Concurrency and Computation-Practice & Experience最新文献

{"title":"Predicting the Best Green Growth Performance With Integrated Intuitionistic Fuzzy and Metaheuristic Algorithms","authors":"Mustafa Ozdemir","doi":"10.1002/cpe.70089","DOIUrl":"https://doi.org/10.1002/cpe.70089","url":null,"abstract":"<p>Although various studies monitor and evaluate green growth performance, no heuristic-based hybrid studies test the reflection of green growth indicators. This study aims to identify the best green growth performance by using intuitionistic fuzzy and metaheuristic algorithms over green growth indicators, contributing to filling the gap in the field and providing inferences about green growth to decision-makers. For this purpose, the green growth levels of 31 selected countries were analyzed based on environmental and resource efficiency indicators, and the best result was predicted. The study was conducted in two phases. In the first phase, countries were ranked according to their green growth performance using intuitionistic fuzzy methods. In the second phase, the best performance value was estimated at different population levels using metaheuristic algorithms. The research results show that the renewable electricity generation variable is the most important criterion. Ireland (1.00), Switzerland (0.98), and Costa Rica (0.95) are the countries with the best green growth performance, respectively. In green growth performance estimation, the ANFIS-TLBO model (<i>R</i>² = 0.908; MAE = 0.196; SMAPE = 0.689; MSE = 0.050; RMSE = 0.223; MBE = 0.188) demonstrated the closest estimation accuracy to the real values. In this study, for the first time, a hybrid model combining the intuitionistic fuzzy method and a metaheuristic algorithm was tested and proposed for green growth performance assessment. With this originality, it is expected that the results of this article will make a significant contribution to the literature gap and serve as a guide for policymakers and researchers.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888825","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":"Attribute-Based Access Control With Credible Outsourcing and Collusion-Resistant Revocation Based on Blockchain for Iomt","authors":"Zhaoqian Zhang,&nbsp;Di Wu,&nbsp;Shang Gao","doi":"10.1002/cpe.70105","DOIUrl":"https://doi.org/10.1002/cpe.70105","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;The maturity of cloud computing and the Internet of Things (IoT) has greatly facilitated the growth of the healthcare industry. Nowadays, Personal Health Records (PHRs) collected by the Internet of Medical Things (IoMT) are shared with healthcare institutions through the public cloud. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can protect PHRs' confidentiality while promoting sharing efficiency. However, current schemes suffer from high computation overhead and data leakage caused by privilege revocation. This paper proposes a CP-ABE scheme with credible outsourcing and collusion-resistant revocation based on blockchain for IoMT. Most encryption and decryption operations are outsourced to the cloud server, and the outsourced computation correctness is verified by the blockchain credibly. The user needs to perform only two exponential operations in encryption and one exponential operation in decryption. Furthermore, we no longer use the cloud server to update the ciphertext in privilege revocation to avoid data leakage. Meanwhile, we add a ciphertext private key &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;K&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;c&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ S{K}_{ct} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; bound to the ciphertext to perform decryption together with the attribute private key. Only users whose attributes satisfy the policy can obtain &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;K&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;c&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ S{K}_{ct} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and the smart contract credibly verifies this process. The revoked user cannot decrypt the ciphertext due to the lack of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;K&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;c&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ S{K}_{ct} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. We performed a rigorous security analysis of our scheme, encompassing confidentiality, collusion resistance, revocability, and blockchain, which collectively validate the robustness and s","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888823","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":"PESC - Parallel Experience for Sequential Code","authors":"Henrique C. T. Santos,&nbsp;Luciano S. de Souza,&nbsp;Jonathan H. A. de Carvalho,&nbsp;Tiago A. E. Ferreira","doi":"10.1002/cpe.70102","DOIUrl":"https://doi.org/10.1002/cpe.70102","url":null,"abstract":"<p>The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that abstract the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that abstracts the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888851","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":"An Effective Load-Balancing Approach for Multicast IPTV Traffic in Multihoming Networks","authors":"Afaf Benaouda Chaht,&nbsp;Mohammed Bencheikh,&nbsp;Chakib Zouaoui,&nbsp;Abdennacer Bounoua,&nbsp;Nasreddine Taleb,&nbsp;Mohamed Naimi","doi":"10.1002/cpe.70103","DOIUrl":"https://doi.org/10.1002/cpe.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional multicast streams are typically sent using unicast routing plans, which follow a single path or, at best, multiple paths of the same cost. However, this method is not efficient for modern networks with various access points. To improve this, we propose a new approach that combines multipath and multicast streams with a load-balancing model. This method aims to enhance multicast traffic transmission in a multipath environment. Our approach separates the Control Plan (CP) from the User Plan (UP). The CP uses Multipath TCP functions, while the UP handles IPTV (Internet Protocol Television) packets, which can be either TCP or UDP. This separation allows for better load distribution, bandwidth conservation, and balanced traffic across available paths. Real-world tests show that all multicast substreams can be smoothly transferred across the network evenly divided and routed through suitable paths whether symmetrical or asymmetrical scénario. Thus, the results of tests on the main performance and reliability parameters such as bandwidth utilization, cross traffic and delay, prove the algorithm's proactivity and responsiveness to network conditions and validate the effectiveness of our approach. In summary, using multicast in multipath TCP networks improves the streaming experience for end users.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888999","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":"Weak–Strong Synergy Learning With Random Grayscale Substitution for Cross-Modality Person Re-Identification","authors":"Zexin Zhang","doi":"10.1002/cpe.70101","DOIUrl":"https://doi.org/10.1002/cpe.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Visible-infrared person re-identification (VI-ReID) is a rapidly emerging cross-modality matching problem that aims to identify the same individual across daytime visible modality and nighttime thermal modality. Existing state-of-the-art methods predominantly focus on leveraging image generation techniques to create cross-modality images or on designing diverse feature-level constraints to align feature distributions between heterogeneous data. However, challenges arising from color variations caused by differences in the imaging processes of spectrum cameras remain unresolved, leading to suboptimal feature representations. In this paper, we propose a simple yet highly effective data augmentation technique called Random Grayscale Region Substitution (RGRS) for the cross-modality matching task. RGRS operates by randomly selecting a rectangular region within a training sample and converting it to grayscale. This process generates training images that integrate varying levels of visible and channel-independent information, thereby mitigating overfitting and enhancing the model's robustness to color variations. In addition, we design a weighted regularized triplet loss function for cross-modality metric learning and a weak–strong synergy learning strategy to improve the performance of cross-modal matching. We validate the effectiveness of our approach through extensive experiments conducted on publicly available cross-modality Re-ID datasets, including SYSU-MM01 and RegDB. The experimental results demonstrate that our proposed method significantly improves accuracy, making it a valuable training trick for advancing VT-ReID research.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888850","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":"Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms","authors":"Mustafa Etcil,&nbsp;Bilge Kagan Dedeturk,&nbsp;Burak Kolukisa,&nbsp;Burcu Bakir-Gungor,&nbsp;Vehbi Cagri Gungor","doi":"10.1002/cpe.70107","DOIUrl":"https://doi.org/10.1002/cpe.70107","url":null,"abstract":"<p>Breast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer-aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA-PSO-LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10-fold cross-validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA-PSO-LR classifier is compared with state-of-the-art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1-score on the WDBC dataset, and 97.94% accuracy and 97.35% F1-score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888824","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":"An Improved and Untraceable Lightweight Authentication and Key Agreement Scheme for Wireless Body Area Networks","authors":"Zhongqing Wu,&nbsp;Ying Wang,&nbsp;Bo Gong,&nbsp;Lei Cheng,&nbsp;Jianbo Xu,&nbsp;Yulong Wang","doi":"10.1002/cpe.70104","DOIUrl":"https://doi.org/10.1002/cpe.70104","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless body area networks (WBANs) are an important component of Medical 4.0, as they can use sensors to collect real-time data on a patient's vital signs and transmit this information over the internet to healthcare providers, greatly improving the quality and efficiency of medical care. However, Since WBANs typically collect human physiological information, which involves personal privacy, and the collected data are usually used by medical professionals for medical diagnosis. If the transmitted data are tampered with by attackers, it may lead to errors in medical diagnosis. Therefore, we must ensure the privacy, integrity, and reliability of the data during the transmission process. As a result, identity authentication and session key negotiation become crucial for secure communication in this context. Moreover, due to the constraints of sensors in terms of memory, computation, and battery life, a lightweight and efficient authentication scheme is required. Previously, Narwal et al. proposed a scheme called SAMAKA, which allows for anonymous authentication and session key establishment between sensor nodes and a control node. However, in-depth analysis has revealed that their scheme is vulnerable to sensor node capture attacks and does not provide session unlinkability or forward secrecy. To address the security flaws in Narwal et al.'s protocol, we have proposed an improved and untraceable mutual authentication and key negotiation scheme. We have also formally verified the security of our scheme using BAN logic and the AVISPA tool. Performance analysis shows that our scheme has significant advantages over other related schemes in terms of computational and communication costs, making it more suitable for the resource-constrained WBAN environment.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871685","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":"Service Placement in Fog Computing Using a Combination of Reinforcement Learning and Improved Gray Wolf Optimization Method","authors":"Pouria Ashkani,&nbsp;Seyyed Hamid Ghafouri,&nbsp;Maliheh Hashemipour","doi":"10.1002/cpe.70097","DOIUrl":"https://doi.org/10.1002/cpe.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>Fog computing extends cloud computing to the edge of the network, bringing processing and storage capabilities closer to end users and Internet of Things (IoT) devices. This paradigm helps to reduce latency, improve response time, and optimize bandwidth usage. In the cloud computing environment, service availability is a criterion for determining user satisfaction, which is strongly influenced by response time and optimal allocation of network resources (communication bandwidth). Service placement in fog computing refers to the process of determining optimal locations for placing services in the network. In this paper, the service placement is done by being aware of the volume of user requests from fog nodes by using neural networks, reinforcement learning, and the improved gray wolf optimization (IGWO) method. Based on the results obtained from simulation, the proposed approach has less response time (between 5% and 21%), more favorable load balance, more utility value (12%) and lower Energy consumption by a minimum of 10% and a maximum of 25%.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865629","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 Blockchain-Based Secure Model to Share Medical Data Using Mobile Edge Computing","authors":"Sagnik Datta,&nbsp;Suyel Namasudra","doi":"10.1002/cpe.70087","DOIUrl":"https://doi.org/10.1002/cpe.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Blockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchain-based scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the Non-Orthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the state-of-the-art offloading schemes.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861833","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 Mining Pool Performance and Security Through Optimized Cluster-Trust Consensus Mechanism in Blockchain Networks","authors":"Naga Sravanthi Puppala,&nbsp;R. Manoharan","doi":"10.1002/cpe.70077","DOIUrl":"https://doi.org/10.1002/cpe.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>This research presents an innovative method for enhancing security in pool mining consensus within Blockchain systems. Block mining is a demanding endeavor in decentralized systems, leading many miners to congregate within mining pools. The inherent traits of Blockchain technology, defined by decentralization and transparency, create a conducive environment for extensive mining pool participation. However, the inclusion of all the miners into mining raises concerns about miners' consensus reliability, malicious attacks, fraud detection rates, and variance in miners' incentives. These consequences necessitate the encouragement of trusted miners to pool consensus to ensure security. A clustering-based trust model in blockchain for pool mining consensus (CBTMB-PM) has been proposed to address the miner's trust dynamics and improve pool consensus efficiency. Miner's trust dynamics are assessed through clusters based on miners' behavioral attributes, which encompass historical performance, readiness, and indirect feedback recommendations of miners to improve overall pool reliability. Furthermore, a Cluster-Trust Proof of Work (CTPoW) consensus protocol has been proposed to enhance pool efficiency, ensuring that only reliable miners contribute to the consensus process by filtering out untrusted miners in the consensus process. Experimental and theoretical evaluations demonstrate the effectiveness of the CTPoW protocol. The effectiveness of the model is tested using a partially decentralized open-source Hyperledger Fabric framework for parameters such as block authorization time, validation time, block creation time, processing time, and confirmation time to analyze the practical changes observed by a group of miners through off-chain mode. It has excellent performance by comparison with some other state-of-the-art.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861639","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}