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Real-time unsupervised video object detection on the edge Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-06 Paula Ruiz-Barroso, Francisco M. Castro, Nicolás Guil
Object detection in video is an essential computer vision task. Consequently, many efforts have been devoted to developing precise and fast deep-learning models for this task. These models are commonly deployed on discrete and powerful GPU devices to meet both frame rate performance and detection accuracy requirements. Furthermore, model training is usually performed in a strongly supervised way so
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A quantization-based technique for privacy preserving distributed learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-04 Maurizio Colombo, Rasool Asal, Ernesto Damiani, Lamees M. AlQassem, Al Anoud Almemari, Yousof Alhammadi
The distributed training of machine learning (ML) models presents significant challenges in ensuring data and parameter protection. Privacy-enhancing technologies (PETs) offer a promising initial step towards addressing these concerns, yet achieving confidentiality and differential privacy in distributed learning remains complex. This paper introduces a novel data protection technique tailored for
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Separation and optimization of encryption and erasure coding in decentralized storage systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-04 Marcell Szabó, Ákos Recse, Róbert Szabó, Dávid Balla, Markosz Maliosz
Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service
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Review on LoRa backscatter technology Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-03 Siaka Konaté, Changli Li, Lizhong Xu
In recent years, LoRa backscatter has been seen as a promising technology to enable long-range communication among low-power IoT devices. Several designs and potential applications of LoRa backscatter have been proposed in the literature. This paper aims to provide a fundamental background for general readers to understand the basic concepts, operation methods, and mechanisms and discusses future potential
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UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-02 Ming Zheng, Fei Wang, Xiaowen Hu, Liangchen Hu, Qingying Yu, Xiaoyao Zheng
Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem
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Edge-cloud solutions for big data analysis and distributed machine learning - 2 Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-01 Loris Belcastro, Jesus Carretero, Domenico Talia
In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated
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Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-02-01 Guanghui Wang, Xueyuan Zhang, Lingfeng Shen, Shengbo Chen, Fei Tong, Xin He, Wenyao Li
Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency
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Prompting Robotic Modalities (PRM): A structured architecture for centralizing language models in complex systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-31 Bilel Benjdira, Anis Koubaa, Anas M. Ali
Despite significant advancements in robotics and AI, existing systems often struggle to integrate diverse modalities (e.g., image, sound, actuator data) into a unified framework, resulting in fragmented architectures that limit adaptability, scalability, and explainability. To address these gaps, this paper introduces Prompting Robotic Modalities (PRM), a novel architecture that centralizes language
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EPPDL: An efficient privacy-preserving distributed ledger for digital asset transfer in Web3.0 Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-31 Lichuan Ma, Lu Zhou, Hang Huang, Youyang Qu, Xuefeng Liu
Nowadays, a new generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the
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EDP-CVSM model-based multi-keyword ranked search scheme over encrypted cloud data Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-28 Yinfu Deng, Hua Dai, Zhangchen Li, Haiping Huang, Qian Zhou, Jian Xu, Geng Yang
Traditional searchable encryption schemes for clouds are generally based on the term frequency-inverse document frequency (TF-IDF) vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It could lead to substantial computational cost of the inner product. If the dimensionality and sparsity of encrypted vectors can be reduced or compressed, the search processing
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Hybrid fuzzy grammar dynamic graph diffusing attention network for traffic flow prediction Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-27 Dongxue Zhang, Zhao Zhang, Xiaohong Jiao, Yahui Zhang
Accurate and real-time traffic flow prediction is an indispensable part of the intelligent transportation system and is essential in improving traffic planning capability. However, due to the highly nonlinear and spatiotemporal fluctuation characteristics of the large-scale traffic network data, it is a challenging issue to establish an accurate and effective prediction model. In this regard, a hybrid
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TEMPORISE: Extracting semantic representations of varied input executions for silent data corruption evaluation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-27 Junchi Ma, Yuzhu Ding, Sulei Huang, Zongtao Duan, Lei Tang
The continuous advancement of technology has led to increasingly complex computing systems, but it has also made them more susceptible to soft errors. Among the challenges posed by soft errors, silent data corruption (SDC) stands out as a particularly insidious threat, often occurring without warning. Estimating SDC probabilities for a program is a formidable task due to the diversity of inputs it
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An enhanced list scheduling algorithm for heterogeneous computing using an optimized Predictive Cost Matrix Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-27 Min Wang, Jiawang Chen, Haoyuan Wang, Ziyi Gao, Weihao Bian, Sibo Qiao
Effective task scheduling is essential for optimizing resource utilization and improving system performance in heterogeneous computing environments. Current algorithms face challenges, particularly their need for more focus on the computational demands of intensive tasks and their inadequate attention to load balancing during processor allocation. To solve these problems, this study introduces the
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Efficient Number Theoretic Transform accelerator on the versal platform powered by the AI Engine Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-27 Zhenshan Bao, Tianhao Zang, Yiqi Liu, Wenbo Zhang
Lattice-based cryptography, essential for fully homomorphic encryption, primarily relies on the computationally intensive Number Theoretic Transform (NTT). This paper proposes an NTT accelerator based on AMD/Xilinx Versal ACAP and AI Engine (AIE), featuring data engines on Programmable Logic (PL) and compute engines on the AIE. For inter-core parallelism on the AIE array, we propose an efficient method
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Evolutionary optimization of spatially-distributed multi-sensors placement for indoor surveillance environments with security levels Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-27 Luis M. Moreno-Saavedra, Vinícius G. Costa, Adrián Garrido-Sáez, Silvia Jiménez-Fernández, J. Antonio Portilla-Figueras, Sancho Salcedo-Sanz
The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on
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Adaptive incremental transfer learning for efficient performance modeling of big data workloads Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-26 Mariano Garralda-Barrio, Carlos Eiras-Franco, Verónica Bolón-Canedo
The rise of data-intensive scalable computing systems, such as Apache Spark, has transformed data processing by enabling the efficient manipulation of large datasets across machine clusters. However, system configuration to optimize performance remains a challenge. This paper introduces an adaptive incremental transfer learning approach to predicting workload execution times. By integrating both unsupervised
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An efficient blockchain for decentralized ABAC policy decision point Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-25 Qiwei Hu, Miguel Correia, Tao Jiang
Blockchain-enabled Policy Decision Point (PDP) has been a promising solution to the centralization concern in practical deployment of Attribute-Based Access Control (ABAC). However, existing blockchain systems cannot support PDP adequately since PDP functionalities introduce extra latency to blockchain’s execution process and limits system throughput. This paper proposes an efficient PDP Blockchain
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GPartition-store: A multi-group collaborative parallel data storage mechanism for permissioned blockchain sharding Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-25 Lin Qiu, Bo Yi, Xingwei Wang, Fei Gao, Kaimin Zhang, Yanpeng Qu, Min Huang
The problem of insufficient storage space caused by the full-replication mechanism, which is commonly employed in existing blockchains, poses an obstacle to system scalability. Moreover, existing storage sharding mechanisms are confronted with the risk of data tampering by reason of the existence of Byzantine nodes. To address the above problems, the storage partition mechanisms, integrating Erasure
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FedDSHAR: A dual-strategy federated learning approach for human activity recognition amid noise label user Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-25 Ziqian Lin, Xuefeng Jiang, Kun Zhang, Chongjun Fan, Yaya Liu
Federated learning (FL) has recently achieved successes in privacy-sensitive health-care applications like medical analysis. Most previous studies suppose that collected user data are well-annotated, however, it is a strong assumption in practice. For instance, human activity recognition (HAR) task aims to train a model which predicts a certain person’s activity based on sensor data series collected
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Leveraging local and global relationships for corrupted label detection Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-24 Phong Lam, Ha-Linh Nguyen, Xuan-Truc Dao Dang, Van-Son Tran, Minh-Duc Le, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo
The performance of the Machine learning and Deep learning models heavily depends on the quality and quantity of the training data. However, real-world datasets often contain a considerable percentage of noisy labels, ranging from 8.0% to 38.5%. This could significantly reduce model accuracy. To address the problem of corrupted labels, we propose Cola, a novel data-centric approach that leverages both
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Using binary hash tree-based encryption to secure a deep learning model and generated images for social media applications Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-22 Soniya Rohhila, Amit Kumar Singh
Deep learning (DL) plays a vital role in identifying critical features and patterns in digital images. Deep learning models and generated records, particularly digital images, are highly effective in media and other applications but pose privacy and security challenges. For example, healthcare professionals must understand how Artificial Intelligence (AI) makes decisions to trust and fully incorporate
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Generating hard Ising instances with planted solutions using post-quantum cryptographic protocols Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-22 Salvatore Mandrà, Humberto Munoz-Bauza, Gianni Mossi, Eleanor G. Rieffel
In this paper we present a novel method to generate hard instances with planted solutions based on the public–private McEliece post-quantum cryptographic protocol. Unlike other planting methods rooted in the infinite-size statistical analysis, our cryptographic protocol generates instances which are all hard (in cryptographic terms), with the hardness tuned by the size of the private key, and with
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Smart contract-based public integrity auditing for cloud storage against malicious auditors Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-22 Hui Tian, Nan Gan, Fang Peng, Hanyu Quan, Chin-Chen Chang, Athanasios V. Vasilakos
Cloud storage, a vital component of cloud computing, faces significant challenges in ensuring data integrity, which hinders its widespread adoption. Public auditing models, which rely on third-party auditors (TPAs), have been developed to address these issues by offloading computation from users. However, maintaining the consistent trustworthiness of TPAs remains a major challenge, especially in preventing
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Distributed and heterogeneous tensor–vector contraction algorithms for high performance computing Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-21 Pedro J. Martinez-Ferrer, Albert-Jan Yzelman, Vicenç Beltran
The tensor–vector contraction (TVC) is the most memory-bound operation of its class and a core component of the higher-order power method (HOPM). This paper brings distributed-memory parallelization to a native TVC algorithm for dense tensors that overall remains oblivious to contraction mode, tensor splitting, and tensor order. Similarly, we propose a novel distributed HOPM, namely dHOPM3, that can
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EC5: Edge–cloud collaborative computing framework with compressive communication Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-19 Jingwei Tan, Fagui Liu, Bin Wang, Qingbo Wu, C.L. Philip Chen
With an increasing number of deep neural network (DNN)-based applications being deployed at the edges, edge–cloud collaborative computing has emerged as a promising solution to alleviate the burden on resource-constrained edges by collaborative inference. However, simply offloading part of DNN to the cloud introduces significant communication overhead during inference. In this paper, we propose EC5
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ENNigma: A framework for Private Neural Networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-18 Pedro Barbosa, Ivone Amorim, Eva Maia, Isabel Praça
The widespread use of the Internet and digital services has significantly increased data collection and processing. Critical domains like healthcare rely on this data, but privacy and security concerns limit its usability, constraining the performance of intelligent systems, particularly those leveraging Neural Networks (NNs). NNs require high-quality data for optimal performance, but existing privacy-preserving
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WFE-Tab: Overcoming limitations of TabPFN in IIoT-MEC environments with a weighted fusion ensemble-TabPFN model for improved IDS performance Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-18 Sergio Ruiz-Villafranca, José Roldán-Gómez, Javier Carrillo-Mondéjar, José Luis Martinez, Carlos H. Gañán
In recent years we have seen the emergence of new industrial paradigms such as Industry 4.0/5.0 or the Industrial Internet of Things (IIoT). As the use of these new paradigms continues to grow, so do the number of threats and exploits that they face, which makes the IIoT a desirable target for cybercriminals. Furthermore, IIoT devices possess inherent limitations, primarily due to their limited resources
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QuantuneV2: Compiler-based local metric-driven mixed precision quantization for practical embedded AI applications Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-17 Jeongseok Kim, Jemin Lee, Yongin Kwon, Daeyoung Kim
Mixed-precision quantization methods have been proposed to reduce model size while minimizing accuracy degradation. However, existing studies require retraining and do not consider the computational overhead and intermediate representations (IR) generated during the compilation process, limiting their application at the compiler level. This computational overhead refers to the runtime latency caused
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Fuzzy energy management strategies for energy harvesting IoT nodes based on a digital twin concept Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-17 Michal Prauzek, Karolina Gaiova, Tereza Kucova, Jaromir Konecny
This study presents a cloud-assisted energy management strategy for energy harvesting Internet-of-Things (IoT) nodes, using a novel digital twin (DT) concept for dynamic optimization of IoT node behavior. The system is built upon a fuzzy-rule-based controller optimized through a differential evolution (DE) algorithm. DE is particularly well-suited for this application, as it is capable of optimizing
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Towards efficient privacy-preserving conjunctive keywords search over encrypted cloud data Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-16 Yaru Liu, Xiaodong Xiao, Fanyu Kong, Hanlin Zhang, Jia Yu
With increasing popularity of cloud computing, more and more users choose to store data on cloud servers. Privacy-preserving keyword search is a critical technology in the field of cloud computing, which can directly search for encrypted data stored on cloud servers. In this paper, we propose a new scheme which can achieve conjunctive keywords search in a privacy-preserving way, and maintain forward
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Scalable compute continuum Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-16 Valeria Cardellini, Patrizio Dazzi, Gabriele Mencagli, Matteo Nardelli, Massimo Torquati
The Compute Continuum paradigm addresses the challenges of heterogeneous and dynamic computing resources, facilitating distributed application execution while enhancing data locality, performance, availability, adaptability, and energy efficiency. By integrating IoT, edge, and cloud resources into a cohesive continuum, applications can operate closer to data sources and end users. This approach supports
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Service-driven dynamic QoS on-demand routing algorithm Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-15 Hao She, Lixing Yan, Chuanfeng Mao, Qihui Bu, Yongan Guo
With the proliferation of Internet of Things (IoT) devices, the scale of networks is growing exponentially. However, dynamically meeting the diverse quality of service (QoS) routing requirements for users and services in large-scale networks remains a critical challenge. To address this issue, this paper proposes a Service-Driven Dynamic QoS On-Demand model and establishes a corresponding QoS optimization
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Enhancing IoT security: Assessing instantaneous communication trust to detect man-in-the-middle attacks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-14 Rabeya Basri, Gour Karmakar, S.H. Shah Newaz, Joarder Kamruzzaman, Linh Nguyen, Mohammad Mahabub Alam, Muhammad Usman
Communication trust is regarded as an effective tool to detect various dangerous cyber attacks, including Man-in-the-Middle (MITM) attacks and acts as a complement to zero trust. There exist some approaches in the literature to calculate inter-node communication trust in Wireless Sensor Networks (WSNs) and IoT networks and leverage it to detect attacks. In WSNs, since promiscuous communication mode
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MSCPR: A maintainable vector commitment-based stateless cryptocurrency system with privacy preservation and regulatory compliance Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-14 Xingyu Yang, Lei Xu, Liehuang Zhu
In traditional account-based cryptocurrency systems, maintaining the state of all accounts consumes significant storage space. To reduce storage costs, recently some studies propose to leverage vector commitment schemes to design stateless cryptocurrency systems. In such systems, validators only need to store a commitment to the state vector to validate transactions. However, to prove membership in
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Hybrid deep learning-based cyberthreat detection and IoMT data authentication model in smart healthcare Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-13 Manish Kumar, Sushil Kumar Singh, Sunggon Kim
The Internet of Medical Things (IoMT)-based medical devices and sensors play a significant role in healthcare applications, enabling on-site and remote monitoring of vital parameters in patients and alerting medical personnel in critical situations. However, these networks are vulnerable to cybersecurity threats, resulting in issues such as patient safety concerns, data breaches, ransom demands, and
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FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-10 Aiting Yao, Shantanu Pal, Gang Li, Xuejun Li, Zheng Zhang, Frank Jiang, Chengzu Dong, Jia Xu, Xiao Liu
In recent years, there has been a rapid increase in the integration of Internet of Things (IoT) systems into edge computing. This integration offers several advantages over traditional cloud computing, including lower latency and reduced network traffic. In addition, edge computing facilitates the protection of users’ sensitive data by processing it at the edge before transmitting it to the cloud using
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Multilayer multivariate forecasting network for precise resource utilization prediction in edge data centers Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-10 Shivani Tripathi, Priyadarshni, Rajiv Misra, T.N. Singh
Efficient resource management and accurate prediction of cloud workloads are vital in modern cloud computing environments, where dynamic and volatile workloads present significant challenges. Traditional forecasting models often fail to fully capture the intricate temporal dependencies and non-linear patterns inherent in cloud data, leading to inefficiencies in resource utilization. To overcome these
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UD-LDP: A Technique for optimally catalyzing user driven Local Differential Privacy Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-09 Gnanakumar Thedchanamoorthy, Michael Bewong, Meisam Mohammady, Tanveer Zia, Md Zahidul Islam
Local Differential Privacy (LDP) has emerged as a popular mechanism for crowd-sourced data collection, but enforcing a uniform level of perturbation may hinder the participation of individuals with higher privacy needs, while high privacy levels that satisfy more users can reduce utility. To address this, we propose a cohort-based mechanism that allows participants to choose the privacy level from
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Task replication based energy management using random-weighted privacy-preserving distributed algorithm for real-time embedded system Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-06 Dr. A. Velliangiri, Dr. Jayaraj Velusamy, Dr. Maheswari M, Dr. R.Leena Rose
Efficient energy management in real-time embedded systems is increasingly challenging due to the growing complexity of distributed tasks and the need for robust privacy preservation. Conventional task mapping and repartitioning techniques have focused on increasing the system reliability, efficiency, and lifespan, but typically incurred a high peak power generation because of Thermal Design Power (TDP)
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Remote sensing revolutionizing agriculture: Toward a new frontier Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-06 Xiaoding Wang, Haitao Zeng, Xu Yang, Jiwu Shu, Qibin Wu, Youxiong Que, Xuechao Yang, Xun Yi, Ibrahim Khalil, Albert Y. Zomaya
Remote sensing-empowered agriculture is a significant approach that utilizes remote sensing (RS) to improve agricultural production and crop management. In the agricultural sector, RS allows the retrieval of extensive data related to land, vegetation, and crops, providing crucial information for farmers and decision-makers to enhance precision and efficiency in crop cultivation and management. The
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RADiCe: A Risk Analysis Framework for Data Centers Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-04 Fabian Mastenbroek, Tiziano De Matteis, Vincent van Beek, Alexandru Iosup
Datacenter service providers face engineering and operational challenges involving numerous risk aspects. Bad decisions can result in financial penalties, competitive disadvantage, and unsustainable environmental impact. Risk management is an integral aspect of the design and operation of modern datacenters, but frameworks that allow users to consider various risk trade-offs conveniently are missing
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Secure blockchain-based reputation system for IIoT-enabled retail industry with resistance to sybil attack Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-02 Wenjia Zhao, Xu Yang, Saiyu Qi, Junzhe Wei, Xinpei Dong, Xu Yang, Yong Qi
Leveraging the recent surge in the electronic retail industry, retailer reputation has emerged with increasing significance in shaping consumer purchasing decisions. Despite this, the existing reputation platforms remain largely centralized, thereby enabling retailers to exert total control over reputation services, a reality that compromises the authentic portrayal of retailers. In response, we introduce
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Blockchain and digital twin empowered edge caching for D2D wireless networks Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-02 Jianbo Du, Zuting Yu, Shulei Li, Bintao Hu, Yuan Gao, Xiaoli Chu
Edge caching is considered a promising technology to fulfill user equipment (UE) requirements for content services. In this paper, we explore the use of blockchain and digital twin technologies to support edge caching in a Device-to-Device (D2D) wireless network, where each UE may fetch content from its own caching buffer, from other UEs through D2D links, or from a content server. A digital twin monitors
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Blockchain and timely auction mechanism-based spectrum management Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-02 Hongyi Zhang, Mingqian Liu, Yunfei Chen, Nan Zhao
The rapid development of 5G/B5G communication networks and the exponential growth of next-generation wireless devices require more advanced and dynamic spectrum management and control architecture. Dynamic spectrum management and control based on blockchain is efficient and robust, but the cost of traditional consensus mechanisms is too high. In this paper, we propose a new spectrum management and
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Forward-Secure multi-user and verifiable dynamic searchable encryption scheme within a zero-trust environment Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-02 Zhihao Xu, Chengliang Tian, Guoyan Zhang, Weizhong Tian, Lidong Han
Privacy-preserving searchable encryption can allow clients to encrypt the data for secure cloud storage, enabling subsequent data retrieval while preserving the privacy of data. In this paper, we initialize the study of constructing a secure dynamic searchable symmetric encryption (DSSE) scheme in a zero-trust environment characterized by the threat model of honest-but-curious data owner (DO) + honest-but-curious
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A Kubernetes-based scheme for efficient resource allocation in containerized workflow Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-02 Danyang Liu, Yuanqing Xia, Chenggang Shan, Ke Tian, Yufeng Zhan
In the cloud-native era, Kubernetes-based workflow engines simplify the execution of containerized workflows. However, these engines face challenges in dynamic environments with continuous workflow requests and unpredictable resource demand peaks. The traditional resource allocation approach, which relies merely on current workflow load data, also lacks flexibility and foresight, often leading to resource
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Multi-Tree Genetic Programming with Elite Recombination for dynamic task scheduling of satellite edge computing Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-01 Changzhen Zhang, Jun Yang
Satellite Edge Computing (SEC) can provide task computation services to terrestrial users, particularly in areas lacking terrestrial network coverage. With the increasing frequency of computational demands from Internet of Things (IoT) devices and the limited and dynamic nature of computational resources in Low Earth Orbit (LEO) satellites, making effective real-time scheduling decisions in dynamic
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HephaestusForge: Optimal microservice deployment across the Compute Continuum via Reinforcement Learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-01 José Santos, Mattia Zaccarini, Filippo Poltronieri, Mauro Tortonesi, Cesare Stefanelli, Nicola Di Cicco, Filip De Turck
With the advent of containerization technologies, microservices have revolutionized application deployment by converting old monolithic software into a group of loosely coupled containers, aiming to offer greater flexibility and improve operational efficiency. This transition made applications more complex, consisting of tens to hundreds of microservices. Designing effective orchestration mechanisms
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Energy-aware scheduling and two-tier coordinated load balancing for streaming applications in apache flink Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2025-01-01 Hongjian Li, Junlin Li, Xiaolin Duan, Jianglin Xia
Apache Flink has become one of the highly regarded streaming computing frameworks with its excellent advantages of high throughput, low latency, and high reliability. However, the default task scheduling policy follows the first-come-first-served principle, which fails to fully consider the differences in energy efficiency and resource loading of nodes in heterogeneous clusters and may lead to high
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Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain Internet of Things based framework Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-31 Osama Almurshed, Ashish Kaushal, Souham Meshoul, Asmail Muftah, Osama Almoghamis, Ioan Petri, Nitin Auluck, Omer Rana
The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for
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Integrating scientific single-page applications with DevSecOps Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-31 Lance Drane, Marshall McDonnell, Randall Petras, Cody Stiner, Arthur J. Ruckman, Gavin M. Wiggins, Gregory Cage, Robert Smith, Seth Hitefield, Jesse McGaha, Andrew Ayres, Mike Brim, Richard Archibald, Addi Malviya-Thakur
In the rapidly evolving field of frontend development, Single-Page Applications (SPAs) stand out for their ability to create dynamic and interactive web applications, particularly valuable in scientific software for their real-time data integration and complex workflow management. However, the process of creating a single-page web application development environment that accurately reflects the production
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A decentralized asynchronous federated learning framework for edge devices Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-31 Bin Wang, Zhao Tian, Jie Ma, Wenju Zhang, Wei She, Wei Liu
The traditional synchronous federated learning framework ensures global model consistency and accuracy. However, it is limited by the computational power differences between devices and the influence of non-IID data, which leads to inefficient training and insufficient model generalization performance. In this paper, we propose a decentralized asynchronous federated learning framework. The framework
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Parallel software design of large-scale diamond-structured crystals molecular dynamics simulation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-30 Jianguo Liang, Qianqian Li, Hao Han, You Fu
Molecular dynamics (MD) simulation, a crucial technique for investigating atomic structure and dynamic properties, has become a primary method for studying the thermodynamic properties of dielectric materials, such as silicon, and their low-dimensional nanostructures. Diamond-structured semiconductors exhibit unique crystallographic properties. Achieving optimal simulation performance on supercomputing
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Cooperative metric learning-based hybrid transformer for automatic recognition of standard echocardiographic multi-views Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-30 Yiran Li, Yankun Cao, Jia Mi, Xiaoxiao Cui, Xifeng Hu, Yuezhong Zhang, Zhi Liu, Lizhen Cui, Shuo Li
The successful recognition of the standard echocardiographic ten-views remains elusive, primarily due to the complexity of cardiac anatomy, confusion caused by low-quality data, and subtle variations among closely related multi-views. To cope with the limitations of existing algorithms, which include a lack of objectivity, accuracy, and robustness, we propose a Hybrid Cooperative Metric Network (HCMN)
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Advancing cuffless arterial blood pressure estimation: A patient-specific optimized approach reducing computational requirements Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-28 José A. González-Nóvoa, Laura Busto, Silvia Campanioni, Carlos Martínez, José Fariña, Juan J. Rodríguez-Andina, Pablo Juan-Salvadores, Víctor Jiménez, Andrés Íñiguez, César Veiga
Hypertension remains a leading cause of premature mortality globally, emphasizing the critical need for early detection and management. Unfortunately, less than half of hypertensive adults receive proper diagnosis and treatment. To address this gap, continuous blood pressure (ABP) monitoring has emerged as a valuable tool for detecting cardiovascular complications before they escalate. ABP monitoring
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DP-LTGAN: Differentially private trajectory publishing via Locally-aware Transformer-based GAN Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-28 R. Zhang, W. Ni, N. Fu, L. Hou, D. Zhang, Y. Zhang
Trajectory data has a wide range of applications in various domains but also raises serious privacy concerns. To address these concerns, the integration of deep learning with differential privacy for trajectory publication has gained widespread attention. However, existing solutions are mostly based on temporal neural networks and the Generative Adversarial Networks (GAN) framework, which intrinsically
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A mask guided cross data augmentation method for industrial defect detection Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-24 Xubin Wang, Wenju Li, Chang Lu
Expert in pattern recognition, deep learning is widely used for defect detection. Data augmentation is in principle capable of improving the accuracy and robustness of such data-driven models by supplementing patterns. However, the requirements for realistic images, precise annotations, and diverse defect patterns often cannot be addressed simultaneously in current augmentation methods. In this work
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Dual-channel meta-federated graph learning with robust aggregation and privacy enhancement Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-24 Jingtong Huang, Xu Ma, Yuan Ma, Kehao Chen, Xiaoyu Zhang
Graph neural networks (GNNs) are effective for graph-based node classification tasks, such as data mining and recommendation systems. Combining federated learning(FL) with GNN enables multiple participants to collaboratively train powerful models without sharing private data. However, subgraph-level FL faces challenges, including missing cross-client edges and non-IID data distributions. Additionally
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Adaptive Stochastic Gradient Descent (SGD) for erratic datasets Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-21 Idriss Dagal, Kürşat Tanriöven, Ahmet Nayir, Burak Akın
Stochastic Gradient Descent (SGD) is a highly efficient optimization algorithm, particularly well suited for large datasets due to its incremental parameter updates. In this study, we apply SGD to a simple linear classifier using logistic regression, a widely used method for binary classification tasks. Unlike traditional batch Gradient Descent (GD), which processes the entire dataset simultaneously
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Scheduling energy-constrained parallel applications in heterogeneous systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-12-21 Hongzhi Xu, Binlian Zhang, Chen Pan, Keqin Li
With the rapid development of information technology, efficient energy utilization has become a major challenge in modern computing system design. This paper focuses on the energy-constrained parallel application scheduling problem in heterogeneous systems and proposes three algorithms to minimize the makespan of applications. The first one is the minimum makespan algorithm under energy constraints