports data storage on devices such as SSDs and HDDs, yet lacks fine-grained tiered storage solutions. For example, Ceph selects storage nodes based primarily on limited criteria, such as node storage capacity, disregarding
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Joint energy efficiency and network optimization for integrated blockchain-SDN-based internet of things networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-04 Akram Hakiri, Bassem Sellami, Sadok Ben Yahia
The Internet of Things (IoT) networks are poised to play a critical role in providing ultra-low latency and high bandwidth communications in various real-world IoT scenarios. Assuring end-to-end secure, energy-aware, reliable, real-time IoT communication is hard due to the heterogeneity and transient behavior of IoT networks. Additionally, the lack of integrated approaches to efficiently schedule IoT
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A Digital Twin-based multi-objective optimized task offloading and scheduling scheme for vehicular edge networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-04 Lin Zhu, Bingxian Li, Long Tan
Traditional research on vehicular edge computing often assumes that the requested and processed task types are the same or that the edge servers have identical computing resources, ignoring the heterogeneity of task types in mobile vehicles and the services provided by edge servers. Meanwhile, the complexity of the vehicular edge environment and the large amount of real-time data required by DRL are
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Trajectory privacy preservation model based on LSTM-DCGAN Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-09-01 Jiajia Hu, Jingsha He, Nafei Zhu, Lu Qu
Rapid scientific and technological development has brought many innovations to electronic devices, which has greatly improved our daily lives. Nowadays, many apps require the permission to access user location information, causing the concern on user privacy and making it an important task to protect user trajectory information. This paper proposes a novel model called LSTM-DCGAN by integrating LSTM
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Software stewardship and advancement of a high-performance computing scientific application: QMCPACK Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-31 William F. Godoy, Steven E. Hahn, Michael M. Walsh, Philip W. Fackler, Jaron T. Krogel, Peter W. Doak, Paul R.C. Kent, Alfredo A. Correa, Ye Luo, Mark Dewing
We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental
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Two-stage multi-objective optimization based on knowledge-driven approach: A case study on production and transportation integration Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-31 Ziqi Ding, Zuocheng Li, Bin Qian, Rong Hu, Rongjuan Luo, Ling Wang
The multi-objective evolutionary algorithm (MOEA) has been widely applied to solve various optimization problems. Existing search models based on dominance and decomposition are extensively used in MOEAs to balance convergence and diversity during the search process. In this paper, we propose for the first time a two-stage MOEA based on a knowledge-driven approach (TMOK). The first stage aims to find
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Special Collection on Advances in Quantum Computing: Methods, Algorithms, and Systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-30 Stefano Markidis, Michela Taufer, Lucio Grandinetti
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Big Data-driven MLOps workflow for annual high-resolution land cover classification models Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-28 Antonio M. Burgueño-Romero, Cristóbal Barba-González, José F. Aldana-Montes
Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently
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HashGrid: An optimized architecture for accelerating graph computing on FPGAs Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-28 Amin Sahebi, Marco Procaccini, Roberto Giorgi
Large-scale graph processing poses challenges due to its size and irregular memory access patterns, causing performance degradation in common architectures, such as CPUs and GPUs. Recent research includes accelerating graph processing using Field Programmable Gate Arrays (FPGAs). FPGAs can provide very efficient acceleration thanks to reconfigurable on-chip resources. Although limited, these resources
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CSMD: Container state management for deployment in cloud data centers Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-28 Shubha Brata Nath, Sourav Kanti Addya, Sandip Chakraborty, Soumya K. Ghosh
As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance
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A multi-level IIOT platform for boosting mines digitalization Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-27 Raúl Miñón, Juan López-de-Armentia, Lander Bonilla, Aitor Brazaola, Ibai Laña, M. Carmen Palacios, Szymon Mueller, Michal Blaszczak, Herwig Zeiner, Julia Tschuden, Mohammad Yusuf Quadri, Ignasi Garcia-Milà, Andrea Bartoli, Norbert Gormolla, Alberto Fernández, Pablo Segarra, José A. Sanchidrián, Philipp Hartlieb
This paper presents an innovative IIoT multi-level platform tailored to address the specific needs of the mining domain. The platform has been conceptualized and built in the context of the illuMINEation European project. For this purpose, mining specific use cases have been designed such as promoting underground safe areas, performing efficient mining operations or boosting predictive maintenance
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READ: Resource efficient authentication scheme for digital twin edge networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-26 Kai Wang, Jiankuo Dong, Yijie Xu, Xinyi Ji, Letian Sha, Fu Xiao
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
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Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-26 Jani Suomalainen, Ijaz Ahmad, Annette Shajan, Tapio Savunen
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
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Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-26 Shengbo Chen, Shuai Li, Guanghui Wang, Keping Yu
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
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Global reduction for geo-distributed MapReduce across cloud federation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-26 Thouraya Gouasmi, Ahmed Hadj Kacem
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
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SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-23 Jiaqi Xia, Meng Wu, Pengyong Li
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
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Small models, big impact: A review on the power of lightweight Federated Learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-23 Pian Qi, Diletta Chiaro, Francesco Piccialli
Federated Learning (FL) enhances Artificial Intelligence (AI) applications by enabling individual devices to collaboratively learn shared models without uploading local data with third parties, thereby preserving privacy. However, implementing FL in real-world scenarios presents numerous challenges, especially with IoT devices with limited memory, diverse communication conditions, and varying computational
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Drug repositioning by collaborative learning based on graph convolutional inductive network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-22 Zhixia Teng, Yongliang Li, Zhen Tian, Yingjian Liang, Guohua Wang
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
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LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-22 Jing Zhang, Ding He, Xueqi Chen, Xiangxuan Zhong, Peiwei Tsai
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
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Concurrent vertical and horizontal federated learning with fuzzy cognitive maps Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-22 Jose L. Salmeron, Irina Arévalo
Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model without compromising the privacy of their data
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JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-20 Kaimin Zhang, Xingwei Wang, Lin Qiu, Enliang lv, Jingjing Guo, Bo Yi
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
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Load-aware switch migration for controller load balancing in edge–cloud architectures Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-19 Yong Liu, Qian Meng, Kefei Chen, Zhonghua Shen
As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load
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Enhancing IoT device security in Kubernetes: An approach adopted for network policies and the SARIK framework Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-17 Jonathan G.P. dos Santos, Geraldo P. Rocha Filho, Rodolfo I. Meneguette, Rodrigo Bonacin, Gustavo Pessin, Vinícius P. Gonçalves
The Internet of Things (IoT) has ushered in an era of connected devices that, while facilitating real-time data collection and sharing, also exposes these devices to significant security risks. This study addresses the challenges of security risks and vulnerabilities by employing the Network Policy in Kubernetes and focusing on the SARIK framework. SARIK is designed to automate the creation and implementation
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IDAD: An improved tensor train based distributed DDoS attack detection framework and its application in complex networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-16 Qiyuan Fan, Xue Li, Puming Wang, Xin Jin, Shaowen Yao, Shengfa Miao, Min An, Yuqing Zhao
With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency
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Program context-assisted address translation for high-capacity SSDs Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-14 Xiaochang Li, Minjae Kim, Sungjin Lee, Zhengjun Zhai, Jihong Kim
As the capacity of NAND flash-based SSDs keeps increasing, it becomes crucial to design a memory-efficient address translation algorithm that offers high performance when a translation table cannot be entirely loaded in a controller DRAM. Existing flash translation layers (FTL) employ demand-based address translation which caches popular mapping information in DRAM by leveraging locality of I/O references
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Underwater Mediterranean image analysis based on the compute continuum paradigm Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-12 Michele Ferrari, Daniele D’Agostino, Jacopo Aguzzi, Simone Marini
Human activity depends on the oceans for food, transportation, leisure, and many more purposes. Oceans cover 70% of the Earth’s surface, but most of them are unknown to humankind. This is the reason why underwater imaging is a valuable resource asset to Marine Science. Images are acquired with observing systems, e.g. autonomous underwater vehicles or underwater observatories, that presently transmit
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Quantum resource estimation for large scale quantum algorithms Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-12 Vlad Gheorghiu, Michele Mosca
Quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial number of logical qubits, and the inherent susceptibility to errors of quantum computers require quantum error correction. The integration of error correction introduces
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Certificateless Proxy Re-encryption with Cryptographic Reverse Firewalls for Secure Cloud Data Sharing Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-10 Nabeil Eltayieb, Rashad Elhabob, Abdeldime M.S. Abdelgader, Yongjian Liao, Fagen Li, Shijie Zhou
Cloud computing has enabled data-sharing to be more convenient than ever before. However, data security is a major concern that prevents cloud computing from being widely adopted. A potential solution to secure data-sharing in cloud computing is proxy re-encryption (PRE), which allows a proxy to transform encrypted data from one key to another without accessing the plaintext. When using PRE, various
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Network-aware federated neural architecture search Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-08 Göktuğ Öcal, Atay Özgövde
The cooperation between Deep Learning (DL) and edge devices has further advanced technological developments, allowing smart devices to serve as both data sources and endpoints for DL-powered applications. However, the success of DL relies on optimal Deep Neural Network (DNN) architectures, and manually developing such systems requires extensive expertise and time. Neural Architecture Search (NAS) has
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Context aware clustering and meta-heuristic resource allocation for NB-IoT D2D devices in smart healthcare applications Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-06 Nahar Sultana, Farhana Huq, Palash Roy, Md. Abdur Razzaque, Md. Mustafizur Rahman, Taiyeba Akter, Mohammad Mehedi Hassan
The utilization of Device-to-Device (D2D) communication among Narrowband Internet of Things (NB-IoT) devices offers significant potential for advancing intelligent healthcare systems due to its superior data rates, low power consumption, and spectral efficiency. In D2D communication, strategies to mitigate interference and ensure coexistence with cellular networks are crucial. These strategies are
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Decentralised Identity Management solution for zero-trust multi-domain Computing Continuum frameworks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-06 José Manuel Bernabé Murcia, Eduardo Cánovas, Jesús García-Rodríguez, Alejandro M. Zarca, Antonio Skarmeta
The adoption of the Computing Continuum is characterised by the seamless integration of diverse computing environments and devices. In this dynamic landscape, sharing resources across the continuum is becoming a reality and security must move an step forward, specially in terms of authentication and authorisation for such a distributed and heterogeneous environments. The need for robust identity management
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15+ years of joint parallel application performance analysis/tools training with Scalasca/Score-P and Paraver/Extrae toolsets Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-02 Brian J.N. Wylie, Judit Giménez, Christian Feld, Markus Geimer, Germán Llort, Sandra Mendez, Estanislao Mercadal, Anke Visser, Marta García-Gasulla
The diverse landscape of distributed heterogeneous computer systems currently available and being created to address computational challenges with the highest performance requirements presents daunting complexity for application developers. They must effectively decompose and distribute their application functionality and data, efficiently orchestrating the associated communication and synchronisation
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A cross-modal high-resolution image generation approach based on cloud-terminal collaboration for low-altitude intelligent network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-08-02 Minghai Jiao, Wenyan Jiang, Tianshuo Yuan, Jing Wang, Yuhuai Peng
The advancement of digitization and automation in Low Altitude Intelligent Networking (LAIN) is constrained by limited computational resources and the absence of a dedicated modal transformation mechanism, affecting the performance of latency-sensitive missions. This study addresses these challenges by proposing a Downscaling Reconstruction Multi-scale Locally Focused Generative Adversarial Network
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A study on characterizing energy, latency and security for Intrusion Detection Systems on heterogeneous embedded platforms Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Camélia Slimani, Louis Morge-Rollet, Laurent Lemarchand, David Espes, Frédéric Le Roy, Jalil Boukhobza
Drone swarms are increasingly being used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for this purpose. Optimizing the execution of resource-hungry IDS algorithms on resource-constrained drone devices, in terms of energy consumption
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Multi-objective federated learning: Balancing global performance and individual fairness Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Yuhao Shen, Wei Xi, Yunyun Cai, Yuwei Fan, He Yang, Jizhong Zhao
In federated learning, non-iid data not only diminishes the performance of the global model but also gives rise to the fairness problem which manifests as an increase in the variance of the global model’s accuracy across clients. Fairness issues can result in the global model performing poorly or even failing on certain clients. Existing methods addressing the fairness problem in federated learning
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Deadline-constrained security-aware workflow scheduling in hybrid cloud architecture Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen
A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary
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Harnessing federated learning for anomaly detection in supercomputer nodes Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Emmen Farooq, Michela Milano, Andrea Borghesi
High-performance computing (HPC) systems are a crucial component of modern society, with a significant impact in areas ranging from economics to scientific research, thanks to their unrivaled computational capabilities. For this reason, the worldwide HPC installation is steeply trending upwards, with no sign of slowing down. However, these machines are both complex, comprising millions of heterogeneous
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PoAh 2.0: AI-empowered dynamic authentication based adaptive blockchain consensus for IoMT-edge workflow Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Joy Dutta, Deepak Puthal
This paper introduces a significant advancement in the Proof of Authentication (PoAh) consensus algorithm, designed specifically for resource-constrained Internet of Things (IoT) devices. Building upon the foundations of PoAh consensus, this enhanced iteration, known as PoAh 2.0, integrates Artificial Intelligence (AI) at the block creator node level. This novel approach allows for the generation of
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Speeding up the communications on a cluster using MPI by means of Software Defined Networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Pablo Gomariz-Martínez, Francisco M. Delicado Martínez, Enrique Arias-Antúnez
The Open MPI library is widely employed for implementing the message-passing programming model on parallel applications running on distributed memory computer systems, such as large data centers. These applications aim to utilize the highest amount of resources required by High Performance Computing (HPC). The interconnection network is an essential part of the HPC environment, as processes on parallel
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Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-31 Xiongtao Zhang, Ji Wang, Weidong Bao, Wenhua Xiao, Yaohong Zhang, Lihua Liu
The past years have witnessed the success of a distributed learning system called Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in concurrency compared to mainstream synchronous FL. However, the inherent systematic and statistical heterogeneity has presented several impediments to AFL: On the client side, the discrepancies in trips and local model drift impede
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Serverless computing in the cloud-to-edge continuum Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-29 Carlo Puliafito, Omer Rana, Luiz F. Bittencourt, Hao Wu
Serverless computing is establishing itself as a way to efficiently run cloud applications while abstracting the underlying infrastructure complexity away from application developers. At the same time, edge computing provides cloud-like facilities toward the edge of the network, in closer proximity to client devices. Integration of serverless and edge computing is promising. However, many research
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ORR-CP-ABE: A secure and efficient outsourced attribute-based encryption scheme with decryption results reuse Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-27 Yu Tao, Yi Zhu, Chunpeng Ge, Lu Zhou, Shouchen Zhou, Yongjing Zhang, Jiarong Liu, Liming Fang
Attribute-based encryption that supports computation outsourcing has been a promising approach to implement fine-grained access control in the Internet of Things (IoT). However, existing outsourcing schemes only focus on minimizing computation on user terminals while overlooking the significant computational burden faced by outsourcing devices. In IoT scenarios with a large volume of concurrent outsourcing
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Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-26 Rui She, Yuting Wu, Enfang Cui, Mengyu Sun, Wei Zhao, Deji Fu
Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters
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VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-26 Sudip Chandra Ghoshal, Md Maruf Hossain, Bishozit Chandra Das, Palash Roy, Md. Abdur Razzaque, Saiful Azad, Mohammad Mehedi Hassan, Claudio Savaglio, Giancarlo Fortino
Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy
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Digital Twin and federated learning enabled cyberthreat detection system for IoT networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-25 Mikail Mohammed Salim, David Camacho, Jong Hyuk Park
The widespread deployment of Internet of Things (IoT) devices across various smart city applications presents significant security challenges, increased by the rapidly evolving landscape of cyber threats. Traditional security solutions, including those using Federated Learning with federated averaging, suffer from inefficiencies due to random node selection and partial data sampling, which can hinder
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Scalability through Pulverisation: Declarative deployment reconfiguration at runtime Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-25 Nicolas Farabegoli, Danilo Pianini, Roberto Casadei, Mirko Viroli
In recent years, the infrastructure supporting the execution of situated distributed computations evolved at a fast pace. Modern collective adaptive applications – as found in the Internet of Things, swarm robotics, and social computing – are designed to be executed on very diverse devices and to be deployed on infrastructures composed of devices ranging from cloud servers to wearable devices, constituting
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Accelerating Maximal Bicliques Enumeration with GPU on large scale network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-24 Chunqi Wu, Jingdong Li, Zhao Li, Ji Zhang, Pan Tang
Bicliques, as a prevalent graph pattern, are of particular interest in graph mining and social network analysis, especially for detecting illegal activities on e-commerce platforms due to their dense structure. Overcoming the challenge of efficiently identifying all bicliques in large-scale graphs, called the Maximal Biclique Enumeration (MBE) problem, demands considerable computational power and remains
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DIDS: A distributed inference framework with dynamic scheduling capability Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Yuwei Yan, Yikun Hu, Qinyun Cai, WangDong Yang, Kenli Li
Distributed inference is a promising solution for deploying Deep Neural Network (DNN) applications in resource-constrained edge environments. However, due to the complexity and variability of edge scenarios, efficiently completing DNN inference remains challenging. While previous works have made significant progress in various aspects such as partition strategy, device diversity, and memory overhead
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IoT-driven blockchain to manage the healthcare supply chain and protect medical records Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Alessandra Rizzardi, Sabrina Sicari, Jesus F. Cevallos M., Alberto Coen-Porisini
Healthcare supply chain domain and medical records’ management face numerous challenges that come with new demands, such as customer dissatisfaction, rising healthcare costs, tracking and traceability of drugs, and security and privacy related to the sensitive information managed in such a domain. Executing processes related to healthcare domain in a trusted, secure, efficient, accessible and traceable
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M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Phasit Charoenkwan, Nalini Schaduangrat, Le Thi Phan, Balachandran Manavalan, Watshara Shoombuatong
A wide variety of chemicals cannot be introduced to the marketplace because of their high allergenicity. Therefore, it is fundamentally crucial to assess the allergenic potential of chemicals before introducing them into clinical therapeutics. However, assessing the allergenicity of chemical compounds experimentally is time-consuming and costly. To tackle this challenge, we propose M3S-ALG, a novel
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Privacy-preserving edge federated learning for intelligent mobile-health systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Amin Aminifar, Matin Shokri, Amir Aminifar
Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning
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Extreme-scale workflows: A perspective from the JLESC international community Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Orcun Yildiz, Amal Gueroudji, Julien Bigot, Bruno Raffin, Rosa M. Badia, Tom Peterka
The Joint Laboratory for Extreme-Scale Computing (JLESC) focuses on software challenges in high-performance computing systems to meet the needs of today’s science campaigns, which often require large resources, consist of multiple tasks, and generate vast amounts of data. In this context, extreme-scale workflows have been the key factor in enabling scientific discoveries by helping scientists automate
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MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Xinyu Tian, Mahbuba Afrin, Sajib Mistry, Redowan Mahmud, Aneesh Krishna, Yan Li
In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks
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MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-23 Wang Zhao, Dongxiao Gu, Xuejie Yang, Meihuizi Jia, Changyong Liang, Xiaoyu Wang, Oleg Zolotarev
Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task
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A prototype-assisted clustered federated learning for big data security and privacy preservation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-20 Yalan Jiang, Dan Wang, Bin Song, Xiaojiang Du
In the rapidly expanding field of IoT, data production has reached an unprecedented scale, providing valuable insights that accelerate decision-making processes. However, ensuring the privacy and security of this massive amount of data poses significant challenges. In this paper, we propose using clustered federated learning (CFL) as a solution to ensure both the security and privacy of big data by