Concurrency and Computation-Practice & Experience最新文献

{"title":"Predictive VNF Deployment With Virtual Network Mapping Using SDN/NFV-Enabled UAV Swarms","authors":"Qizhao Zhou,&nbsp;Zhongyu Shi","doi":"10.1002/cpe.70229","DOIUrl":"https://doi.org/10.1002/cpe.70229","url":null,"abstract":"<div>\u0000 \u0000 <p>Unmanned Aerial Vehicle (UAV) networks are emerging as pivotal enablers for supporting Network Function Virtualization (NFV) and Software-Defined Networking (SDN) services, particularly in meeting the diverse and stringent virtual network function (VNF) scheduling demands of future communication networks. However, a fundamental challenge arises from the SDN controller's inability to synchronize resource request information from VNFs in real time, potentially causing significant delays in mapping and scheduling strategies, especially for delay-sensitive UAV network services. To address this challenge, this paper introduces a predictive VNF deployment model, seamlessly integrated with virtual network mapping, designed to operate within constraints such as the ordered sequence of VNFs, delay requirements, and service arrival time. In recognition of the dynamic nature of UAV services, our framework incorporates VNF live migration and rescheduling. Consequently, we formulate the VNF mapping and scheduling challenge as a predictive long-term lateral resource optimization problem, leveraging Long Short-Term Memory (LSTM) techniques. By employing digital twin (DT)-based virtual network mapping, the SDN controller gains precise insights into the UAV's VNF resource demands, thereby effectively addressing service acceptance issues within VNF mapping and scheduling policies. Our simulation resultsdemonstrate that the proposed method achieves superior outcomes in terms of total benefit, network service acceptance rate, and average delay within the digital twin system. This approach not only enhances the operational efficiency of UAV networks but also ensures robust and timely service delivery in complex network environments, thereby contributing to the advancement of UAV-based NFV and SDN services.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740147","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":"Enhancing the Harris Hawks Optimization Algorithm With Ambush-Based Operators for Feature Selection in UAV-Based Intrusion Detection Systems","authors":"Sayed Zabihullah Musawi,&nbsp;Mohammad Farshi,&nbsp;Sepehr Ebrahimi Mood,&nbsp;Alireza Souri","doi":"10.1002/cpe.70207","DOIUrl":"https://doi.org/10.1002/cpe.70207","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous vehicles (AVs), including drones, rely on sensors, machine learning algorithms, and large datasets for perception, decision-making, and control. However, the high dimensionality of these datasets increases computational load and hampers real-time performance. In Unmanned Aerial Vehicle (UAV) systems, feature selection is critical for reducing complexity and enhancing processing efficiency, thereby enabling faster and more accurate decision-making. In this study, we enhance the Harris Hawks Optimization (HHO) algorithm by introducing a novel ambush-based operator to regulate selection pressure, resulting in an improved variant named AMHHO. The effectiveness of AMHHO is validated using IEEE CEC2019 benchmark functions and compared against several well-known optimization algorithms. To further evaluate its robustness, ablation studies and sensitivity analyses are conducted to identify the most efficient AMHHO variants. Furthermore, a binary version of AMHHO (BAMHHO) is applied to ten high-dimensional datasets and the UAV-IDS-2020 dataset for feature selection and classification tasks. BAMHHO is assessed based on classification accuracy, fitness value, feature selection ratio, and computation time, demonstrating superior performance across multiple datasets and outperforming state-of-the-art methods. To rigorously evaluate the statistical significance of its results, Wilcoxon Signed-Rank test is applied to compare BAMHHO with other well-known algorithms, confirming the statistical superiority of BAMHHO. In conclusion, BAMHHO not only achieves effective performance on high-dimensional datasets but also achieves 100% classification accuracy on the UAV-IDS-2020 dataset, all while maintaining an optimal balance between feature reduction and computational efficiency. These findings confirm BAMHHO's effectiveness in handling high-dimensional data and highlight its potential for application in UAV-based intrusion detection systems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740432","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":"Profiling and Optimization of Multicard GPU Machine Learning Jobs","authors":"Marcin Lawenda,&nbsp;Kyrylo Khloponin,&nbsp;Krzesimir Samborski,&nbsp;Łukasz Szustak","doi":"10.1002/cpe.70196","DOIUrl":"https://doi.org/10.1002/cpe.70196","url":null,"abstract":"<div>\u0000 \u0000 <p>The article discusses various model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition are analyzed, adapted to different hardware and software configurations, including distributed data parallelism and distributed hardware processing. Changing the tensor layout in PyTorch DataLoader from NCHW to NHWC and enabling <i>pin</i>_<i>memory</i> has proven to be very beneficial and easy to implement. Furthermore, the impact of different performance techniques (DPO, LoRA, QLoRA, and QAT) on the tuning process of LLMs was investigated. LoRA allows for faster tuning, while requiring less VRAM compared to DPO. On the other hand, QAT is the most resource-intensive method, with the slowest processing times. A significant portion of LLM tuning time is attributed to initializing new kernels and synchronizing multiple threads when memory operations are not dominant.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681077","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":"Privacy-Secure Asynchronous Federated Multimodal Pedestrian Trajectory Prediction Models","authors":"Liu Kun,&nbsp;Wenbo Zhou,&nbsp;Wang Hui,&nbsp;Zihao Shen,&nbsp;Peiqian Liu","doi":"10.1002/cpe.70201","DOIUrl":"https://doi.org/10.1002/cpe.70201","url":null,"abstract":"<div>\u0000 \u0000 <p>In distributed contexts, pedestrian trajectory prediction faces data silos, making cross-scene data sharing difficult. Centralised training and synchronised federated learning pose risks of privacy breaches, as attackers may extract sensitive information through model inversion techniques. This paper presents a privacy-secure asynchronous federated multimodal pedestrian trajectory prediction model (AFed-MTP) to enhance global update efficiency and reduce reliance on delayed nodes through dynamic aggregation, as traditional synchronous training diminishes efficiency due to discrepancies in node performance, resulting in postponed global updates that affect real-time applications. This scheme introduces a multimodal trajectory prediction model based on generative adversarial networks (GAN-MTP) for each scenario, integrating spatiotemporal graph networks with a generative adversarial framework to generate multimodal trajectories during localised training, thereby reducing data leakage and ensuring strong privacy protection. Experimental results show that this scheme outperforms the method trained directly across various scenarios regarding data privacy security, with the mutual information value reduced to 0.018 by replacing real data with locally predicted trajectories, thereby improving privacy protection efficacy by 25%. In decentralised contexts, the ADE prediction errors for the FD1 and FD2 datasets decrease significantly compared to previous methodologies by 31.7% and 31.9%, respectively. This framework strikes a balance between privacy preservation and predictive accuracy, offering practical and safe solutions for applications such as autonomous driving and smart cities.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672827","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":"Quality of Service Enhancement in Mobile Crowdsensing Through Metaheuristic Techniques: A Survey","authors":"Hadi Ghahremani,&nbsp;Masumeh Damrudi,&nbsp;Ali Ghaffari,&nbsp;Kamal Jadidy Aval","doi":"10.1002/cpe.70168","DOIUrl":"https://doi.org/10.1002/cpe.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile crowdsensing (MCS) has emerged as a promising paradigm leveraging the widespread availability of mobile devices for large-scale data collection. Ensuring high quality of service (QoS) in MCS is paramount for its effectiveness and reliability. This survey reviews the application of metaheuristic optimization algorithms to enhance QoS in MCS systems, with a focus on adaptive and hybrid optimization techniques for real-time applications. We discuss key QoS metrics, such as accuracy, latency, and reliability, and outline the challenges inherent in maintaining these metrics, including scalability, adaptability to dynamic environments, and energy efficiency. The survey provides a comprehensive overview of various metaheuristic algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA), evaluating their applicability and potential in MCS contexts. Through a systematic review of the literature, we highlight recent advancements and practical implementations of these algorithms, presenting comparative insights and case studies to illustrate their effectiveness in addressing QoS challenges.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666431","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":"Enhancing Wireless Sensor Durability via On-Demand Mobile Charging and Energy Estimation","authors":"Dinesh Dash,&nbsp;Rupayan Das,&nbsp;Chandra Bhushan Kumar Yadav","doi":"10.1002/cpe.70205","DOIUrl":"https://doi.org/10.1002/cpe.70205","url":null,"abstract":"<div>\u0000 \u0000 <p>In wireless rechargeable sensor networks (WRSN), wireless energy charging (WEC) is a potential approach to extend sensor lifetime. To continuously provide electric charge to sensors, WEC uses a mobile charger (MC). All things considered, creating a charging plan that works for the MC is difficult because it depends on various aspects such the amount of energy left, the location of the limitations, and the time of day. The purpose of this work is to offer a novel and efficient charging process to extend the life of sensors in WRSN. According to this algorithm, the sensors periodically transmit to the service station (SS) the energy spending rate and their remaining energy. The SS estimates how long the sensors will last, and if it falls below a predetermined level, that sensor is taken into consideration for charging and is placed in a serving queue. After that, the SS schedules MC using a suggested priority function. Comparing the suggested method to baseline charging techniques, simulation experiments show that it performs better in terms of charging, especially in terms of prolonging the lifetime of sensors. The experimental results demonstrate that the suggested method outperforms the state-of-the-art approaches, in achieving a superior average dead period for sensors.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666433","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":"Enhanced YOLOv7 for EMU Damage Detection: Overcoming False Detection and Data Scarcity by Network Optimization and AIGC","authors":"Jintao Liu,&nbsp;Wenjin Xue,&nbsp;Wei Liu,&nbsp;Guowei Xu,&nbsp;Jian Liu","doi":"10.1002/cpe.70200","DOIUrl":"https://doi.org/10.1002/cpe.70200","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of advancements in Artificial Intelligence Generated Content (AIGC) technology, this study focuses on addressing the challenges of data scarcity and high false negative rates in the detection of damage for Electric Multiple Units (EMU). To overcome these challenges, we propose an enhanced You Only Look Once version 7 (YOLOv7) model. First, we utilize the Low-Rank Adaptation (LoRA) lightweight training strategy, which utilizes a small number of actual damage images (such as foreign objects, oil leaks, and scratches). We also use the Stable Diffusion model to generate additional high-quality damage samples, effectively enriching the training dataset. Second, we incorporate the Coordinate Concatenated Spatial(CS) Attention mechanism into the YOLOv7 backbone network, adaptively adjusting channel and spatial attention weights to improve the model's ability to detect targets in complex backgrounds while maintaining a lightweight design. Third, we decouple the detection head to independently perform classification and localization tasks. Finally, we introduce the Focal-Efficient Intersection over Union (Focal-EIOU) loss function to optimize gradient allocation during training, promoting rapid convergence of high-quality anchor boxes and improving bounding box prediction accuracy. Experiments conducted on a dataset of 3717 generated images demonstrate that the improved YOLOv7 model achieves Mean Average Precision (mAP) and Recall rates of 98.0% and 96.1%, respectively, representing significant improvements over other YOLO models.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666432","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 Framework for Parallel Segmentation of Lung Nodule Images Based on Reinforcement Learning Enhancement With Multiple Agents","authors":"Jiahui Liu,&nbsp;Zhe Liu,&nbsp;Baiqiang Hu,&nbsp;Zeling Hou","doi":"10.1002/cpe.70198","DOIUrl":"https://doi.org/10.1002/cpe.70198","url":null,"abstract":"<div>\u0000 \u0000 <p>Aiming at the problem of lung nodule image segmentation accuracy, a segmentation framework combining TransU-Net and multiagent reinforcement learning was proposed. The powerful global modeling ability of the transformer is adopted to obtain long-distance dependent features, and then combined with the encoder–decoding structure of U-Net to achieve high-precision image restoration and detail preservation, thereby generating a segmentation probability graph with more semantic consistency. Then, the probability map is dynamically divided into multiple overlapping regions, which are processed by the agent in parallel. Each agent makes autonomous decisions based on local features and shares boundary information and global rewards through graph neural networks to achieve information collaboration and strategy collaboration among multiple agents. This mechanism supports the agent to continuously perceive the local state in the graph structure, exchange neighborhood features and respond to the overall feedback, and achieve dynamic collaborative optimization in the constantly updated strategy, thereby improving the segmentation accuracy. The self-attention mechanism is introduced to enhance global perception, and a sharing strategy network is designed to optimize the integration of local and global information. The experiments on the LIDC-IDRI and LUNA16 datasets with complex morphological structures and high fuzzy boundaries show that the Dice coefficient of the proposed method reaches 91.03 and the IoU reaches 83.15, which are significantly better than the existing methods and show good generalization ability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647334","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":"Parallel Symmetric Appearance-Motion Framework With Diffusion and Refinement Blocks for Video Anomaly Detection System","authors":"Kavitapu Naga Siva Shankara Vara Prasad,&nbsp;Dasari Haritha","doi":"10.1002/cpe.70183","DOIUrl":"https://doi.org/10.1002/cpe.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>Video anomaly detection is crucial in network security, application performance monitoring, and quality control. It recognizes unexpected patterns or behaviors in video footage, allowing for threat detection, app optimization, and product quality improvement. Many deep learning models effectively detect video anomaly detection but have some limitations, such as more time computation and model complexity. To address these issues, this paper proposes the Parallel Symmetric Appearance-Motion framework with Diffusion and Refinement blocks (PSAM-DRB) for detecting video abnormalities. The proposed model's initial step is to pre-process input videos to accentuate anomalous activities through video frame selection. Spatial and temporal Residual Inception-based autoencoder extracts multi-level features and optical flow maps in video frames. Feature decoding is performed using motion- and appearance-dominated branches. A Diffusion Strengthening and Intermodal Refinement block enhances feature representation through cross-scale augmentation and cross-modality interaction. Finally, a fusion module combines the upper and lower branches to detect video anomalies. In this evaluation, the proposed model using the UCF-Crime dataset achieved an accuracy of 99.19%. Finally, the proposed PSAM-DRB framework provides a robust and efficient method for identifying anomalies in video data, with applications in a variety of industries.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647332","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":"Correction to “Markov Chain-Based Analysis and Fault Tolerance Technique For Enhancing Chain-Based Routing in WSNs”","authors":"","doi":"10.1002/cpe.70190","DOIUrl":"https://doi.org/10.1002/cpe.70190","url":null,"abstract":"<p>\u0000 <span>Jalili, A</span>, <span>Alzubi, JA</span>, <span>Rezaei, R</span>, et al. <span>Markov Chain-Based Analysis and Fault Tolerance Technique For Enhancing Chain-Based Routing in WSNs</span>. <i>Concurrency Computat Pract Exper</i> <span>36</span>, no. <span>12</span> (<span>2024</span>): e8032, https://doi.org/10.1002/cpe.8032.</p><p>In the published article, the affiliation of Mehdi Gheisari was incorrect.</p><p>The correct affiliation listing should be 6-Institute of Artificial Intelligence, Shaoxing University, Zhejiang, China 7-Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India 8-Department of R&amp;D, Shenzhen BKD Co Ltd, Shenzhen, China.</p><p>We apologize for this error.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 18-20","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}