International Journal of Communication Systems最新文献

{"title":"Energy-Aware and Traffic-Optimized Routing for MANETS in Sustainable Smart Cities","authors":"S. Muthumarilakshmi,&nbsp;Thilagavathi Narayanamoorthi,&nbsp;D. Vishnu Sakthi,&nbsp;M. R. Akshara","doi":"10.1002/dac.70533","DOIUrl":"https://doi.org/10.1002/dac.70533","url":null,"abstract":"<div>\u0000 \u0000 <p>In Mobile Ad Hoc Networks (MANETs), ensuring energy efficiency and optimizing traffic flow present significant challenges, especially in smart city environments characterized by limited resources and dynamic node mobility. Leveraging real-time traffic analysis and adaptive energy management, the proposed method ensures balanced energy consumption while maintaining optimal routing performance. Unlike traditional fixed-weight routing models, our protocol employs an adaptive weighting mechanism where the routing parameters alpha, beta, and gamma dynamically adjust based on real-time network conditions including node energy levels, traffic congestion patterns, and mobility dynamics. This adaptive approach automatically prioritizes energy conservation when battery levels are critically low, emphasizes traffic load balancing during congestion, and optimizes path length when routing efficiency becomes paramount. This fundamental distinction from fixed-weight models enables superior performance across varying network conditions without manual parameter tuning. Extensive simulations conducted using NS-3 simulator under varying node densities, mobility patterns, and traffic loads demonstrate that the proposed protocol significantly reduces energy consumption, improves packet delivery ratio, increases throughput, and extends network lifetime compared to conventional approaches. The results highlight its practical applicability in smart city scenarios, contributing to sustainable urban development by facilitating efficient and reliable communication in MANETs. This work advances energy-aware networking and supports scalable, intelligent communication for next-generation urban systems.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Improving Security in Software-Defined Networks Through an Optimized Support Vector Machine Model for Detecting Distributed Denial-of-Service Attacks","authors":"Sadia Khan,&nbsp;Syed Muhammad Sheraz,&nbsp;Muhammad Faraz Hyder,&nbsp;Zehra Batool,&nbsp;Ayesha Najeeb","doi":"10.1002/dac.70537","DOIUrl":"https://doi.org/10.1002/dac.70537","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, software-defined networking (SDN) has emerged as one of the modern networks, offering a centralized control for managing and controlling the network. This centralization in SDN makes it vulnerable to distributed denial of service (DDoS) attacks, leading to network damage and unavailability. This research presents a novel approach to detect the DDoS attacks in real-time SDN environment using the support vector machine (SVM) model, enhanced by kernel principal component analysis (KPCA) and genetic algorithm (GA). KPCA is employed to reduce the dimensions of the network traffic features and to effectively capture the nonlinear patterns, while minimizing the computational complexity. To optimize the SVM hyperparameters and to improve the accuracy of the proposed model, a GA is used. The proposed model is deployed on a customized Ryu controller incorporating FlowTracker for real-time OpenFlow feature extraction, KPCA and StandardScaler for transformation, and an optimized SVM classifier with high-priority flow rules for instantaneous attack mitigation. The system is rigorously evaluated on the CIC-DDoS2019 dataset as well as live SDN traffic generated in a Mininet + Ryu environment. Results indicate that the proposed approach is successful and efficient with high detection accuracy, and a very low false positive rates, as compared to the other traditional machine learning models.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Customized Chimp With Blue Monkey Optimization (CCBMO)-Based Secure-Aware Cluster-Based Routing in Wireless Sensor Network","authors":"Y. Basanthi,&nbsp;K. Kalaiselvi,&nbsp;V. Senthil Murugan","doi":"10.1002/dac.70531","DOIUrl":"https://doi.org/10.1002/dac.70531","url":null,"abstract":"<div>\u0000 \u0000 <p>One of the most well-known routing systems is cluster-based routing, where the head node collects information from every other cluster node, performs specific aggregation functions, and afterwards delivers the aggregated information towards the base station. Motivated by that, we have developed a novel cluster-based routing approach that is comprised of two working phases which are optimal cluster head selection as well as routing. Cluster-based routing reduces the energy consumption of sensor nodes by organizing them into clusters, allowing data to be aggregated before transmission, which minimizes redundant data communication. The suggested approach allows the network to dynamically react to shifting conditions, such as node failures, mobility, and changes in ambient parameters. In this first phase, the suitable cluster head is selected using the hybridized optimization named customized chimp with blue monkey optimization (CCBMO), by considering the parameters like energy, distance as well as delay. After the selection of the optimal cluster head, routing is done which uses factors such as packet delivery ratio (PDR) and security for the optimal routing process.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"LUD-YOLO Based Lightweight Object Detection With Semantically Aware Re-Pathing Model for Multi-Sensor Drone-Based Indoor Object Tracking System","authors":"Vijaya Lakkannavar,&nbsp;Om Prakash Verma,&nbsp;Sheela Tiwari","doi":"10.1002/dac.70535","DOIUrl":"https://doi.org/10.1002/dac.70535","url":null,"abstract":"<div>\u0000 \u0000 <p>Drones, also referred to as unmanned aerial vehicles (UAVs), have evolved into an effective solution for autonomous detection and data collection in indoor spaces without the need for human involvement. It was more suitable for crucial applications, but there are several impacts faced in indoor surveillance, like battery power constraints, unreliable communication, and poor maintenance of coverage across large indoor areas. In order to improve the performance of indoor surveillance, several AI-based approaches are used for detecting objects and ensuring drone pathing according to the targeted object in indoor environments with obstacle avoidance. However, these methods include the inability to accommodate very dynamic indoor environments, where they do not have the ability to find unexpected occurrences of obstacles in real-time and the lack of reliability in low light or sensor-degradation scenarios, which limit the resilience of indoor drone surveillance systems. To overcome these issues, a lightweight UAV detection–You Only Look Once based object detection with a deep reinforcement learning method was implemented for re-planning the drone's path to avoid collision. Initially, the drone was designed with no GPS for indoor applications. The drone contains two sensors, namely, stereo cameras and light detection and ranging. The stereo cameras are used for capturing images of indoor environments. The LIDAR generates a 3D point cloud of the indoor environments, and it shows the objects' distances and shapes for exact mapping. Here, the hybrid AI model was implemented for object detection and rephasing the drone. The lightweight UAV detection–You Only Look Once (LUD-YOLO [C3K2]) detects objects based on the stereo cameras' images. After that, semantically driven deep reinforcement learning (SD-DRL) was used for re-pathing the drone based on LIDAR distance measuring if the obstacles are detected in the moving path of the drone. Finally, the proportional-integral-derivative controller adjusted the drone's motion through its pitch, yaw, thrust, and roll for stable collision-free navigation in indoor environmental monitoring. The proposed LUD-YOLO (C3K2) model achieves accuracy, precision, recall, and specificity values of 97.01%, 89.72%, 84.93%, and 88.67%. Additionally, the proposed reinforcement learning-based deep deterministic policy gradient model attains an average reward comparison value of 320, a root mean squared error value of 1.2, and a success rate value of 86%. The proposed model attains accurate object detection and efficient re-pathing without collisions, ensuring reliable surveillance in indoor environments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Responsible AI-Driven Optimized Cross-Hierarchical Structure-Detail-Aware Cascaded Capsule Neural Network for Intelligent 6G-IoT Attack Detection","authors":"N. SreeKanth,&nbsp;K. Padmanaban,&nbsp;R. Giri Prasad,&nbsp;L. Guganathan","doi":"10.1002/dac.70525","DOIUrl":"https://doi.org/10.1002/dac.70525","url":null,"abstract":"<div>\u0000 \u0000 <p>Intrusion detection systems (IDSs) for sixth generation (6G) internet of things (IoT) networks often suffer from low adaptability, high false alarm rates, and limited ability to handle complex and evolving traffic. Traditional deep learning models struggle to capture spatial and temporal attack patterns, and gradient-based optimizers often get stuck in local minima, reducing detection accuracy. To overcome these challenges, this work proposes an optimized cross-hierarchical structure-detail-aware cascaded capsule neural network (CS2C2N-GBFIO) for efficient and responsible attack detection in responsible artificial intelligence (AI)–enabled 6G-IoT networks. Initially, real-world IoT traffic datasets are collected. The Quality-aware fuzzy min-max neural network (QF2MNet) is used to ensure clean, normalized, and multiview feature representation. Subsequently, the Hybrid Divine Religions and Catch Fish Optimization (DRA-CFOA) algorithm is used to select the features that are the most discriminative and nonredundant. This is followed by the cross-hierarchical structure-detail-aware cascaded capsule neural network (CS2C2N) that learns both structural and fine-grained features of traffic behavior and Grizzly Bear Fat Increase Optimization (GBFIO) that learns hyperparameters to obtain optimal convergence. The experiments of Real-Time Internet of Things 2022 (RT-IoT2022) and Canadian Institute for Cybersecurity Internet of Things 2023 (CICIoT2023) datasets demonstrate superior performance, achieving 99.36% accuracy, 99.43% F1 score, and a false alarm rate (FAR) of 1.30%. The low computational cost, scalability of the model, and adaptability in real-time make it appropriate to securely implement the model at fog and edge layers of contemporary 6G-IoT infrastructures.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Quokka Swarm Optimized Clifford-Steerable Dynamic Graph Attention Network for Wireless SDN Traffic Control","authors":"S. Kavitha Bharathi,&nbsp;M. Mathivanan,&nbsp;A. Mohamedyaseen,&nbsp;Poovendran Alagarsundaram","doi":"10.1002/dac.70532","DOIUrl":"https://doi.org/10.1002/dac.70532","url":null,"abstract":"<div>\u0000 \u0000 <p>Software-defined wireless networks (SDWNs) have emerged as a groundbreaking approach to managing advanced network environments, enabling centralized control and dynamic traffic management to deliver optimal performance. The highly advanced distributed denial-of-service (DDoS) attacks and growing network demands are signs of constant scalability, latency control, and effective routing issues during high load. Given these difficulties, this paper proposes a new system to address the shortcomings of traditional approaches. QCD-Net (Quokka-optimized Clifford-steerable dynamic graph network) for wireless SDN traffic control proposes a new comprehensive approach that starts with adequate data collection from the DDOS attack SDN dataset and stringent preprocessing using min–max <i>Z</i>-score normalization to maintain homogeneity. The most important characteristics are acquired using the uncertainty-aware decision transformer, which identifies the major traffic behaviors and uncertainties. The inputs are fed into a software-defined networking (SDN) controller with a traffic-predicting unit based on a Clifford-steerable dynamic graph attention network, encompassing both spatial and temporal network behavior. Routing decisions are optimized by the traffic routing unit using Quokka swarm optimization for adaptive load balancing with low latency. The performance of experimental testing is excellent, with a prediction accuracy of 99.5, routing efficiency of 98.7, detection rates of 99.2, and overall network performance improvements of 99.0. In general, the QCD-Net framework should be regarded as a significant improvement in network resilience, scale, and efficiency in a dynamic, attack-susceptible environment, paving the way for further improvements in wireless SDN traffic management and laying the foundations for future adaptive network management solutions.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 High-Resolution DOA Estimation Method by Compressive-Sensing Based Sparse Minimum Redundancy Linear Array for IoT Sensory Networks","authors":"Chethana Periyapatna Nagaraj,&nbsp;Saurav Ganguly","doi":"10.1002/dac.70527","DOIUrl":"https://doi.org/10.1002/dac.70527","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;IoT sensors are increasingly being utilized for direction of arrival (DOA) estimation, which is crucial for tracking signal sources and enhancing localization in various applications, particularly in wireless sensor networks. The ability of IoT sensors to retrieve direction information from the received electromagnetic waves enables them to effectively function in heterogeneous environments, thus facilitating more accurate monitoring and localization of sources. In recent years, linear antenna arrays, with spatially sparse positioning of elements has evolved as one of the most significant arrangement of arrays, in IoT sensory networks intended for source localization or DOA estimation of targets in the far field region. Sparse arrays yield with a higher degree of freedom (DOF) and can produce a larger antenna aperture as compared to uniform linear arrays (ULA). Various kinds of linear sparse arrays have been familiarized and researched in the last decade, such as co-prime array, nested array, super nested array, etc. The minimum redundancy linear array (MRLA) structure is a class of sparse arrays introduced few decades ago and is essentially a linear array arrangement, which reduces the redundant spacings between the array elements, to attain maximum resolution. Along with the investigation of sparse and nonuniform array geometries, a new paradigm of sparse signal reconstruction of underdetermined system of linear equations became apparent in the last decade, known as compressive sensing methodology. Quite a few benefits have transpired with compressive sensing technique in array signal processing, including a significantly reduced requirement of sensory devices, a substantial reduction in memory storage, a higher data transfer rate, and a considerable reduction in computational complexity. In this paper, an enhanced DOA estimation technique is proposed by integrating the MRLA sensory structure with the compressive sensing framework for single snapshot instance. The MRLA model's low-dimensional kernel compresses incoming signals to preserve the large array aperture. The high-resolution DOA estimation method is subsequently executed using these compressed signals through a proposed modified &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ℓ&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {ell}_1- $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;norm-based reconstruction process. Several simulation experiments with various parameters are performed and compared with the popular standard models to verify the performance and ascendancy of the proposed method, such as computational complexity, failure rate, root mean square error (RMSE) of the estimated DOA, probability of resolution, with variation of signal-to-noise ratio (SNR) values, etc. The comparative results","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148203062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Energy-Efficient Cluster-Based Routing in Wireless Sensor Networks Using Fuzzy-Reinforcement Whale Optimization","authors":"K. Mohaideen Pitchai","doi":"10.1002/dac.70536","DOIUrl":"https://doi.org/10.1002/dac.70536","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless sensor networks (WSNs) have attracted significant attention from researchers due to their wide range of environmental monitoring applications and their ability to adapt and integrate seamlessly with various advanced technologies. However, the limited computational capabilities and energy-constrained batteries of sensor nodes present major challenges that restrict the broader deployment of WSNs. Clustering has emerged as a key technique to mitigate these challenges, as cluster-based routing significantly reduces the energy consumption of nodes. The proposed work introduces a hybrid algorithm named Fuzzy-Reinforcement Whale-Optimized Clustered Routing (FRWCR), aimed at enhancing energy-efficient routing through intelligent clustering. FRWCR leverages fuzzy logic for the selection of energy-efficient cluster heads, employs Q-learning for adaptive and efficient cluster formation, and utilizes the whale optimization algorithm (WOA) to determine the most energy-efficient path from the source node to the base station. Simulation results demonstrate that FRWCR outperforms three existing related algorithms in terms of lower average energy consumption, extended network lifetime, higher throughput, improved packet delivery ratio, and reduced routing overhead.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148203060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Energy-Efficient Trust-Based Routing Using Fuzzy–Ant Colony Optimization in Mobile Ad Hoc Networks","authors":"R. Rajan,&nbsp;Yosuef Alotaibi,&nbsp;A. Sathiyaraj,&nbsp;B. Dwarakanath,&nbsp;Barkathulla A,&nbsp;Pandimurugan V","doi":"10.1002/dac.70529","DOIUrl":"https://doi.org/10.1002/dac.70529","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile Ad Hoc Networks (MANETs) are highly vulnerable to security threats due to their dynamic topology, open wireless communication medium, and lack of centralized control. To address these challenges, this work proposes a trust-aware Fuzzy-Ant Colony Optimization (Fuzzy–ACO) routing framework that employs an interconnected framework in which fuzzy clustering organizes nodes into clusters, the intrusion detection module identifies malicious nodes, and a trust evaluation mechanism quantifies node reliability. The proposed components collectively guide the Ant Colony Optimization (ACO)–based routing process to establish secure and energy-efficient communication paths. The proposed approach is evaluated through extensive simulations and compared against well-established benchmark routing schemes, including AODV, DSR, and conventional ACO-based routing protocols, which are widely used as baselines for performance evaluation in MANET research. Performance analysis is conducted using key metrics such as packet delivery ratio, end-to-end delay, energy consumption, and routing overhead under varying network sizes, node densities, mobility conditions, and attack scenarios. Simulation results demonstrate that the proposed Fuzzy–ACO framework consistently outperforms the benchmark schemes by achieving higher packet delivery ratio, reduced energy consumption, and improved resilience against malicious attacks while maintaining low computational overhead suitable for resource-constrained MANET environments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"BBR-G: A Novel End-to-End Congestion Control Protocol for Optimum Throughput","authors":"Midhula K S,&nbsp;Arun Raj Kumar P","doi":"10.1002/dac.70534","DOIUrl":"https://doi.org/10.1002/dac.70534","url":null,"abstract":"<div>\u0000 \u0000 <p>The Bottleneck Bandwidth and Round-trip propagation time (BBR) protocol has recently been adopted on the Internet to achieve high sending rates, especially for multimedia applications. However, the gradual increase in sending rate overloads the bottleneck link, leading to persistent queues, higher queuing delay, increased packet loss, and overall network performance degradation. Further, intra-protocol unfairness limits BBR's effectiveness in congestion control. In this paper, we address these limitations by proposing an adaptive pacing gain mechanism that dynamically responds to RTT variations. We formulate a multi-variable optimization objective that jointly considers RTT variation, queuing delay, and pacing gain. The proposed BBR-G is implemented in the ns-3 network simulator and further validated using Mininet experimentation. The ns-3 experiments are conducted across multiple topologies (many-to-one and many-to-many) and varied bottleneck bandwidth networks. The experimental results show that BBR-G improves average network throughput up to 50.77%, reduces bottleneck queue occupancy up to 50.84%, and decreases delay up to 45.2% than BBR, BBR-A, FEBBR, Vegas, and CUBIC. The <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math>-test-based statistical validation of throughput alongwith ablation study and sensitivity analysis demonstrate the robustness of the proposed BBR-G in simulation environments. Further, Mininet-based emulation experiments are conducted on a shared bottleneck dumbbell topology with multiple concurrent flows to validate the practicality of the proposed approach. The experimental results demonstrate that BBR-G achieves improved throughput and reduced latency while maintaining better fairness compared to existing congestion control algorithms.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}