Computer Communications最新文献_第2页

Multi-modal clinical AI: An edge-aware interpretable framework fusing textual-temporal data 多模式临床人工智能：融合文本-时间数据的边缘感知可解释框架

IF 4.2 3区计算机科学

Computer Communications Pub Date : 2026-08-01 DOI: 10.1016/j.comcom.2026.108633

Shamsul Haq, Prabal Verma

{"title":"Multi-modal clinical AI: An edge-aware interpretable framework fusing textual-temporal data","authors":"Shamsul Haq, Prabal Verma","doi":"10.1016/j.comcom.2026.108633","DOIUrl":"10.1016/j.comcom.2026.108633","url":null,"abstract":"<div><div>Modern clinical AI faces growing challenges in integrating high-frequency features from clinical sensors with unstructured text under latency-sensitive and localized data processing requirements. In response, this paper introduces a unified, lightweight edge-centric framework designed for next-generation clinical AIoT that represents a fundamental paradigm shift from architectures that inherently conflate independent feature dynamics. Initially pre-trained on a cloud layer, the system utilizes a novel deep learning mechanism that decomposes recurrent processing into independent units to effectively trace the memory of a single variable while capturing global semantic guidance. Consequently, this design allows the model to jointly reason over textual and temporal data, capturing short and long-term dependencies without informational dilution. Furthermore, to ensure robust deployment, we incorporate a favorable, effective validation mechanism to certify the structural fidelity of representations generated at the edge. Ultimately, a retrained lightweight encoder facilitates early detection and rapid local inference close to the data source. This localized processing and the effective data compression reduce the communication overhead and restrict raw data exposure. Subsequently, the deployed decoder drives scalable deep fusion and model updates, achieving significant performance with accuracy, precision, recall, and F1 metric values of (0.921, 0.920, 0.910, 0.915) for hypertension, (0.932, 0.910, 0.920, 0.915) for diabetes, and (0.925, 0.920, 0.920, 0.910) for heart attack.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108633"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special Issue on selected papers from IFIP Networking 2024 IFIP网络2024年论文特刊

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-06-01 Epub Date: 2026-01-23 DOI: 10.1016/j.comcom.2026.108426

Ioannis Chatzigiannakis , Periklis Chatzimisios , Luis Muñoz , Manabu Tsukada

{"title":"Special Issue on selected papers from IFIP Networking 2024","authors":"Ioannis Chatzigiannakis , Periklis Chatzimisios , Luis Muñoz , Manabu Tsukada","doi":"10.1016/j.comcom.2026.108426","DOIUrl":"10.1016/j.comcom.2026.108426","url":null,"abstract":"<div><div>This Special Issue presents extended versions of selected top papers from the 23rd IFIP Networking Conference (IFIP Networking 2024), sponsored by the IFIP Technical Committee on Communication Systems (TC6), IEEE ComSoc, and the Italian RESTART Foundation. The contributions advance reliability and determinism through multipath redundancy for 5G streaming and practical packet ordering for deterministic wireless networking. They improve scalable traffic engineering by speeding up segment-routing optimization via path preprocessing without breaking constraints. Support is provided for emerging XR services with both cooperative access/edge orchestration and realistic, measurement-based XR traffic modeling. Finally, the contributions strengthen trustworthy and impactful networking through AS risk assessment at IXPs, an IoT framework for smart water systems, and optimized virtual channel establishment in payment channel networks.</div><div>Taken together, they paint a picture of a field that is simultaneously pushing new capabilities, while also advancing a shift <em>from optimality to operational practicality</em>, where scalable methods bridge the gap between theoretically optimal solutions and real-world constraints in traffic engineering, deterministic networking, and XR orchestration. An emerging theme is that of <em>measurement-driven design</em>, highlighting how empirical traffic modeling and control-plane analyses strengthen credibility, reproducibility, and operational relevance. Finally, the papers emphasize <em>reliability, resilience, and security by construction</em> and the growing importance of networking for <em>societal-scale applications</em>, spanning sustainable smart infrastructure and immersive services.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"255 ","pages":"Article 108426"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148187005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Festschrift in Honor of Prof. Arturo Azcorra 社论：纪念阿图罗·阿兹科拉教授的节日

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1016/j.comcom.2026.108449

Albert Banchs , Claudio Fiandrino , Marco Gramaglia , Joerg Widmer

引用次数: 0

Corrigendum to ‘A metaheuristic study: A partial multitask offloading strategy for vehicular fog computing based on adaptive particle swarm optimization’ [Comput. Commun. 250 (2026) 108455] “一项元启发式研究：基于自适应粒子群优化的车辆雾计算部分多任务卸载策略”的更正[计算机]。common . 250 (2026) 108455]

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-06-01 Epub Date: 2026-04-08 DOI: 10.1016/j.comcom.2026.108507

Fuqi Zhang , Huilin Jiang , Fu Liu , Tao Hou , Yujia Liu

引用次数: 0

RAPSOVA: A Responsive, Adaptive, Scalable, and control plane Overhead-Aware framework for load balancing in Wi-Fi networks RAPSOVA：用于Wi-Fi网络负载平衡的响应性、自适应、可扩展和控制平面开销感知框架

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-06-01 Epub Date: 2026-05-21 DOI: 10.1016/j.comcom.2026.108567

Mohamed Bellouch , Lynda Zitoune , Iyad Lahsen-Cherif , Véronique Vèque

{"title":"RAPSOVA: A Responsive, Adaptive, Scalable, and control plane Overhead-Aware framework for load balancing in Wi-Fi networks","authors":"Mohamed Bellouch , Lynda Zitoune , Iyad Lahsen-Cherif , Véronique Vèque","doi":"10.1016/j.comcom.2026.108567","DOIUrl":"10.1016/j.comcom.2026.108567","url":null,"abstract":"<div><div>Load balancing in WiFi networks remains a persistent yet under-explored challenge. It consists of dynamically selecting the optimal mapping from active stations (STAs) to available access points (APs) that maximizes a performance measure (e.g., a fairness index or a network performance indicator). Nevertheless, load balancing is not addressed by the 802.11 standards and is typically left to vendor-specific solutions. In addition, existing literature lacks a unified technique capable of handling key challenges such as device mobility, STAs and APs switching on/off, scalability in large deployments, and delays in association decisions. We introduce a deep reinforcement learning (DRL)-based framework termed RAPSOVA (Responsive, Adaptive, Scalable, and control plane Overhead-Aware). RAPSOVA achieves scalability by dynamically partitioning the network into clusters, each managed by an agent that learns optimal STA-to-AP association policies to balance the load across its APs. Adaptivity is ensured through transitions between different Markov Decision Processes (MDPs) when changes in device count occur due to mobility or on/off behavior. For responsiveness, RAPSOVA employs three distinct neural network architectures to approximate Q-functions, enabling trade-offs between response delay, parameter storage, and learning performance. For overhead-awareness, agents share their training updates to collaborate, but in a selective fashion: an agent shares its training update only if a condition dependent on a threshold is met. Optimal thresholds are identified through an offline procedure that balances overhead and learning performance. Experimental studies are carried out to validate the effectiveness of RAPSOVA in guaranteeing these properties.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"255 ","pages":"Article 108567"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148187006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transforming wireless network efficiency with UAV path planning using deep reinforcement learning 基于深度强化学习的无人机路径规划改造无线网络效率

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-06-01 Epub Date: 2026-05-22 DOI: 10.1016/j.comcom.2026.108568

Karel Toledo , Guillermo Donoso , Daniel Psijas , Javier Baladron

引用次数: 0

Streaming algorithm and hardware accelerator for high-throughput entropy estimation of network flows in sliding windows 滑动窗口网络流高吞吐量熵估计的流算法和硬件加速器

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-03-15 Epub Date: 2026-02-10 DOI: 10.1016/j.comcom.2026.108446

Yaime Fernández , Javier E. Soto , Carolina Gallardo-Pavesi , Yasmany Prieto , Cecilia Hernández , Miguel Figueroa

{"title":"Streaming algorithm and hardware accelerator for high-throughput entropy estimation of network flows in sliding windows","authors":"Yaime Fernández , Javier E. Soto , Carolina Gallardo-Pavesi , Yasmany Prieto , Cecilia Hernández , Miguel Figueroa","doi":"10.1016/j.comcom.2026.108446","DOIUrl":"10.1016/j.comcom.2026.108446","url":null,"abstract":"<div><div>Shannon’s empirical entropy is widely used in network monitoring for anomaly detection and resource management. Since recent traffic is most relevant for predicting network behavior, entropy estimation is usually performed over sliding time windows. In modern links this task involves high-speed data streams, where both throughput and memory efficiency are critical. Hardware accelerators provide low-latency, energy-efficient processing within the network, but face strict on-chip memory limits. We propose a streaming algorithm for entropy estimation in sliding windows with discrete steps, tailored to these hardware constraints. The algorithm combines frequency and cardinality sketches, storing the most frequent flows and estimating the rest under a set of observations of the sorted log–log scaled histogram of the flows. Flow frequencies for a window <span><math><mi>W</mi></math></span> are reconstructed by merging ten non-sliding frequency sketches, each covering an interval <span><math><mrow><mi>s</mi><mo>=</mo><mi>W</mi><mo>/</mo><mn>10</mn></mrow></math></span>. We also design a Field-Programmable Gate Array (FPGA) accelerator architecture that implements this algorithm. On eight real traffic traces with two million flows per window, the algorithm achieves mean relative errors below 0.45% with a standard deviation of 0.14%. The accelerator, implemented on a Xilinx Virtex XCU55 UltraScale+ FPGA, sustains packet processing at over 160 Gbps while using 56% of the available BRAM, leaving capacity for additional tasks. These results show that accurate entropy estimation in sliding windows with discrete steps enables scalable, high-speed monitoring of modern networks.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"250 ","pages":"Article 108446"},"PeriodicalIF":4.3,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A metaheuristic study: A partial multitask offloading strategy for vehicular fog computing based on adaptive particle swarm optimization 基于自适应粒子群优化的车辆雾计算部分多任务卸载策略

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-03-15 Epub Date: 2026-02-11 DOI: 10.1016/j.comcom.2026.108455

Fuqi Zhang , Huilin Jiang , Fu Liu , Tao Hou , Yujia Liu

{"title":"A metaheuristic study: A partial multitask offloading strategy for vehicular fog computing based on adaptive particle swarm optimization","authors":"Fuqi Zhang , Huilin Jiang , Fu Liu , Tao Hou , Yujia Liu","doi":"10.1016/j.comcom.2026.108455","DOIUrl":"10.1016/j.comcom.2026.108455","url":null,"abstract":"<div><div>To satisfy the demand for low perceived latency in 5G vehicular services, a metaheuristic latency-aware optimization strategy based on the adaptive particle swarm optimization (PSO) algorithm is proposed. First, a single road side unit intensive short-range urban traffic scenario is modelled. Second, to address the described nondeterministic polynomial-time-hard problem, a nonlinear programming method is employed to schedule the idle vehicular fog nodes within a one-hop communication range. Finally, the adaptive PSO strategy is applied to optimize the total and average latencies of task processing by balancing the loads and perceived latency levels of the cloud and vehicular fog nodes. In the numerical simulation experiments, the total processing latency of the proposed strategy is reduced by up to 11.6% compared with that of the classic PSO algorithm, which is one of the most well-known bio-inspired approaches. The total processing latency is reduced by at least 38.0% compared with that of the other test schemes, which are demonstrated effective in this scenario. The experimental results indicate that the proposed method can be used to balance the loads of cloud and vehicular fog nodes and improve the quality of vehicle services and user satisfaction, which provides a suitable solution for the vehicle-fog-cloud scenario of urban smart transportation.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"250 ","pages":"Article 108455"},"PeriodicalIF":4.3,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiclass cost–latency aware framework for multi-tiered cloud storage optimization via access pattern forecasting 基于访问模式预测的多层云存储优化的多类成本延迟感知框架

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.comcom.2026.108437

Flávio A.A. Motta , Saulo Moraes Villela , Heder Soares Bernardino , Glauber Dias Gonçalves , Alex Borges Vieira

{"title":"A multiclass cost–latency aware framework for multi-tiered cloud storage optimization via access pattern forecasting","authors":"Flávio A.A. Motta , Saulo Moraes Villela , Heder Soares Bernardino , Glauber Dias Gonçalves , Alex Borges Vieira","doi":"10.1016/j.comcom.2026.108437","DOIUrl":"10.1016/j.comcom.2026.108437","url":null,"abstract":"<div><div>Efficient management of cloud storage resources requires intelligent tier allocation strategies that balance cost optimization with performance requirements. While previous approaches have focused on binary classification schemes for storage tiering, real-world scenarios demand more granular solutions that can adapt to diverse user preferences and workload characteristics. This paper extends our previous work on access frequency prediction by proposing a comprehensive multiclass machine learning framework for intelligent cloud storage tiering. The proposed framework incorporates a novel three-tier classification system (<em>Cold</em>/<em>Warm</em>/<em>Hot</em>) and integrates user-centric preferences through a cost-weight parameter, enabling dynamic adaptation to varying preferences along the cost–latency spectrum. We demonstrate the framework’s effectiveness through extensive experiments on real-world access patterns, where we assess the performance of thirteen machine learning algorithms under various user preference profiles. The results show that our multiclass approach achieves cost reductions of up to 40% compared to a static tiering strategy, while providing Pareto-optimal solutions for different user profiles. Through comprehensive Pareto frontier analysis, we demonstrate the framework’s ability to provide transparent trade-off visualization, enabling informed decision-making for cloud storage administrators. Our main contributions are: a multiclass classification approach for storage tiering, the integration of user preferences via parameterized optimization, a comparative analysis of multiple algorithms across different preference configurations, and a practical validation of the framework’s applicability in production cloud storage environments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108437"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven intrusion detection for UAV in Smart Urban ecosystems: A comprehensive survey 智慧城市生态系统中无人机的ai驱动入侵检测研究综述

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.comcom.2026.108435

Abdullah Khanfor , Raby Hamadi , Noureddine Lasla , Hakim Ghazzai

{"title":"AI-driven intrusion detection for UAV in Smart Urban ecosystems: A comprehensive survey","authors":"Abdullah Khanfor , Raby Hamadi , Noureddine Lasla , Hakim Ghazzai","doi":"10.1016/j.comcom.2026.108435","DOIUrl":"10.1016/j.comcom.2026.108435","url":null,"abstract":"<div><div>UAVs have the potential to revolutionize urban management and provide valuable services to citizens. They can be deployed across diverse applications, including traffic monitoring, disaster response, environmental monitoring, and numerous other domains. However, this integration introduces novel security challenges that must be addressed to ensure safe and trustworthy urban operations. This paper provides a structured, evidence-based synthesis of UAV applications in smart cities and their associated security challenges as reported in the literature over the last decade, with particular emphasis on developments from 2019 to 2025. We categorize these challenges into two primary classes: (1) cyber-attacks targeting the communication infrastructure of UAVs and (2) unwanted or unauthorized physical intrusions by UAVs themselves. We examine the potential of Artificial Intelligence (AI) techniques in developing intrusion detection mechanisms to mitigate these security threats. We analyze how AI-based methods, such as machine/deep learning for anomaly detection and computer vision for object recognition, can play a pivotal role in enhancing UAV security through unified detection systems that address both cyber and physical threats. Furthermore, we consolidate publicly available UAV datasets across network traffic and vision modalities suitable for Intrusion Detection Systems (IDS) development and evaluation. The paper concludes by identifying ten key research directions, including scalability, robustness, explainability, data scarcity, automation, hybrid detection, large language models, multimodal approaches, federated learning, and privacy preservation. Finally, we discuss the practical challenges of implementing UAV IDS solutions in real-world smart city environments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108435"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0