Computer Communications最新文献

{"title":"System-level edge assistance for multi-hop NR-V2X Mode-2 vehicular networks","authors":"Mohamed Mazouzi","doi":"10.1016/j.comcom.2026.108628","DOIUrl":"10.1016/j.comcom.2026.108628","url":null,"abstract":"<div><div>This paper presents E-CLARITY, a system-level edge-assisted cluster management architecture for NR-V2X Mode 2 vehicular networks that integrates Software-Defined Networking (SDN) coordination with Roadside Unit (RSU)-hosted edge assistance to stabilize multi-hop communications while keeping vehicle-side processing lightweight. The proposed architecture explicitly accounts for Mode 2 semi-persistent scheduling (SPS) operation and urban line-of-sight/non-line-of-sight (LOS/NLOS) propagation, enabling adaptive cluster-head (CH) selection and maintenance based on connectivity and energy constraints. Unlike algorithm-centric approaches, E-CLARITY emphasizes system-level integration and standards-aligned operation under realistic NR-V2X constraints. Performance is evaluated through NS-3/SUMO co-simulation using a system-level NR-V2X Mode 2 stack with stochastic urban shadowing. Extensive results over 30 independent runs show that E-CLARITY maintains packet delivery ratio (PDR) values above 90% at high vehicle densities, achieves end-to-end delays below 70 ms across the considered speed range, and significantly reduces average vehicle-side energy consumption compared to heuristic, energy-aware, optimization-driven (CLORP), and flat NR-V2X baselines. Additional sensitivity analysis confirms robustness to <span><math><mrow><mo>±</mo><mn>20</mn><mtext>%</mtext></mrow></math></span> communication range variation and 50%–150% traffic load levels, with consistent scheme ranking across all conditions. Validation on an OpenStreetMap-based irregular urban topology further confirms comprehensive performance advantages across PDR, delay, energy consumption, and cluster stability metrics. RSU outage analysis demonstrates graceful degradation (PDR drop <span><math><mrow><mo>≤</mo><mn>4.5</mn></mrow></math></span> pp, delay <span><math><mrow><mo>≤</mo><mn>40</mn><mspace></mspace><mi>ms</mi></mrow></math></span>, CH lifetime <span><math><mrow><mo>−</mo><mn>36</mn><mtext>%</mtext></mrow></math></span>), with delay within ITS safety thresholds and recovery within 10 s. These results establish that system-level edge-assisted coordination provides a practical, resilient, and energy-aware solution for robust multi-hop clustering in realistic NR-V2X Mode 2 vehicular environments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108628"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641991","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}

{"title":"Unsupervised CoAP-based anomaly detection in private 5G core network for Industrial-IoT","authors":"Kevin Yaker,&nbsp;Boussad Ait Salem,&nbsp;Dave Appadoo,&nbsp;Nadjib Aitsaadi,&nbsp;Vivien Raynal","doi":"10.1016/j.comcom.2026.108634","DOIUrl":"10.1016/j.comcom.2026.108634","url":null,"abstract":"<div><div>In the ever-evolving Industry 4.0 landscape, logs and events play a critical role in maintaining the reliability and security of complex systems. This is especially true for 5G networks, where lightweight protocols such as CoAP and LwM2M produce massive volumes of structured but often unlabeled log data. Although machine learning and deep learning methods have become common tools for anomaly detection in such environments, they are frequently hindered by practical limitations: scarce labeled data, high-dimensionality, long log sequences, and the lack of realistic attack scenarios in available datasets. To address the above challenges, we adopt a deep clustering framework that learns low-dimensional structured latent representations without requiring labels. TF–IDF statistical analysis excels at detecting repetitive attack patterns and anomalous term frequencies characteristic of threats like distributed denial-of-service attacks and message flooding, while enabling rapid compression and interpretation of large-scale log data. However, TF–IDF alone fails to capture semantic context—for instance, distinguishing between benign timeouts and attack-induced timeout cascades. Conversely, while pre-trained models like sentence-transformer capture rich semantics, their latent spaces are optimized for classification and are often unsuitable for clustering, due to their entangled and non-topological structure. In this paper, we propose a novel hybrid architecture that fuses a TF–IDF-based autoencoder with a sentence-transformer encoder through a cross-attention mechanism. This combination leverages TF–IDF’s statistical sensitivity to repetitive attack signatures while enriching it with semantic understanding, allowing the latent representation to selectively incorporate both statistical anomalies and contextual semantic signals, thereby preserving the interpretability and clusterability of the learned space. We implement this architecture through a Wazuh-based SIEM deployment at the 5G network edge, demonstrating that unsupervised hybrid clustering can deliver effective CoAP anomaly detection in industrial IoT environments without labeled data, intrusive agents, or cloud-dependent processing addressing critical gaps in operational security for private 5G networks.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108634"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From classification to optimization: Slicing and resource management with TRACTOR","authors":"Joshua Groen,&nbsp;Zixian Yang,&nbsp;Divyadharshini Muruganandham,&nbsp;Mauro Belgiovine,&nbsp;Lei Ying,&nbsp;Kaushik Chowdhury","doi":"10.1016/j.comcom.2026.108625","DOIUrl":"10.1016/j.comcom.2026.108625","url":null,"abstract":"<div><div>5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity. The Open Radio Access Network (O-RAN) framework offers significant flexibility to reach these competing goals through network slicing and closed-loop RAN control. Central to this evolution is the integration of machine learning (ML) for dynamic network control. This paper presents a comprehensive framework designed to optimize the operation of O-RAN systems. First, we build and share a robust, O-RAN dataset generated from real-world traffic captured across diverse locations and mobility scenarios, replicated within a full-stack srsRAN-based O-RAN system using the Colosseum RF emulator. This dataset serves as a foundation for training and deploying ML models. We then introduce a traffic classification approach that leverages a variety of ML models, showcasing the ability to rapidly train, test, and refine models to improve classification accuracy. With up to 99% offline average accuracy and 92% online accuracy for specific slices, the results highlight the flexibility and efficiency of the framework in adapting to different models and network conditions. Finally, we present a physical resource block (PRB) assignment optimization strategy within our framework, which employs reinforcement learning to iteratively refine resource allocation. Our learned policy achieves a higher mean performance score (0.631) than both a manually configured expert policy (0.609) and a random allocation baseline (0.588), demonstrating improved PRB utilization. More importantly, our approach exhibits significantly lower variability, with the Coefficient of Variation (CV) reduced by up to an order of magnitude in three out of four cases, ensuring more consistent and robust performance across varying user configurations. Our contributions, including open-source access to the tools and datasets, enable accelerated research in O-RAN systems and ML-driven network control.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108625"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641992","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}

{"title":"Semantic-bit coexistence transmission for securing ISASC system with RIS-assisted symbiotic radio","authors":"Siyang Xu,&nbsp;Yang Guo,&nbsp;Zhixin Xia,&nbsp;Xin Song","doi":"10.1016/j.comcom.2026.108624","DOIUrl":"10.1016/j.comcom.2026.108624","url":null,"abstract":"<div><div>The security of the integrated sensing and semantic communication (ISASC) system based on traditional encryption and decryption algorithms heavily relies on the absolute accuracy of transmitted ciphertext bits, which offsets the robustness and fault tolerance advantages of semantic communication. To tackle this issue, we propose a semantic-bit coexistence transmission framework with reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) for physical layer security (PLS) enhancement. As the eavesdropper merely has the semantic-oriented communication structure, RIS can modulate its generated bit signal into the incident semantic signal to destroy intelligibility of the semantic data via reflect beamforming. Specifically, the superimposed signal produces a mutualistic symbiotic relationship for the intended legitimate users while inducing a parasitic interaction for the eavesdropper. Based on the constructed system model, the semantic secrecy rate (SSR) maximization problem is formulated while meeting minimum quality of service (QoS) requirements for both bit data decoding and target sensing. However, the problem faces significant challenges due to its inherent non-convexity and the strong coupling relationship between multiple variables. To further improve the PLS of the ISASC system, we develop a block coordinate descent (BCD) optimization structure by employing the introduction of slack variables, sequential convex approximation (SCA) and semidefinite relaxation (SDR) techniques, where the decomposed subproblems can be solved by deriving closed-form solutions or equivalent convex formulations. Monte Carlo simulation results show that the proposed approach achieves consistent SSR gains over the considered benchmark schemes.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108624"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641985","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}

{"title":"Performance-driven RU contention control for OFDMA-based Uplink IoT communications","authors":"Said Ould Amara,&nbsp;Mohand Yazid","doi":"10.1016/j.comcom.2026.108626","DOIUrl":"10.1016/j.comcom.2026.108626","url":null,"abstract":"<div><div>Uplink OFDMA Random Access (UORA) is the only standard protocol designed to control Uplink Multi-User Orthogonal Frequency Division Multiple Access (UL MU-OFDMA) communications in Wi-Fi 7 networks, aiming to meet the requirements of Internet of Things (IoT) environments characterized by variable and unpredictable data traffic. However, as originally defined in the IEEE 802.11ax standard, UORA still faces several challenges, despite numerous studies that have sought to properly dimension it and enhance its performance. In particular, UORA lacks effective access control mechanisms as well as efficient strategies for Resource Unit (RU) allocation and utilization, which leads to persistent RU collisions, partial allocation of available RUs, and underutilization of allocated resources. To address these issues, this paper proposes a Performance-driven RU Contention Control (P-RUCC) protocol, with the objectives of (i) controlling RU access to reduce collisions and their impact, (ii) ensuring efficient RU allocation and utilization without waste or loss of spectral efficiency, and (iii) jointly improving performance metrics that are critical for IoT applications. Performance evaluation of the proposed protocol, along with its comparison to two closely related existing protocols, H-UORA and MBTA, demonstrates its effectiveness and suitability for IoT environments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108626"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641993","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}

{"title":"Towards greener next-generation Open RAN: A comprehensive survey of O-RAN energy efficiency","authors":"Haoxin Sun,&nbsp;Almudena Díaz Zayas,&nbsp;Javier Rivas,&nbsp;Germán Corrales Madueño","doi":"10.1016/j.comcom.2026.108629","DOIUrl":"10.1016/j.comcom.2026.108629","url":null,"abstract":"<div><div>The rapid evolution of cellular networks has made them a critical infrastructure for modern society, but it has also led to quickly increasing energy consumption. Open Radio Access Network (O-RAN), with its disaggregated architecture, offers significant potential for improving energy efficiency (EE) in next-generation networks. This survey presents a comprehensive review of state-of-the-art O-RAN energy efficiency research, structured around three key dimensions: energy measurement, energy modeling, and energy optimization. We examine both software- and hardware-based measurement techniques, highlighting their strengths, limitations, and applicability on different network functions. The survey then categorizes existing energy consumption models into theoretical and empirical approaches, with a discussion of their methodologies and constraints. Finally, we explore energy optimization strategies defined by O-RAN specifications and beyond, revealing diverse technical directions in academia and industry. The survey also highlights emerging research challenges, including the lack of high-fidelity and generic datasets, the need for standardized evaluation frameworks and methodologies, and the impact of continuous O-RAN evolution on the validity of existing studies. Overall, this work aims to serve as a foundational reference for researchers and engineers to build sustainable and energy-aware O-RAN networks.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108629"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPASS: A multi-objective computational offloading framework for edge-cloud continuum in real-time industrial telemetry","authors":"Andrei García Cuadra","doi":"10.1016/j.comcom.2026.108627","DOIUrl":"10.1016/j.comcom.2026.108627","url":null,"abstract":"<div><div>This paper presents COMPASS (COmputationally-Managed Pareto-Aware Scheduling System), a multi-objective computational offloading framework for real-time telemetry in industrial edge-cloud continuum architectures. COMPASS jointly optimizes latency, energy consumption, and data fidelity through a lightweight Pareto-front approximation heuristic with <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> per-decision complexity and empirically validated convergence. The framework integrates three components on a four-tier hierarchical architecture spanning on-vehicle edge processors, trackside fog nodes, regional servers, and cloud infrastructure: (C1) a multi-objective offloading algorithm with Pareto-aware utopia-point selection, (C2) an entropy-based adaptive sampling strategy with a formal fidelity bound, and (C3) a multi-tier graceful degradation protocol with bounded performance under node failures. The system is field-validated on a high-speed motorsport platform processing over 100 sensor channels at aggregate rates exceeding 50<!--> <!-->Mbps under intermittent 5G connectivity. Field deployment demonstrates sub-50<!--> <!-->ms 99th-percentile latency for critical edge-fog paths (22.4<!--> <!-->ms mean), 99.7% data delivery reliability, and 99.92% system availability over 120 h across 2.4<!--> <!-->TB of telemetry. Separately, simulation-based algorithm evaluation against seven baseline offloading strategies — including NSGA-II and a capacity-aware greedy — shows COMPASS achieves the best hypervolume indicator with 0% deadline misses for safety-critical tasks, while the field-validated infrastructure reduces on-vehicle energy by 73% relative to commercial baselines. The architecture transfers to smart manufacturing, autonomous vehicle sensor fusion, and industrial IoT deployments requiring predictable quality of service.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108627"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SD-SAC: An intelligent DRL approach for QoS-aware atomic task offloading in hybrid LEO satellite-edge networks","authors":"Syed Saqib Jamal,&nbsp;Wang-Cheol Song","doi":"10.1016/j.comcom.2026.108623","DOIUrl":"10.1016/j.comcom.2026.108623","url":null,"abstract":"<div><div>The increasing computational demands of Internet of Things (IoT) devices require efficient Mobile Edge Computing (MEC) strategies, especially in remote and underserved areas. Traditional terrestrial MEC solutions often encounter challenges such as computational overload and latency issues. This paper introduces a novel intelligent Deep Reinforcement Learning (DRL)-based approach, named Software-Defined Soft Actor-Critic (SD-SAC), specifically tailored for hybrid Low Earth Orbit (LEO) satellite-edge networks. The proposed framework integrates hierarchical Software-Defined Networking (SDN) with DRL to dynamically optimize atomic task offloading decisions. Unlike conventional methods, our solution categorizes satellites into computational edge nodes and non-computational relay nodes, allowing for optimized resource utilization and enhanced task processing. The SD-SAC algorithm employs real-time network state monitoring and adaptive decision-making to effectively balance computational loads, reduce latency, and maximize the task success rate. Comprehensive simulations validate that the SD-SAC framework significantly outperforms traditional offloading methods by improving Quality of Service (QoS) satisfaction, minimizing delays, and ensuring efficient CPU and bandwidth utilization. This research offers a robust solution for managing computational tasks in dynamic satellite-edge computing environments, demonstrating clear advantages in QoS-aware task atomic offloading strategies.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108623"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641990","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}

{"title":"Semantic intent translation and policy synthesis using multi-agent deep reinforcement learning for next-generation networks","authors":"Dr. D. Syinthiya,&nbsp;A. Kaliappan,&nbsp;A. Syed Haroon,&nbsp;S.P. Santhoshkumar,&nbsp;S. Ponni Alias Sathya,&nbsp;M. Kasiselvanathan","doi":"10.1016/j.comcom.2026.108631","DOIUrl":"10.1016/j.comcom.2026.108631","url":null,"abstract":"<div><div>Cognitive systems must transform high-level user intents into network rules that maintain quality of visual data transmission in next-generation networks to support increasingly advanced visual communication services. Intention-based networking systems generally include semantic ambiguity in visual quality standards, delayed policy synthesis for dynamic video streams, and poor network adaptability. These constraints hinder policy-driven orchestration for immersive multimedia applications and autonomous management of visual communication infrastructure. Therefore, an intelligent and adaptive method is needed to effectively analyze the semantic intentions of visual content and improve network regulations. This paper proposes a COGVIS-MADRL (Cognitive Visual Semantic Multi-Agent Deep Reinforcement Learning) framework with Semantic Intent Translation and Policy Synthesis, based on Multi-Agent Deep Reinforcement Learning (MADRL), for next-generation networks supporting visual communication services. Semantic intent parsing targets visual quality metrics such as resolution, latency tolerance, and region-of-interest prioritization, and a novel Semantic-Aware Multi-Agent Deep Deterministic Policy Gradient (SA-MADDPG) algorithm optimizes the distributed policy. The agents observe a hybrid state representation that combines semantic embeddings of user intent with visual cues from encoded video streams for content-aware policy synthesis. Multiple cooperative agents interact with the network environment and receive incentive feedback on policy success, visual quality preservation, resource usage, and compliance with the video traffic service-level agreement to develop optimal policy-generation techniques. Testing on a testbed streaming adaptive 4K video content shows that the proposed COGVIS-MADRL framework improves intent translation accuracy (correctly interpreting visual QoE requirements), policy deployment latency (33.2%), and network resource utilization (26.4%). The findings show that the COGVIS-MADRL-driven framework synthesizes scalable, adaptive, and intelligent policy for visual data pipelines, allowing autonomous administration of next-generation visual communication infrastructures.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108631"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656496","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}

{"title":"A Boundary-Aware Routing Protocol Utilizing Game Theory for Flying Ad-Hoc Networks","authors":"Huizhi Tang,&nbsp;Abdul Rauf,&nbsp;Yihong Zhang","doi":"10.1016/j.comcom.2026.108630","DOIUrl":"10.1016/j.comcom.2026.108630","url":null,"abstract":"<div><div>Flying Ad-Hoc Network (FANET) consists of unmanned aerial vehicles (UAVs) that cooperatively establish wireless communication networks. It has been widely used in communications, transportation, forest fire monitoring and other fields. Due to the highly dynamic topology and boundary disconnection problems in three-dimensional (3-D) environments, designing an efficient routing protocol for FANETs remains challenging. To address these issues, this paper proposes a Boundary-Aware Routing Protocol utilizing Game Theory (BARP-GT). Firstly, BARP-GT defines a 3-D forwarding area by considering network boundary effects. Then, it estimates node degree in different boundary scenarios without relying on Hello message broadcasting, which reduces routing overhead and energy consumption. In addition, the forwarding process of route request packets is modeled as a static game to derive an adaptive forwarding probability and suppress broadcast storms. Simulation results based on Network Simulator-2 (NS-2) demonstrate that BARP-GT achieves better performance than iPipe, QEHLR, and BLPR in terms of end-to-end delay, packet delivery ratio, packet drop ratio, and routing load.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"257 ","pages":"Article 108630"},"PeriodicalIF":4.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736482","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}