Future Generation Computer Systems-The International Journal of Escience最新文献

英文中文

Large-scale HPC approaches and applications on highly distributed platforms. 大规模高性能计算方法及其在高度分布式平台上的应用

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 : 2026-01-07 DOI: 10.1016/j.future.2025.108365

Alessia Antelmi , Emanuele Carlini

The ever-increasing complexity of scientific and industrial challenges due to the enormous amount of data available nowadays requires advanced high-performance computing (HPC) solutions capable of processing and analyzing data efficiently on highly distributed platforms. Traditional centralized HPC systems frequently fall short of the demands of contemporary large-scale applications (e.g., large language models), prompting a move towards more flexible and scalable distributed computing environments. Furthermore, the growing emphasis on the environmental impact of large-scale computing has highlighted the need for sustainable computing practices that minimize energy consumption and carbon footprint. This special issue targets contributions that investigate both the challenges and the opportunities arising from this evolution. The accepted articles highlight enhancements in five key areas: (i) HPC in the cloud continuum, (ii) heterogeneous HPC architectures, performance tools, and programming models, (iii) parallel and distributed algorithms and applications, (iv) data management and storage systems, and (v) sustainable and energy-efficient HPC systems. In total, 29 submissions were received, and 20 papers were selected after a rigorous peer-review process. Collectively, these contributions provide a representative snapshot of current research efforts towards resilient, efficient, and sustainable HPC approaches and applications on highly distributed platforms.

由于目前可用的大量数据，科学和工业挑战的复杂性不断增加，需要能够在高度分布式平台上有效处理和分析数据的先进高性能计算（HPC）解决方案。传统的集中式HPC系统经常无法满足当代大规模应用程序（例如，大型语言模型）的需求，这促使人们转向更灵活和可扩展的分布式计算环境。此外，大规模计算对环境的影响日益受到重视，这突出了对可持续计算实践的需求，这些实践可以最大限度地减少能源消耗和碳足迹。本期特刊针对的是调查这一演变所带来的挑战和机遇的文章。被接受的文章强调了五个关键领域的增强：(i)云连续体中的HPC，（ii）异构HPC架构，性能工具和编程模型，（iii）并行和分布式算法和应用程序，（iv）数据管理和存储系统，以及(v)可持续和节能的HPC系统。总共收到了29份意见书，经过严格的同行评议过程，20篇论文被选中。总的来说，这些贡献提供了当前对高度分布式平台上弹性、高效和可持续的高性能计算方法和应用的研究工作的代表性快照。

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引用次数: 0

Distributed multi-objective consumer-centric routing for LoRa-based IoT-enabled FANET 基于lora的物联网FANET分布式多目标以消费者为中心的路由

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 : 2026-01-06 DOI: 10.1016/j.future.2026.108368

Omer Chughtai , Muhammad Waqas Rehan , Muhammad Naeem , Ali Hamdan Alenezi , Sajjad Ali Haider

LoRa-enabled Flying Ad Hoc Networks (FANETs) offer long-range and energy-efficient connectivity for next-generation IoT and post-disaster communication infrastructures, yet their performance is fundamentally constrained by limited bandwidth, dynamic topologies, and uneven energy depletion across aerial nodes. This work develops a distributed consumer-centric multi-objective routing framework (Proposed-CCR) that jointly optimizes residual energy, link quality, and flow-level priority through a lightweight utility-driven forwarding mechanism. The design integrates composite cost modeling, two-hop neighbor awareness, adaptive path monitoring, and local repair to ensure scalable, resilient, and delay-aware multi-hop communication. Extensive simulations demonstrate that Proposed-CCR reduces per-packet energy consumption by 28–35%, extends network lifetime by over 35%, and decreases high-priority flow delay by nearly 40% relative to state-of-the-art schemes including MinHop, ACOR, GCCR, and BBCCR. These results confirm the effectiveness of a consumer-centric, LoRa-aware multi-objective heuristic for UAV-IoT integration and emergency communication scenarios, while highlighting practical opportunities for sustainable and resource-efficient airborne networking architectures.

基于lora的飞行自组织网络（fanet）为下一代物联网和灾后通信基础设施提供了远程和节能的连接，但其性能从根本上受到带宽有限、动态拓扑和空中节点间能量消耗不均匀的限制。本研究开发了一个以消费者为中心的分布式多目标路由框架（Proposed-CCR），通过轻量级实用驱动的转发机制共同优化剩余能量、链路质量和流级优先级。该设计集成了复合成本建模、两跳邻居感知、自适应路径监控和本地修复，以确保可扩展、弹性和延迟感知的多跳通信。大量的仿真表明，与MinHop、ACOR、GCCR和BBCCR等最先进的方案相比，提议的ccr将每包能耗降低了28-35%，将网络寿命延长了35%以上，并将高优先级流延迟降低了近40%。这些结果证实了以消费者为中心、lora感知的多目标启发式方法在无人机-物联网集成和应急通信场景中的有效性，同时强调了可持续和资源高效的机载网络架构的实践机会。

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引用次数: 0

Edge-based proactive and stable two-tier routing for IoV 基于边缘的主动稳定的两层车联网路由

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 : 2026-01-06 DOI: 10.1016/j.future.2026.108367

Asif Mehmood , Muhammad Afaq , Faisal Mehmood , Wang-Cheol Song

The Internet of Vehicles (IoV) is an evolving domain fueled by advancements in vehicular communications and networking. To enhance vehicle coverage, integrating vehicle-to-everything (V2X) networks with cellular networks has become essential, though this integration places increased demand on cellular infrastructure. To address this, we propose a two-tier stable path routing algorithm designed to improve the stability and proactiveness of V2X networks. Our approach divides the coverage area into zones, further segmented into road segments based on road structure. The first tier manages routing within a road segment, while the second tier handles routing between vehicles in adjacent segments. This method improves road awareness, stabilizes topologies, adapts to dynamic changes, and reduces routing overhead. Additionally, the incorporation of Kalman filter-based prediction model further strengthens proactive routing. To validate the proposed approach, we conduct synthetic evaluations across varying vehicular densities with different mobility and traffic scenarios. We compare the traditional centralized routing strategy with the proposed distributed two-tier mechanism to assess execution cost, end-to-end latency, network resource consumption, data rates, packet flow, and packet loss. Quantified results demonstrate that our two-tier approach reduces the average execution cost from 438.61 to 230.48, lowers average latency from 232.34 ms to 129.03 ms, and minimizes average network consumption from 231.26 MB to 129.39 MB. The proposed approach continues to significantly enhance data rates, reduce packet flow processing, decrease packet loss across various routing strategies. Overall, the proposed solution enhances stability, responsiveness, and robustness of V2X communication, making it suitable for future large-scale IoV deployments.

车联网（IoV）是一个不断发展的领域，受到汽车通信和网络技术进步的推动。为了提高车辆覆盖范围，将V2X网络与蜂窝网络集成变得至关重要，尽管这种集成对蜂窝基础设施的需求增加了。为了解决这个问题，我们提出了一种两层稳定路径路由算法，旨在提高V2X网络的稳定性和主动性。我们的方法将覆盖区域划分为区域，并根据道路结构进一步细分为道路段。第一层管理路段内的路线，而第二层处理相邻路段车辆之间的路线。该方法提高了道路感知能力，稳定了拓扑结构，适应动态变化，减少了路由开销。此外，结合基于卡尔曼滤波的预测模型，进一步加强了主动路由。为了验证所提出的方法，我们在不同的车辆密度、不同的机动性和交通场景下进行了综合评估。我们将传统的集中式路由策略与提出的分布式两层机制进行比较，以评估执行成本、端到端延迟、网络资源消耗、数据速率、数据包流和数据包丢失。量化结果表明，我们的两层方法将平均执行成本从438.61降低到230.48，将平均延迟从232.34 ms降低到129.03 ms，并将平均网络消耗从231.26 MB降低到129.39 MB。所提出的方法继续显著提高数据速率，减少数据包流处理，减少各种路由策略之间的数据包丢失。总体而言，该解决方案增强了V2X通信的稳定性、响应性和鲁棒性，适用于未来的大规模车联网部署。

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引用次数: 0

SP-IFDA-Traj: Optimizing differentially private trajectory publishing for enhanced utility SP-IFDA-Traj：优化不同的私人轨迹发布增强效用

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 : 2026-01-05 DOI: 10.1016/j.future.2025.108359

Yongxin Zhao , Hao Lin , Xiangtian Zheng , Yixuan Song , Jinze Du

Differential privacy (DP) is crucial for trajectory data publication, yet the utility of existing prefix-tree-based mechanisms heavily depends on intricate parameter tuning, often yielding suboptimal performance. To address this, we propose SP-IFDA-Traj, a novel framework for automatically and jointly optimizing critical parameters in personalized noisy prefix-tree trajectory publishing. SP-IFDA-Traj enhances the Flow Direction Algorithm (FDA) for optimizing parameters by: 1) employing chaotic mapping initialization for improved population diversity, 2) incorporating adaptive neighborhood generation to balance exploration and exploitation, and 3) leveraging Spark-based parallelized fitness evaluation for enhanced efficiency. Guided by a meticulously designed fitness function targeting maximal data utility, our framework optimizes Hilbert encoding order, privacy budget allocation, and pruning strategies. Extensive experiments on real-world large-scale trajectory datasets demonstrate that SP-IFDA-Traj substantially improves the privacy-utility trade-off. In convergence tests, the optimization engine improves the average fitness over FDA by 1.74% on BJ-Day3 and 103.98% on BJ-Day7, indicating consistently superior convergence across heterogeneous datasets. In terms of trajectory query accuracy, SP-IFDA-Traj reduces error to about 1% of that of baseline methods and approximately 10% of that of other existing optimization strategy models.

差分隐私（DP）对于轨迹数据发布至关重要，但现有基于前缀树的机制的效用严重依赖于复杂的参数调优，通常会产生次优性能。为了解决这个问题，我们提出了SP-IFDA-Traj，这是一个新的框架，用于自动和联合优化个性化噪声前缀树轨迹发布中的关键参数。SP-IFDA-Traj对Flow Direction Algorithm （FDA）的参数优化进行了改进：1)采用混沌映射初始化来提高种群多样性；2)采用自适应邻域生成来平衡探索和开发；3)利用基于spark的并行适应度评估来提高效率。在精心设计的适应度函数的指导下，以最大的数据效用为目标，我们的框架优化了希尔伯特编码顺序、隐私预算分配和修剪策略。在真实世界的大规模轨迹数据集上进行的大量实验表明，SP-IFDA-Traj极大地改善了隐私-效用权衡。在收敛测试中，优化引擎在BJ-Day3上的平均适应度提高了1.74%，在BJ-Day7上提高了103.98%，表明在异构数据集上始终具有优异的收敛性。在轨迹查询精度方面，SP-IFDA-Traj将误差降低到基线方法的1%左右，将误差降低到其他现有优化策略模型的10%左右。

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引用次数: 0

COZO : A secure and efficient blockchain-enhanced federated learning paradigm with optimized storage and equitable contribution valuation COZO：一种安全高效的区块链增强联邦学习范式，具有优化的存储和公平的贡献评估

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 : 2026-01-05 DOI: 10.1016/j.future.2025.108362

Ning Liu , Yizhi Zhou , Yuchen Qin , Qinzheng Feng , Heng Qi

Federated Learning (FL) serves as a crucial enabler for digital supply chain transformation by supporting secure multi-party data collaboration while preserving privacy. However, its real-world implementation encounters two critical limitations: (1) conventional contribution assessment approaches (e.g., Shapley value) often ignore individual rationality, resulting in biased incentives that undermine long-term engagement; and (2) the underlying blockchain storage infrastructure suffers from inherent performance constraints, including slow read/write operations that cannot keep pace with FL’s need for real-time data exchange and frequent model updates. To overcome these challenges, we propose a framework that leverages Clique Games Solution theory to ensure fair and efficient contribution measurement. Our solution incorporates coalition screening and Pareto optimization to guarantee individual rationality with polynomial computational complexity. Additionally, we integrate COLE-a columnar storage module enhanced with learned indexes and LSM-tree merge mechanisms-to dramatically accelerate data access. Experimental results demonstrate that our approach achieves 5.72e-4 reduction in core distance for contribution assessment stability, improves storage IOPS by 28.7%, and maintains model accuracy within 1.2% of the baseline methods, outperforming mainstream baselines in evaluation stability and storage performance while maintaining competitive model accuracy, thus offering a comprehensive and practical solution for privacy-aware distributed learning in sensitive environments.

联邦学习（FL）通过支持安全的多方数据协作，同时保护隐私，成为数字供应链转型的关键推动者。然而，它在现实世界的实施遇到了两个关键的限制：(1)传统的贡献评估方法（如Shapley值）往往忽视个人理性，导致有偏见的激励，破坏了长期参与；(2)底层区块链存储基础设施受到固有的性能限制，包括读写操作缓慢，无法跟上FL对实时数据交换和频繁模型更新的需求。为了克服这些挑战，我们提出了一个利用派系博弈解决方案理论的框架，以确保公平和有效的贡献测量。我们的解决方案结合了联盟筛选和帕累托优化，以多项式的计算复杂度保证了个体的合理性。此外，我们还集成了cole（一种增强了学习索引和lsm树合并机制的列式存储模块），以显著加快数据访问速度。实验结果表明，我们的方法在贡献评估稳定性方面的核心距离减少了5.72e-4，存储IOPS提高了28.7%，模型精度保持在基线方法的1.2%以内，在评估稳定性和存储性能方面优于主流基线，同时保持了具有竞争力的模型精度，从而为敏感环境下的隐私感知分布式学习提供了全面实用的解决方案。

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引用次数: 0

Artificial Intelligence for Interoperability (AIFI) - FGCS Editorial summary 人工智能互操作性(AIFI) - FGCS编辑摘要

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 : 2026-01-02 DOI: 10.1016/j.future.2025.108366

Luca Sciullo , Ivan Zyrianoff , Ronaldo C. Prati , Lionel Medini

引用次数: 0

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 : 2026-01-01

引用次数: 0

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 : 2026-01-01

引用次数: 0

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 : 2026-01-01

引用次数: 0

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 : 2026-01-01

引用次数: 0

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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Future Generation Computer Systems-The International Journal of Escience

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