Computer Communications最新文献_第6页

I still know you were here: Leveraging probe request templates for identifying Wi-Fi devices at scale 我仍然知道您在这里：利用探针请求模板来大规模识别Wi-Fi设备

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.comcom.2025.108367

Daniel Vogel , Felix Viola , Nicholas Malte Kreimeyer , Daniel Bücheler , Sebastian Böhm , Michael Meier

{"title":"I still know you were here: Leveraging probe request templates for identifying Wi-Fi devices at scale","authors":"Daniel Vogel , Felix Viola , Nicholas Malte Kreimeyer , Daniel Bücheler , Sebastian Böhm , Michael Meier","doi":"10.1016/j.comcom.2025.108367","DOIUrl":"10.1016/j.comcom.2025.108367","url":null,"abstract":"<div><div>MAC address randomization in Wi-Fi Probe Requests (PRs) is supposed to ensure unlinkability for improving user privacy, but PRs still contain enough information to track or re-identify users without relying on the MAC address. This poses a risk to privacy but may also assist law enforcement in identifying devices present at a crime scene. We examine whether it is possible to separate Wi-Fi devices based on observed PRs with randomized MAC addresses using only the content and structure of these PRs. Previous work has predominantly focused on techniques for device counting using feature reduction as a means to manage data set complexity, and has failed to achieve sufficient accuracy for individual device identification. We propose an approach that leverages templates reflecting a PR’s structure to identify its influence based on vendor and device, allowing the use of more complex fingerprinting algorithms that utilize the full set of available features. To that end we examine differences between vendors and devices based on observation length, used information elements, and overlapping Fingerprints (FPs) while ignoring MAC addresses. Using PR templating, we construct a knowledge base of FPs and templates from not only existing public data sets but also a new data set published for future research. Our data set tackles critical challenges in labelling and quality in currently available data sets, and we introduce a streamlined and comprehensible crowdsourcing process including automated measurements to enable other researchers to contribute to our data set. We evaluate our device identification approach on the currently available data and demonstrate that, depending on the data set, between 75% and 85% of Wi-Fi devices can be uniquely separated within the anonymity group of devices contributing PRs to the respective data set.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"246 ","pages":"Article 108367"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618718","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

Artificial immune system-based congestion control routing for Satellite networks 基于人工免疫系统的卫星网络拥塞控制路由

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-11-07 DOI: 10.1016/j.comcom.2025.108353

Zhihan Yu, Li Zhang, Haoru Su, Wanting Zhu

引用次数: 0

Energy saving in Fixed Wireless Access networks utilizing scheduled coordinated sleeping time 利用预定的协调睡眠时间节约固定无线接入网络的能源

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-12-09 DOI: 10.1016/j.comcom.2025.108399

Ozgur Ozkaya, Jetmir Haxhibeqiri, Ingrid Moerman, Jeroen Hoebeke

{"title":"Energy saving in Fixed Wireless Access networks utilizing scheduled coordinated sleeping time","authors":"Ozgur Ozkaya, Jetmir Haxhibeqiri, Ingrid Moerman, Jeroen Hoebeke","doi":"10.1016/j.comcom.2025.108399","DOIUrl":"10.1016/j.comcom.2025.108399","url":null,"abstract":"<div><div>Fixed Wireless Access (FWA) networks are used to extend connectivity to areas with limited or no access, especially where the deployment of wired infrastructure is costly. In such networks, the infrastructure can take the form of a multi-hop mesh network consisting of Distribution Nodes (DNs) and Client Nodes (CNs). A CN is served by a DN and extends connectivity within homes by acting as an access point (AP). Due to fluctuations in traffic over time, network utilization also fluctuates. When the network is scarcely utilized, it leads to energy waste due to powering the network during these times. In this paper, we explore methods to reduce energy consumption in wireless mesh networks (WMNs) by implementing coordinated sleeping times for APs of the last hop and the DNs inside the FWA network. By dynamically scheduling sleep patterns in the last hop, the solution achieves network-wide energy savings without compromising the quality of service for traffic flows in terms of latency and reliability. Moreover, in this paper, we reduce the Orthogonal Frequency Division Multiple Access (OFDMA) overhead and utilize this to organize time-critical traffic in the last hop of FWA, benefiting from the coordinated sleeping time. Our results show that in medium-load scenarios, this approach can achieve up to 33% energy savings in FWA mesh networks combined with NextGen Wi-Fi while maintaining bounded latency for time-critical applications and serving non-time-critical traffic.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108399"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738565","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

Making cellular networks crisis-proof: Towards island-ready, resilient-by-design 6G communication networks 使蜂窝网络防危机：迈向孤岛就绪、设计弹性的6G通信网络

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-12-30 DOI: 10.1016/j.comcom.2025.108407

Leon Janzen, Matthias Hollick

{"title":"Making cellular networks crisis-proof: Towards island-ready, resilient-by-design 6G communication networks","authors":"Leon Janzen, Matthias Hollick","doi":"10.1016/j.comcom.2025.108407","DOIUrl":"10.1016/j.comcom.2025.108407","url":null,"abstract":"<div><div>5G and 5G-Advanced cellular networks are vulnerable to local outages resulting from disasters or targeted attacks. This fragility stems from the reliance on the central core network involved for most 5G connectivity use cases. Crisis-struck areas isolated from the cellular core network form <em>islands</em>, where crisis response is hindered by the unavailability of recovery-relevant services, such as emergency calls, cell broadcasts, messengers, and news apps. Our concept of island-ready, resilient-by-design 6G communication networks envisions local cellular connectivity allowing users to connect to local application servers, which is currently impossible. In our conceptualization, we follow an all-society approach, as realizing island connectivity requires the cooperation of multiple actors, including users, operators, developers, providers, and authorities. We evaluate the island readiness of 5G and 5G-Advanced systems and outline the open challenges stakeholders must address for full island readiness, such as decentralizing the 6G core network and designing local-first application architectures.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108407"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925933","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

Hierarchical Stackelberg game-based collaborative learning for ultrasound intelligence in wireless edge healthcare networks 基于分层Stackelberg游戏的无线边缘医疗网络超声智能协同学习

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-11-27 DOI: 10.1016/j.comcom.2025.108377

Fei Chen , Shalli Rani , Byung-Gyu Kim , Shakila Basheer , Huamao Jiang

{"title":"Hierarchical Stackelberg game-based collaborative learning for ultrasound intelligence in wireless edge healthcare networks","authors":"Fei Chen , Shalli Rani , Byung-Gyu Kim , Shakila Basheer , Huamao Jiang","doi":"10.1016/j.comcom.2025.108377","DOIUrl":"10.1016/j.comcom.2025.108377","url":null,"abstract":"<div><div>The integration of artificial intelligence in wireless edge healthcare networks has revolutionized medical imaging, particularly in ultrasound diagnostics, where real-time processing and privacy preservation are paramount. Traditional centralized AI approaches face substantial obstacles in healthcare applications, including communication bottlenecks, privacy violations, and inadequate resource allocation among heterogeneous medical devices. This paper introduces a novel hierarchical Stackelberg game-based collaborative learning framework for ultrasound intelligence in wireless edge healthcare networks (HSGUL), which innovatively applies Stackelberg game mechanisms to ultrasound image analysis tasks. Based on the computational heterogeneity of medical edge devices, our framework establishes dynamic gaming relationships among cloud healthcare platforms, edge medical clusters, and ultrasound diagnostic nodes through a dual-pricing fair incentive process. This creates personalized hierarchical resource allocation strategies that obtain optimal Nash equilibrium solutions for ultrasound model training, effectively guiding edge-based medical AI models toward positive acceleration. The framework operates through a three-phase Stackelberg game mechanism coordinating resource allocation and incentive distribution across the healthcare network hierarchy. Experimental validation on cardiac, abdominal, and thyroid ultrasound datasets demonstrates superior performance compared to established baseline methods. HSGUL achieves 94.73 % accuracy on cardiac ultrasound classification while reducing communication overhead by 47 % compared to centralized approaches, maintaining patient data privacy through localized edge processing and enabling scalable deployment across diverse healthcare institutions with varying computational capabilities.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108377"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658888","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

Joint optimization of UAV dual-task co-track and charging station location in large-scale IoT scenarios 大规模物联网场景下无人机双任务协同跟踪与充电站位置联合优化

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.comcom.2026.108412

Yi Zhang, Yi Hong, Chuanwen Luo, Xin Fan

{"title":"Joint optimization of UAV dual-task co-track and charging station location in large-scale IoT scenarios","authors":"Yi Zhang, Yi Hong, Chuanwen Luo, Xin Fan","doi":"10.1016/j.comcom.2026.108412","DOIUrl":"10.1016/j.comcom.2026.108412","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs), owing to their high flexibility and mobility, have emerged as efficient tools for data collection in fields such as environmental monitoring and agricultural mapping. However, their limited battery capacity significantly constrains flight range and mission duration. This limitation becomes particularly critical in large-scale Internet of Things (IoT) scenarios involving multiple cooperative UAVs. Existing studies often adopt fixed charging stations or costly mobile charging devices and treat data collection and energy replenishment as separate optimization problems, which hinders task continuity and reduces system energy efficiency. In this paper, we propose a joint optimization framework that integrates charging station placement with collaborative UAV scheduling for dual-task co-track data collection and charging, aiming to maximize data throughput and enhance energy efficiency. A multi-UAV system model is developed that incorporates various constraints, including task allocation, time, and energy. The objective is to jointly optimize the placement of fixed charging stations, the task assignments among UAVs, and the design of flight trajectories that unify data collection and charging operations. To solve this complex joint optimization problem, a path planning collaborative optimization algorithm (PCA) is designed. Simulation results show that, compared with greedy algorithms and fixed charging-station strategies, our method improves energy efficiency by about 31.28% and 15.18%, and reduces task completion time by 31.41% and 14.33%, respectively, clearly demonstrating the effectiveness and advantages of the proposed joint optimization strategy. This study offers a systematic solution for sustainable and efficient UAV-based data collection in complex operational environments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108412"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925931","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

Joint optimization of UAV trajectory, RIS selection, and offloading strategy for RIS-assisted UAV-MEC systems based on DRL 基于DRL的RIS辅助UAV- mec系统航迹、RIS选择及卸载策略联合优化

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.comcom.2025.108402

Jianhua Liu , Bo Tang , Jiajia Liu , Xia Lei , Xiaoguang Tu , Xiaofan Wang

{"title":"Joint optimization of UAV trajectory, RIS selection, and offloading strategy for RIS-assisted UAV-MEC systems based on DRL","authors":"Jianhua Liu , Bo Tang , Jiajia Liu , Xia Lei , Xiaoguang Tu , Xiaofan Wang","doi":"10.1016/j.comcom.2025.108402","DOIUrl":"10.1016/j.comcom.2025.108402","url":null,"abstract":"<div><div>The Mobile Edge Computing (MEC) system equipped with servers on Unmanned Aerial Vehicle (UAV) is an effective solution for computing offloading in the absence of communication resource infrastructure. Introducing Reconfigurable Intelligent Surfaces (RIS) into the UAV-MEC system can enhance air-to-ground communication quality and optimize Non-Line-of-Sight (N-LoS) links. However, how to dynamically select service users for the RIS based on environmental changes, so as to reduce signal interference and improve resource utilization, remains a key challenge to be addressed. To tackle this issue, we aim to jointly optimize the three-dimensional (3D) trajectory of UAV, RIS selection decisions, and task offloading strategy to minimize the overall system delay. Due to the non-convexity of the joint problem, it is difficult to solve it efficiently using traditional optimization methods. Therefore, this paper proposes a Hybrid Exploration Deep Deterministic Policy Gradient (HEDDPG) algorithm based on Deep Reinforcement Learning (DRL). By integrating random exploration into the original DDPG framework, the proposed algorithm enhances global search capability in complex environments. The experimental results show that with the addition of RIS assistance, the system delay is reduced by 18.8%. Compared to other benchmark algorithms, HEDDPG performs better, achieving efficient strategy optimization for the system and improving the performance of communication links and resource utilization efficiency.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108402"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884352","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

Making TCP IoT-friendly towards the 6G era 面向6G时代，使TCP物联网友好

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-12-27 DOI: 10.1016/j.comcom.2025.108406

Carles Gomez , Jon Crowcroft

引用次数: 0

Characterizing the performance of classification models through conformal correlation matrices 用共形相关矩阵来表征分类模型的性能

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-12-04 DOI: 10.1016/j.comcom.2025.108398

Alessandro Perlo , Carla Fabiana Chiasserini , Gustavo De Veciana , Francesco Malandrino

{"title":"Characterizing the performance of classification models through conformal correlation matrices","authors":"Alessandro Perlo , Carla Fabiana Chiasserini , Gustavo De Veciana , Francesco Malandrino","doi":"10.1016/j.comcom.2025.108398","DOIUrl":"10.1016/j.comcom.2025.108398","url":null,"abstract":"<div><div>In classification tasks, it is critical to accurately distinguish between specific classes, as misclassifications can undermine system reliability and user trust. In this paper, we study how client selection in both centralized and federated learning environments affects the performance of classification models trained on heterogeneous data. When training datasets across clients are statistically diverse, careful client selection becomes crucial to improve the ability of the model to discriminate between classes, while preserving privacy. In particular, we introduce a novel metric based on conformal prediction outcomes – the conformal correlation matrix – which captures the likelihood of class pairs co-occurring within conformal prediction sets. Unlike the traditional confusion matrix, which quantifies actual misclassifications, our metric characterizes potential ambiguities between classes, thus offering a complementary perspective on model performance and uncertainty. Through a series of examples, we demonstrate how our proposed metric can guide informed client selection and enhance model performance in both centralized and federated training settings. Our results highlight the potential of conformal-based metrics to improve classification reliability while safeguarding sensitive information about individual client data.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"247 ","pages":"Article 108398"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685748","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

Cache-assisted task offloading in Vehicular Edge Computing: A spatio-temporal deep reinforcement learning approach 车辆边缘计算中的缓存辅助任务卸载：一种时空深度强化学习方法

IF 4.3 3区计算机科学

Computer Communications Pub Date : 2026-02-01 Epub Date: 2025-11-05 DOI: 10.1016/j.comcom.2025.108351

Xiguang Li , Junlong Li , Yunhe Sun , Ammar Muthanna , Ammar Hawbani , Liang Zhao

{"title":"Cache-assisted task offloading in Vehicular Edge Computing: A spatio-temporal deep reinforcement learning approach","authors":"Xiguang Li , Junlong Li , Yunhe Sun , Ammar Muthanna , Ammar Hawbani , Liang Zhao","doi":"10.1016/j.comcom.2025.108351","DOIUrl":"10.1016/j.comcom.2025.108351","url":null,"abstract":"<div><div>Vehicular Edge Computing (VEC) faces significant challenges in jointly managing caching and task offloading due to dynamic network conditions and resource constraints. This paper proposes a novel framework that addresses these challenges through a synergistic three-stage process. The innovation lies in the tight integration of our modules: first, a Spatio-Temporal Fast Graph Convolutional Network (ST-FGCN) accurately forecasts task demands by capturing complex spatio-temporal correlations. Second, these predictions guide a Prediction-Informed Edge Collaborative Caching (PIECC) algorithm to proactively optimize resource placement across edge servers. Finally, a Genetic Asynchronous Advantage Actor–Critic (GA3C) strategy performs robust task offloading within this optimized environment. Unlike traditional reinforcement learning methods that often struggle with the large state–action spaces in VEC and converge to local optima, our framework simplifies the decision process via predictive caching and enhances exploration with the GA-infused GA3C algorithm. Simulation results demonstrate that our proposed framework significantly reduces long-term system cost, outperforms baseline methods in both latency and energy efficiency, and offers a more adaptive solution for dynamic VEC systems.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"246 ","pages":"Article 108351"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618643","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