Concurrency and Computation-Practice & Experience最新文献

{"title":"Energy-Efficient Multi-Level Adaptive Clustering for Enhanced Performance in Underwater Sensor Networks","authors":"Emad S. Hassan","doi":"10.1002/cpe.70246","DOIUrl":"https://doi.org/10.1002/cpe.70246","url":null,"abstract":"<div>\u0000 \u0000 <p>Underwater wireless sensor networks (UWSNs) are vital for real-time data collection and monitoring in underwater environments, with applications in environmental conservation, disaster management, marine biodiversity monitoring, and offshore energy production. However, UWSN deployment faces significant challenges such as high energy consumption, limited communication range, signal attenuation, and environmental factors like underwater currents and temperature variations. Existing solutions suffer from scalability and adaptability in deeper and more complex underwater environments. This paper presents an Energy-Efficient Multi-Level Adaptive Clustering (EEMLAC) scheme designed to optimize energy consumption and enhance the network lifetime of UWSNs. The proposed approach dynamically adjusts clustering structures based on depth and water pressure, optimizing signal management and minimizing energy loss. Nodes are categorized into three adaptive levels: the first level, at shallower depths, divides the outer and middle sections into three subsections at 120° angles; the second level, at mid-depths, increases the division to four subsections at 90° angles to mitigate higher signal attenuation; and the third level, at the greatest depths, further refines clustering with six subsections at 60° angles to efficiently manage data flow and energy usage. Normal nodes are positioned in the middle section for direct communication with the cluster head, whereas advanced nodes in the outer section utilize helper nodes to relay data, reducing transmission energy consumption. By adapting to environmental factors such as water pressure and attenuation, the proposed scheme effectively addresses communication challenges in deep-water UWSNs. Simulation results demonstrate that EEMLAC achieves superior performance, retaining approximately 0.6 J residual energy after 1000 rounds, reducing energy consumption to 2.3 × 10⁻³ J per node, extending network lifetime beyond 1800 rounds, achieving a packet delivery ratio (PDR) of 0.586, and improving throughput to 3.7 × 10⁶ bits/s, outperforming benchmark schemes such as EBREC, EAMC, and EGRCs.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832913","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":"Research on Lightweight Image Matching and Cattle Individual Identification Technology Based on Subpixel Keypoints","authors":"Zhi Weng,&nbsp;Xiaoding Wu,&nbsp;Yiyang Li,&nbsp;Zhiqiang Zheng","doi":"10.1002/cpe.70236","DOIUrl":"https://doi.org/10.1002/cpe.70236","url":null,"abstract":"<div>\u0000 \u0000 <p>To enhance the recognition model's adaptability to non-standardized data, this study proposes a lightweight image matching-based cattle individual identification technique that utilizes subpixel keypoints. The technique leverages SuperPoint and LightGlue to construct an image matching algorithm, with improvements made to enhance recognition accuracy. During the feature point extraction process, keypoint refinement is introduced, using the learned displacement vectors of features to enhance SuperPoint's subpixel accuracy. Additionally, the OTSU algorithm is employed to compute the feature extraction threshold adaptively, improving the feature point extraction process. A two-layer validation screening method is employed to optimize the matching pairs of LightGlue, further improving matching efficiency. To validate the effectiveness of the algorithm, comparative experiments were conducted on a self-constructed cattle facial dataset, comparing it with various image matching methods. The results indicate that, on the narrow-baseline dataset, the macro-average precision, recall, and F1 scores are 97.87%, 97.50%, and 97.68%, respectively. On the wide-baseline dataset, these metrics are 85.09%, 74.75%, and 79.59%, respectively. All results significantly surpass those of traditional image matching algorithms. In conclusion, the image matching algorithm proposed in this study effectively improves the cattle individual recognition model's adaptability to non-standardized data, providing valuable technical references for the practical application of cattle individual recognition methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833059","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 “Secure Data Authentication for Remotely Stored Data Using Bilinear Pairing on Elliptic Curves With Optimized Data Block Size”","authors":"","doi":"10.1002/cpe.70234","DOIUrl":"https://doi.org/10.1002/cpe.70234","url":null,"abstract":"<p>DOI: 10.1002/cpe.70141</p><p>Article CPE70141</p><p>In the originally published article, the authors' departmental affiliation was incorrectly listed as “Computer Science Engineering.” The correct affiliation is:</p><p>Computer Engineering</p><p>Shah &amp; Anchor Kutchhi Engineering College, Mumbai, India</p><p>We apologize for this error.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833060","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}

{"title":"GPU Optimization of ILU-Preconditioned GCR for Solving 19-Diagonal Linear Equations in GRAPES","authors":"Feng Zhang,&nbsp;Jinrong Jiang,&nbsp;Junlin Wei,&nbsp;Xuebin Chi,&nbsp;Huadong Xiao,&nbsp;Qingu Jiang,&nbsp;Xiangjun Wu,&nbsp;Sa Xiao,&nbsp;Lian Zhao,&nbsp;Youyun Li","doi":"10.1002/cpe.70217","DOIUrl":"https://doi.org/10.1002/cpe.70217","url":null,"abstract":"<div>\u0000 \u0000 <p>This article investigates the GPU optimization of solving 19-diagonal asymmetric linear systems within the numerical weather prediction model GRAPES. Such systems are commonly encountered when solving partial differential equations on 3D structured grids using finite difference methods. The five-diagonal patch-ILU preconditioner, which retains the essential connection coefficient, is well-suited for GPU platforms as it accelerates linear iterative convergence by approximately tenfold and offers a degree of parallelism. However, the forward-backward substitution process, used to solve the upper and lower triangular equations generated by the five-diagonal patch-ILU preconditioner, remains a major performance bottleneck on the GPU due to serial data dependencies. We designed the Shuffle Thomas algorithm, leveraging the GPU's shuffle functionality for data reuse, achieving efficient memory coalescing and data reuse, significantly enhancing memory throughput. Further exploiting the diagonal direction's parallelism in the substitution process, we designed the Divided Shuffle Thomas algorithm, doubling the instruction-level parallelism. This approach achieved a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>11</mn>\u0000 <mo>.</mo>\u0000 <mn>42</mn>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ 11.42times $$</annotation>\u0000 </semantics></math> to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>15</mn>\u0000 <mo>.</mo>\u0000 <mn>11</mn>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ 15.11times $$</annotation>\u0000 </semantics></math> speedup compared to cuSPARSE-gpsv. Our GCR solver on the Hygon DCU platform demonstrated a 5.41 to 8.47 times performance improvement over the CPU implementation with the same number of computing nodes, achieving higher computational efficiency with fewer processes. This has the potential to significantly enhance the computational efficiency for high-resolution numerical weather forecasting.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832635","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":"An Image Processing Method for Dynamic Monitoring of Wind Turbine Blade Operation Based on Ground Multi-Synchronous Camera Capture","authors":"Wenbo Wu,&nbsp;Yanbin Liu,&nbsp;Qiming Yang,&nbsp;Shuang Zhou,&nbsp;Yinggu Wu,&nbsp;Derui Gao,&nbsp;Shouxiao Ma","doi":"10.1002/cpe.70241","DOIUrl":"https://doi.org/10.1002/cpe.70241","url":null,"abstract":"<div>\u0000 \u0000 <p>With the increase in the capacity of wind turbine units, the length of their blades has significantly grown. Existing machine vision-based image acquisition methods are unable to capture the full view of the blades, leading to the inability to accurately monitor the operational status of wind turbine blades dynamically. Therefore, this study proposes an image processing method for dynamic monitoring of wind turbine blade operation based on point and line features from images captured by ground multi-synchronous cameras. After image dehazing filtering and edge detection, the method utilizes line detection, so as to extract line features. Additionally, on the basis of improved Harris and Scale-Invariant Feature Transform (SIFT) registration, constraints such as line feature constraints, parallel constraints, and equidistant intercept constraints are incorporated for blade stitching. This process involves stitching fragmented images into a complete image, along with image enhancement, and assessing seam smoothness using root mean square error. Results indicate that this method can effectively capture and retain complete blade information, particularly blade edge information and blade damage information. The method proposed in this paper integrates machine vision, image recognition, and trajectory tracking technologies to construct a database of blade operating conditions and images. It is capable of operating in hazy environments, enabling accurate monitoring of blade operating conditions, supporting intelligent maintenance of wind farms, improving resource utilization, and reducing operating costs. Compared with existing methods, it has certain advantages. The research findings are expected to provide valuable data support for detecting potential defects or damages on the blades using machine vision-based methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832636","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":"Business Process Next Activity Prediction Method Utilizing Remove Marked Case Reorganization and Fine-Grained Image Cube Action Engine","authors":"Ruoyuan Zhang,&nbsp;Xianwen Fang,&nbsp;Ke Lu,&nbsp;Xiwei Zhang","doi":"10.1002/cpe.70233","DOIUrl":"https://doi.org/10.1002/cpe.70233","url":null,"abstract":"<div>\u0000 \u0000 <p>Predictive business process monitoring discovers anomalies in business execution by predicting the next activity of a business process in real time, thereby helping enterprises to adjust and optimize business processes in a timely manner. Existing research usually focuses on the sequence information of a single trace in event logs or the structural information of process models, while ignoring the contextual correlation information in the process and the impact of existing and potential operational conflicts on the accuracy of the next activity prediction. To address these issues, we propose a next activity prediction method that combines trace case reorganization and expansion with a fine-grained image cube constraint action engine. This method addresses the problem of limited case numbers in a single trace and the lack of sparse pixel information in the encoded image. First, the labels of cases are removed, and cases are reorganized based on context dependencies, expanding the number of cases in the trace. Then, Gramian Angular Field (GAF) is used for fine-grained image encoding to enrich the content of the encoded image. A constraint cube constraint action engine is constructed, and Online Analytical Processing (OLAP) operations are used to constrain the process direction, monitor operational conflicts, and select the correct process direction. Finally, experimental results on four real event logs show that the proposed method outperforms the baseline methods in terms of the accuracy of next activity prediction.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782724","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 Secure and Communication-Efficient Federated Recommendation Method With Blockchain","authors":"Sheng Lu,&nbsp;Daming Huang,&nbsp;Zhehong Wang,&nbsp;Zheng Li,&nbsp;Hang Zhang,&nbsp;Wanchun Dou,&nbsp;Chen Tian","doi":"10.1002/cpe.70219","DOIUrl":"https://doi.org/10.1002/cpe.70219","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the significant advantages of federated learning (FL) in privacy protection, federated recommendation systems (FedRSs) have garnered increasing attention by enhancing recommendation performance through local data training. However, most current FedRSs adopt a client-server communication architecture, which may lead to communication overload and single points of failure. Additionally, clients may face challenges due to limited communication resources and malicious attacks. To address the above challenges, we propose a Blockchain-assisted Federated learning method for Recommendation, called BFedRec, suitable for recommendation systems with high communication efficiency requirements. Specifically, BFedRec achieves the aggregation and distribution of recommendation models through a blockchain system, reducing reliance on central servers and alleviating communication bottlenecks and single points of failure. On this basis, BFedRec applies an innovative FL method that trains recommendation models directly on low-rank parameters to achieve efficient and secure data aggregation and distribution. Moreover, the flexibility of this aggregation and distribution strategy allows for scalable applications in other fields, such as blockchain-enabled software-defined network (SDN) management in on-chain and off-chain communication networks. Experimental results demonstrate that BFedRec outperforms existing methods on real datasets, significantly improving communication efficiency while effectively enhancing system security and robustness.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774013","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":"Optimizing the Substrate for Hypercube-Based Neuroevolution of Augmented Topologies to Design Soft Actuators","authors":"Hugo Alcaraz-Herrera,&nbsp;Michail-Antisthenis Tsompanas,&nbsp;Igor Balaz,&nbsp;Andrew Adamatzky","doi":"10.1002/cpe.70204","DOIUrl":"https://doi.org/10.1002/cpe.70204","url":null,"abstract":"<p>The characteristics of soft robots make them better candidates for applications such as healthcare, due to their enhanced safety, adaptability, and more natural human-robot interaction compared to traditional counterparts. Different actuating systems have been proposed for soft robotics. On the other hand, since this technology is fairly young, the design process of soft actuators is not yet well formalized. In an attempt to enhance the applicability of this type of actuator, the utilization of a NeuroEvolution algorithm to automatically design them is proposed here. More specifically, Hypercube-based NeuroEvolution of Augmented Topologies (HyperNEAT) is investigated for different substrate architectures. These substrates are Artificial Neural Networks that encode the three-dimensional representation of the soft actuators. The produced three-dimensional sketches are tested within a simulated environment under two different targets (the maximum displacement and the combination of maximum displacement and minimum actuator volume) to identify the suitability of HyperNEAT as an efficient designing methodology. Since the evaluation of candidate solutions under a physics simulator is the most computationally demanding process, the proposed methodology was realized under a client-server setting, with the aim of accelerating the evolutionary optimization of actuator sketches. The evaluation part of the algorithm was outsourced to the server side, which can be a specialized and high-performing computational entity. The resulting soft actuators of this study proved to be of higher competence when compared with actuators derived under previously published evolutionary methodologies.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767359","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}

{"title":"Annotation-Guided AoS-to-SoA Conversions and GPU Offloading With Data Views in C++","authors":"Pawel K. Radtke,&nbsp;Tobias Weinzierl","doi":"10.1002/cpe.70199","DOIUrl":"https://doi.org/10.1002/cpe.70199","url":null,"abstract":"<p>The C++ programming language provides classes and structs as fundamental modeling entities. Consequently, C++ code tends to favor array-of-structs (AoS) for encoding data sequences, even though structure-of-arrays (SoA) yields better performance for some calculations. We propose a C++ language extension based on attributes that allows developers to guide the compiler in selecting memory arrangements, that is, to select the optimal choice between AoS and SoA dynamically depending on both the execution context and algorithm step. The compiler can then automatically convert data into the preferred format prior to the calculations and convert results back afterward. The compiler handles all the complexity of determining which data to convert and how to manage data transformations. Our implementation realizes the compiler-extension for the new annotations in Clang and demonstrates their effectiveness through a smoothed particle hydrodynamics (SPH) code, which we evaluate on an Intel CPU, an ARM CPU, and a Grace-Hopper GPU. While the separation of concerns between data structure and operators is elegant and provides performance improvements, the new annotations do not eliminate the need for performance engineering. Instead, they challenge conventional performance wisdom and necessitate rethinking approaches how to write efficient implementations.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767891","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}

{"title":"Robust Attack Detection Framework Using Pretrained CNN Model for the Edge Industrial IoT Networks","authors":"Ibtihal A. Alablani,&nbsp;Mohammed J. F. Alenazi","doi":"10.1002/cpe.70206","DOIUrl":"https://doi.org/10.1002/cpe.70206","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid expansion of edge industrial Internet of things (Edge-IIoT) has transformed industrial operations while introducing critical security challenges at the network edge. The growing sophistication of cyber attacks targeting Edge-IIoT networks, particularly in resource-constrained industrial environments, necessitates advanced detection mechanisms capable of identifying and classifying diverse attack patterns at the edge. This article presents a comprehensive edge-centric attack detection framework leveraging pretrained deep learning models for securing Edge-IIoT networks. Our methodology uses five state-of-the-art pretrained models, GoogleNet, AlexNet, EfficientNetB0, ResNet50, and MobileNet, evaluated on the Edge-IIoTset dataset comprising 2,219,201 network flow samples across 15 distinct attack classes. The framework efficiently processes many input features extracted from edge network traffic, including basic network characteristics, protocol headers, and industrial application-level attributes specific to Edge-IIoT environments. The experimental results demonstrate that GoogleNet achieves the highest accuracy of 97% and lowest performance degradation compared to other pretrained models with AlexNet at 96.85%, EfficientNetB0 at 96.81%, ResNet50 at 96.7%, and MobileNet at 96.42% in edge environments. Furthermore, our proposed approach significantly outperforms existing Edge-IIoT security studies using the same dataset by up to 4.2%.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773517","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}