Future Generation Computer Systems-The International Journal of Escience最新文献_第5页

Scalable compute continuum

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-16 DOI: 10.1016/j.future.2024.107697

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 refined adaptation strategies tailored to specific infrastructure components, enabling reduced latency, optimized bandwidth use, and improved privacy. To fully realize the Compute Continuum’s potential, autonomous and proactive management is essential, leveraging interdisciplinary methods from optimization theory, control theory, machine learning, and artificial intelligence. This special issue highlights advancements in three key areas: resource characterization and scheduling, middleware for application deployment and reconfiguration, and applications in the Compute Continuum. These contributions highlight innovative solutions for resource optimization, dynamic management, and real-world implementations, showcasing the potential of the Compute Continuum to revolutionize distributed computing across diverse domains.

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引用次数: 0

Towards efficient privacy-preserving conjunctive keywords search over encrypted cloud data

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-16 DOI: 10.1016/j.future.2025.107716

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 security. In order to realize conjunctive keywords search with reduced communication cost and leakage, our scheme constructs a secure index based on the full binary tree data structure. Each leaf node represents a keyword, and the node stores the file identifier containing the keyword. Thus, all files containing searched keywords can be searched at one time without searching one file by one. The search time is only related to the number of search keywords and not related to the number of files. Each non-leaf node stores the keywords of its left and right child nodes, which are mapped to the Indistinguishable Bloom Filter(IBF). To achieve forward security, we choose a random string as the latest state to update trapdoors for each update query. Thus, update trapdoor cannot match with previous search trapdoors to achieve forward security. Finally, detailed experiments and security analysis prove that our scheme is secure and efficient.

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引用次数: 0

Service-driven dynamic QoS on-demand routing algorithm

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-15 DOI: 10.1016/j.future.2024.107685

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 objective function. The SHA-256 hash algorithm is utilized to simplify the large-scale network model, effectively reducing the number of Segment Routing (SR) nodes. The proposed Service-Driven Dynamic QoS On-Demand Routing Algorithm (SDDRL) identifies the optimal path, which is then uniformly disseminated by the SDN controller, thereby addressing existing challenges in SDN-IoT networks. Compared to OSPF-based and DDQN-based algorithms, the SDDRL algorithm reduces the average delay by 53.85% and 31.63%, respectively. The proposed algorithm reduces the packet loss rate, improves the average network congestion degree and route calculation time compared to other existing algorithms, and it demonstrates superior performance in handling complex tasks.

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引用次数: 0

Enhancing IoT security: Assessing instantaneous communication trust to detect man-in-the-middle attacks

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-14 DOI: 10.1016/j.future.2025.107714

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 is used in calculating inter-node communication trust, it is not suitable for IoT networks. For IoT, the packet forwarding behavior of edge nodes is used in calculating inter-node communication trust, which is limited to detect the MITM attacks effectively unless an edge node is compromised and acts as an MITM attacker. Additionally, these trust calculation mechanisms neither leverage communication channel characteristics nor the communication trust between sensor and edge nodes. Protecting IoT networks from various cyber attacks like MITM attacks requires the instantaneous trust calculation using communication channel characteristics. Since active MITM attacks incur delays, consideration of delay in trust calculation appears to be an effective means in identifying attacks. Neither end-to-end (E2E) delay nor delay due to attacks has been used in communication trust calculation in the existing literature. To bridge this research gap and detect active MITM attacks accurately and spontaneously, in this paper, a new conceptual model, named IPCTCM is introduced for instantaneous trust calculation of an IoT communication channel leveraging delay due to active MITM attacks. Two popular time-series data estimation tools, named Kalman filter and LSTM are used to estimate the expected E2E delay to identify delay due to attacks. Our proposed communication trust calculation model is validated using the data, generated by a testbed implementation in our IoT lab at Federation University Australia. Performance evaluation shows our proposed model achieves an attack detection accuracy of 98.9%, which outperforms an existing intrusion detection method with the improvement of 48.1% accuracy. Furthermore, our trust calculation method has broader applicability in other communication domains as well.

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引用次数: 0

MSCPR: A maintainable vector commitment-based stateless cryptocurrency system with privacy preservation and regulatory compliance MSCPR：一个可维护的基于矢量承诺的无状态加密货币系统，具有隐私保护和法规遵从性

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-14 DOI: 10.1016/j.future.2025.107713

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 the state vector, each user is required to locally maintain a position proof. This introduces a burden as users need to update their proofs every time the commitment value changes. Additionally, existing stateless systems often include users’ account balances and transferred values in transactions explicitly, which compromises privacy. To address above issues, we propose a stateless cryptocurrency system based on a maintainable vector commitment scheme. In the proposed system, a bucketing technique is employed to simplify the proof update operations. And we leverage the homomorphic property of vector commitments to preserve the confidentiality of transactions. Furthermore, by constructing an anonymity set, transaction anonymity is ensured. To prevent adversaries from taking advantage of the anonymity, we design a predicate encryption-based regulation scheme. Through a series of simulations, we demonstrate that the proposed system is storage-efficient, with acceptable time overhead for privacy preservation and regulatory compliance.

在传统的基于账户的加密货币系统中，维护所有账户的状态需要消耗大量的存储空间。为了降低存储成本，最近一些研究提出利用矢量承诺方案来设计无状态加密货币系统。在这样的系统中，验证器只需要将承诺存储到状态向量以验证事务。然而，为了证明状态向量中的成员资格，每个用户都需要在本地维护一个位置证明。这带来了负担，因为用户需要在每次承诺值更改时更新他们的证明。此外，现有的无状态系统通常显式地包含用户的帐户余额和交易中的转移值，这损害了隐私。为了解决上述问题，我们提出了一种基于可维护的矢量承诺方案的无状态加密货币系统。在该系统中，采用了桶式技术来简化证明更新操作。我们利用向量承诺的同态特性来保护事务的机密性。通过构造匿名集，保证了事务的匿名性。为了防止攻击者利用匿名性，我们设计了一个基于谓词加密的监管方案。通过一系列模拟，我们证明了所提出的系统具有存储效率，并且在隐私保护和法规遵从性方面具有可接受的时间开销。

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引用次数: 0

Hybrid deep learning-based cyberthreat detection and IoMT data authentication model in smart healthcare 智能医疗中基于深度学习的混合网络威胁检测和IoMT数据认证模型

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-13 DOI: 10.1016/j.future.2025.107711

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 device tampering. Detecting cyberthreats efficiently is challenging because IoMT generates large temporal data. Furthermore, cyberattacks typically involve imbalanced classification, where classes are not equally represented. The absence of data authentication can lead to severe consequences, including threats to patient privacy and financial ramifications, ultimately eroding trust in the healthcare system.

This paper proposes an improved deep learning-based model for cyberthreat detection and IoMT data authentication in smart healthcare. First, it introduces an embedded Ensemble Learning (EL) technique to select important features of IoMT, which trims unnecessary features and reduces the possibility of overfitting by classifiers. These scaled inputs are fed into the proposed One-Dimensional Convolution Long Short-Term Memory (1D-CLSTM) Neural Network to classify cyberthreats. The random undersampling boosting technique has been applied to address issues like imbalance classification. The PoAh consensus algorithm is applied in the fog layer for data authentication. The proposed model is evaluated based on various performance metrics and compared to state-of-the-art techniques such as 1D-CNN, LSTM, and GRU. Evaluation results show that the proposed 1D-CLSTM achieves 100% accuracy with the WUSTL-EHMS-2020 and 98.55% test accuracy with the ECU-IoHT datasets. The PoAh-based authentication takes 3.47 s at average 9th iteration.

基于医疗物联网（IoMT）的医疗设备和传感器在医疗保健应用中发挥着重要作用，可以对患者的重要参数进行现场和远程监控，并在危急情况下向医务人员发出警报。然而，这些网络容易受到网络安全威胁，导致诸如患者安全问题、数据泄露、赎金要求和设备篡改等问题。有效检测网络威胁具有挑战性，因为IoMT会产生大量的时间数据。此外，网络攻击通常涉及不平衡分类，其中类别的代表并不平等。缺乏数据身份验证可能导致严重后果，包括对患者隐私和财务后果的威胁，最终侵蚀对医疗保健系统的信任。

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引用次数: 0

Multilayer multivariate forecasting network for precise resource utilization prediction in edge data centers 面向边缘数据中心资源利用精确预测的多层多元预测网络

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-10 DOI: 10.1016/j.future.2024.107692

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 limitations, this research introduces the MultiLayer Multivariate Resource Predictor (MMRP), a novel deep learning architecture that seamlessly integrates a Multi-Head Attention Transformer model with Convolutional Neural Networks and Bidirectional Long Short-Term Memory units. The proposed model is designed to excel in capturing long-range dependencies and complex patterns, thereby significantly enhancing the accuracy of workload predictions. Extensive, rigorous experimentation using real-world Alibaba and Google cluster traces reveals that the proposed model consistently outperforms existing state-of-the-art models and related cloud resource utilization prediction in both univariate and multivariate time series forecasting tasks. The model demonstrates a remarkable improvement in prediction performance, with an average R squared increase of 5.76% and a Mean Absolute Percentage Error reduction of 84.9% compared to the best-performing baseline models. Furthermore, our model achieves a significant reduction in Root Mean Square Error by approximately 35.34% and decreases Mean Absolute Error by about 39.49% on average. Its scalability and adaptability across various cloud environments underscore the proposed model’s potential to optimize resource allocation, paving the way for more efficient and reliable cloud-based systems.

在现代云计算环境中，高效的资源管理和准确的云工作负载预测至关重要，因为动态和不稳定的工作负载带来了重大挑战。传统的预测模型往往不能完全捕捉云数据中复杂的时间依赖性和非线性模式，导致资源利用效率低下。为了克服这些限制，本研究引入了多层多元资源预测器（MMRP），这是一种新颖的深度学习架构，将多头注意力转换器模型与卷积神经网络和双向长短期记忆单元无缝集成。所提出的模型被设计为在捕获远程依赖关系和复杂模式方面表现出色，从而显著提高了工作负载预测的准确性。使用真实世界的阿里巴巴和谷歌聚类轨迹进行的广泛、严格的实验表明，在单变量和多变量时间序列预测任务中，所提出的模型始终优于现有的最先进模型和相关的云资源利用预测。与表现最好的基线模型相比，该模型在预测性能上有了显著的提高，平均R平方增加了5.76%，平均绝对百分比误差减少了84.9%。此外，我们的模型使均方根误差（Root Mean Square Error）降低了约35.34%，平均绝对误差（Mean Absolute Error）降低了约39.49%。其跨各种云环境的可伸缩性和适应性强调了所建议模型优化资源分配的潜力，为更高效和可靠的基于云的系统铺平了道路。

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引用次数: 0

FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-10 DOI: 10.1016/j.future.2025.107706

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 techniques such as Federated Learning (FL) and Differential Privacy (DP). However, these techniques have limitations, such as the risk of user information being obtained by attackers through the uploaded weights/model parameters in FL and the randomness of DP, which limits data availability. To address these issues, this paper proposes a framework called FedShufde (Federated Learning with a Shuffle Model and Differential Privacy in Edge Computing Environments) to protect user privacy in edge computing-based IoT systems, using an Unmanned Aerial Vehicle (UAV) delivery system as an example. FedShufde uses local differential privacy and the shuffle model to prevent attackers from inferring user privacy from information such as UAV’s location, flight conditions, or delivery address. In addition, the network connection between the UAV and the edge server cannot be obtained by the cloud aggregator, and the shuffle model reduces the communication cost between the edge server and the cloud aggregator. Our experiments on a real-world edge-based smart UAV delivery system using public datasets demonstrate the significant advantages of our proposed framework over baseline strategies.

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引用次数: 0

UD-LDP: A Technique for optimally catalyzing user driven Local Differential Privacy UD-LDP：一种最佳催化用户驱动的本地差分隐私的技术

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-09 DOI: 10.1016/j.future.2025.107712

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 a predefined set. We investigate optimal cohort configurations and uncover insights about utility convexity, enabling the identification of privacy-utility balanced settings. Our proposed mechanism, called UD-LDP, empowers users, promotes transparency, and facilitates suitable privacy budget selection. We demonstrate the effectiveness of cohortisation through experiments on synthetic and real-world datasets.

局部差分隐私（LDP）已成为一种流行的众包数据收集机制，但强制执行统一的扰动水平可能会阻碍具有更高隐私需求的个人的参与，而满足更多用户的高隐私水平可能会降低效用。为了解决这个问题，我们提出了一种基于队列的机制，允许参与者从预定义的集合中选择隐私级别。我们研究了最优队列配置，并揭示了效用凸性的见解，从而能够识别隐私-效用平衡设置。我们提出的机制，称为UD-LDP，赋予用户权力，提高透明度，并促进适当的隐私预算选择。我们通过对合成数据集和现实世界数据集的实验证明了协同化的有效性。

引用次数: 0

Task replication based energy management using random-weighted privacy-preserving distributed algorithm for real-time embedded system

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-06 DOI: 10.1016/j.future.2025.107708

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) limitations which confines the scalability and applicability. To overcome these problems, the Task Replication-based Energy Management using Random-weighted Privacy-preserving Distributed Algorithm (TR-EM-R-RWPPDA-RTES) is proposed as a new scheme for real-time embedded systems. This architecture integrates Hotspot-Aware Task Mapping (HATM) to optimally load tasks across cores, Dynamic Heterogeneous Earliest Finish Time (DHEFT) scheduling to improve execution timing, and a Reliability-based Random-Weighted Privacy-Preserving Distributed Algorithm (R-RWPPDA) to optimize power consumption. Using these elements, the proposed approach reduces both system energy consumption and system trustworthiness. Comprehensive simulations based on the MiBench benchmark suite, as well as gem5 and McPAT simulators on ARM multicore processors (4, 8, and 16 cores), are also shown to validate the robustness of the proposed method. TR-EM-R-RWPPDA-RTES yields 23.73 %, 36.33 %, and37.84 % peak power consumption reduction with respect to the state-of-the-art solutions, thus providing a robust solution for energy-efficient, robust and reliable real-time embedded systems.

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引用次数: 0