Concurrency and Computation-Practice & Experience最新文献

{"title":"A Genetic Algorithm-Based Approach for Collision Avoidance in a Multi-UAV Disaster Mitigation Deployment","authors":"Anuradha Banerjee,&nbsp;Sachin Kumar Gupta,&nbsp;Vinod Kumar","doi":"10.1002/cpe.70061","DOIUrl":"https://doi.org/10.1002/cpe.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>This research delves into the intricacies of designing trajectories for unmanned aerial vehicles (UAVs) within a multi-UAV system, specifically addressing the challenges presented during simultaneous rescue operations in neighboring states. The unique scenario introduces a potential risk of UAVs from one state intersecting with those from others, leading to communication issues and the looming threat of collisions. These collisions not only cause delays in emergency operations but also result in additional costs for repairing damaged UAV components. In response to this critical challenge, the study proposes an innovative approach utilizing Genetic Algorithms to facilitate collision avoidance in a multi-UAV environment, tailored explicitly for disaster mitigation scenarios. This technique is an efficient solution to enhance the safety and effectiveness of UAV operations during disaster response and relief efforts. The proposed trajectory planning method uses a genetic algorithm, with the fitness function strategically designed to optimize two pivotal objectives: utility (maximizing the number of people saved postdisaster) and collision avoidance (minimizing conflicts between multiple UAVs as they navigate predetermined paths). The overarching goal of this approach is to strike a balance, aiming to maximize utility while concurrently minimizing the risk of collisions. By adopting this approach, the research significantly contributes to advancing the field of disaster response strategies, enhancing the overall efficiency of multi-UAV systems in complex and dynamic environments. The proposed solution not only addresses the immediate challenges posed by potential collisions but also underscores the importance of optimizing UAV trajectories to achieve maximum utility in postdisaster scenarios.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809387","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":"Multiuser Hierarchical Authorization Using Sparsity Polarization Pruning for Model Active Protection","authors":"Yujia Zhu,&nbsp;Jia Luo,&nbsp;Ruoxi Wang,&nbsp;Xiaojie Du,&nbsp;Daoxun Xia","doi":"10.1002/cpe.70076","DOIUrl":"https://doi.org/10.1002/cpe.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>Currently, artificial intelligence technology is rapidly penetrating into various fields of socioeconomic development with increasing depth and breadth, becoming an important force driving innovation and development, empowering thousands of industries, while also bringing challenges such as security governance. The application of deep neural network models must implement hierarchical access based on user permissions to prevent unauthorized users from accessing and abusing the model, and to prevent malicious attackers from tampering or damaging the model, thereby reducing its vulnerabilities and security risks. To address this issue, the model provider must implement a hierarchical authorization policy for the model, which can grant users access to the model based on their specific needs, while ensuring that unauthorized users cannot use the model. Common methods for implementing hierarchical authorization of models include pruning and encryption, but existing technologies require high computational complexity and have unclear hierarchical effects. In this article, we propose a sparsity polarization pruning approach for layered authorization, which combines sparsity regularization to filter insignificant channels and a polarization technique to cluster critical channels into distinct intervals. By pruning channels based on polarized scaling factors from the batch normalization (BN) layer, our method dynamically adjusts model precision to match user authorization levels. Initially, we extract the scaling factor of the BN layer to assess the importance of each channel. A sparsity regularizer is then applied to filter out irrelevant scaling factors. To enhance the clarity and rationality of pruning intervals, we use a polarization technique to induce clustering of scaling factors. So we proposed multiuser hierarchical authorization using sparsity polarization pruning for model active protection. Based on the grading requirements, we prune channels corresponding to varying numbers of significant scaling factors. Access is granted at different levels depending on the precision key provided by the user, thereby ensuring a secure and efficient means of accessing the model's resources. Experimental results demonstrate that our approach achieves superior grading performance across three datasets and two different neural networks, showcasing its broad applicability. Moreover, our method achieves effective grading just by pruning a small portion of the channels, offering a high level of efficiency.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801882","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 Traceable and Anonymous Mutual Authentication Scheme for Smart Healthcare on Elliptic Curves","authors":"Yujia Xie,&nbsp;Dongmei Li,&nbsp;Xiaomei Zhang,&nbsp;Wenjing Lv","doi":"10.1002/cpe.70063","DOIUrl":"https://doi.org/10.1002/cpe.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid development of big data technologies has exacerbated the challenge of maintaining patient privacy in smart healthcare environments. Although previous mutual patient–physician authentication systems achieve basic anonymization, patients' communication addresses are still exposed, and attackers can analyze transaction records to establish correlations between users' addresses and even obtain their real identities. To address this problem, we propose a user anonymization scheme based on the elliptic curve discrete logarithmic problem assumption, which aims to prevent malicious interception and theft of patients' personal data by obfuscating the identity of registered users. By combining identity-based encryption with advanced anonymization techniques and reconstructing signatures of knowledge, traceability is achieved while ensuring that only the intended recipient with the corresponding private key can decrypt the data. The validation shows that our system guarantees unlinkability and anonymity while resisting hijacking attacks and man-in-the-middle attacks, and it is simulated using JPBC 2.0.0 (Jdk version 14.0.1), which shows that the communication overhead needs 808 bytes and that the computation overhead for system initialization, signature, and validation are 102, 167, and 70 ms, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809388","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":"Multi-Criteria Optimization of Scientific Workflow Schedules for Improved Energy Efficiency in Cloud Infrastructures","authors":"Nadia Dahmani,&nbsp;Hatem Aziza,&nbsp;Hajer Ben Romdhane,&nbsp;Saoussen Krichen","doi":"10.1002/cpe.70065","DOIUrl":"https://doi.org/10.1002/cpe.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>Rising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from data centers. However, the persistent challenge of high-energy consumption in these facilities necessitates a concentrated approach toward energy reduction. This paper introduces an innovative multi-objective scheduling strategy for scientific workflows, tailored for heterogeneous computing environments. Our method employs a hybrid genetic algorithm, incorporating Hill Climbing to generate an initial population of chromosomes. Subsequently, a genetic algorithm optimizes task assignments to the most suitable virtual machines, utilizing a meticulously designed fitness function to evaluate each chromosome's suitability for solving the scheduling problem. Through extensive experimentation, we demonstrate that our proposed algorithm outperforms other scheduling techniques in terms of solution quality, contributing to reduced energy consumption, processing duration, and cost. We contend that this innovative approach holds substantial potential in mitigating the energy consumption and carbon footprint associated with cloud data centers, offering a sustainable and environmentally conscious solution for scientific workflow scheduling.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801881","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 Protection During the Issuance and Revocation of Verifiable Credentials in Self-Sovereign Identity","authors":"Tiantian Zhang,&nbsp;Ying Wang,&nbsp;Bo Gong,&nbsp;Jianbo Xu,&nbsp;Junjie Wu,&nbsp;Changxu Wan","doi":"10.1002/cpe.70084","DOIUrl":"https://doi.org/10.1002/cpe.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Self-sovereign identity management systems operate in open network environments and face security threats from semi-trusted or malicious adversary models. In such environments, verifiable credentials are susceptible to attacks such as theft and forgery. In response to the privacy risks associated with verifiable credentials during issuance and revocation, this article proposes a privacy protection scheme for user information during the issuance and revocation processes of verifiable credentials in self-sovereign identity management based on blockchain technology. First, a privacy-preserving method that does not rely on a single identity provider and resists Sybil attacks has been designed using secure multi-party computation cryptographic techniques. Second, the consortium blockchain committee nodes act as the issuer of verifiable credentials. By combining attribute commitments and zero-knowledge proof techniques, the user's identity information is hidden, achieving the privacy protection goal during the issuance of verifiable credentials. Furthermore, in order to protect user privacy during the revocation of verifiable credentials (VCs), we employ a cryptographic accumulator technique to implement the revocation operation. This approach ensures the security of user privacy while effectively managing the revocation of credentials. Finally, this paper conducts a security analysis and performance evaluation of the proposed scheme. The results show that our scheme strikes a balance between security needs and time efficiency.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809386","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":"Toward Latency-Efficient Multicast Coflow Scheduling for Reconfigurable Data Center Networks","authors":"Fulong Li,&nbsp;Fanlong Zhang,&nbsp;Yuhang Wu,&nbsp;Zhuowei Wang,&nbsp;Quan Chen,&nbsp;Yuan Chai,&nbsp;Yongchao Tao","doi":"10.1002/cpe.70085","DOIUrl":"https://doi.org/10.1002/cpe.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>The emerging optical circuit technology, which can establish circuit connections among switches, has been proposed as a promising paradigm for data center networks. This paper investigates the problem of minimizing the completion time of multicast coflows in optical circuit switches (OCSs)-based data center networks. The existing works either only focused on multicast coflow scheduling or focused solely on circuit scheduling in OCS-based networks, which greatly limits their performance. Hence, in this paper, we study how to reduce the completion time of multicast coflows by considering circuit scheduling and coflow scheduling simultaneously. First, the problem of multicast coflow scheduling is formulated and proved to be NP-hard. Then, a Delay-efficient Multicast Coflow Scheduling (DMCS) algorithm is proposed by integrating multicast coflow scheduling with circuit scheduling. The proposed DMCS algorithm is proved to have an approximation ratio of at most <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <msqrt>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msqrt>\u0000 </mrow>\u0000 <annotation>$$ 2sqrt{n} $$</annotation>\u0000 </semantics></math>, where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math> represents the number of OCS. Through extensive simulations, it is shown that the proposed DMCS algorithm can achieve high performance compared to state-of-the-art methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809723","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":"Adaptive Hierarchical Offloading for Mobile Edge Computing in High-Mobility SWIPT-Enabled Networks","authors":"Zewei Zhang,&nbsp;Taoshen Li,&nbsp;Linfeng Yang","doi":"10.1002/cpe.70078","DOIUrl":"https://doi.org/10.1002/cpe.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile devices are affected by limited energy and channel resources in the dynamic scenarios with high mobility and complexity, which will trigger the risks such as task failure or low offloading efficiency. This article proposes a multiuser and multiserver MEC network framework based on simultaneous wireless information and power transfer (a.k.a., SWIPT). We first consider multiple mobile devices with fixed task information in which the tasks can be either processed in-local or offloaded to the MEC server for processing via uplink transmission. Our method also embraces a hierarchical demand-weighted index (HDWI) and priority channel transmission scheduling rule, which can evaluate the status of device services and effectively conduct the hierarchical offloading of tasks. In this case, our model not only ensures the continuity of the service provided by mobile devices but also evaluates the relationship between energy consumption and device delay during the offloading process. Finally, we propose an efficient mobile device cost hierarchical offloading algorithm (MCHOA) to deal with the issues produced by the constructed multiobjective optimization mathematical model. MCHOA complies with the principle of HDWI and is combined with a multiobjective evolutionary algorithm based on decomposition to solve various mathematical tasks including obtaining the Pareto optimal curve regarding the average time consumption and the average energy consumption of devices. The experimental results show that MCHOA can simultaneously reduce time consumption and energy consumption costs by at least 13.3% and 37.5%, respectively. Our experiments also validate the superiority of the proposed algorithm and the application prospect of the model.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801883","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":"MOFDRNet: A Model for Data Leakage Attacks in Federated Learning","authors":"Yaru Zhao,&nbsp;Jianbiao Zhang,&nbsp;Yihao Cao,&nbsp;Xianqun Han","doi":"10.1002/cpe.70032","DOIUrl":"https://doi.org/10.1002/cpe.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>Federated Learning allows multiple clients to train local models and aggregate them on the server side. The client is invisible to the shared global model generated by the server, which provides an opportunity for malicious attackers to utilize the inherent vulnerability of federated learning to initiate data leakage attacks. Existing attack techniques are largely <i>client-based</i> and focus on <i>inferring model parameters directly</i>, but do not work for server-based attacks, mainly due to differences in their ability to generalize attacks. Yet few robust data leakage attacks toward federated learning vulnerability have been developed on the server side. To address the above problem, we propose MOFDRNet, a <i>Multi-Objective Fake Data Regression Network</i> model that integrates the loss function and multiple metrics strategies. The key idea is to deploy a malicious attack model on the server with the purpose of generating fake data and labels and continuously approximating the shared gradients between clients and the server, thereby recovering clients' private data. Experimental results demonstrate that the MOFDRNet model has significant advantages in implementing data leakage attacks. Finally, we also discuss the differential privacy defense approach in this study.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809385","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":"Hierarchical Feature Selection Based on Instance Correlation and Label Semantic Structure","authors":"Yu Mao,&nbsp;Chunyu Shi,&nbsp;Zhiyi Cai,&nbsp;Hui Chen,&nbsp;Lei Guo","doi":"10.1002/cpe.70079","DOIUrl":"https://doi.org/10.1002/cpe.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>Hierarchical classification learning aims to exploit the hierarchical relationship between data categories. Making full use of the hierarchical structure between class labels can effectively reduce the number of categories for each classification task and improve the accuracy of classification. For hierarchical feature selection, usually the more similar two labels are, the more features they share. However, existing hierarchical feature selection algorithms often ignore this. In addition, current hierarchical feature selection algorithms do not deeply consider the semantic structure between labels when exploiting label correlations. In this article, we propose a hierarchical feature selection based on instance correlation and label semantic structure. This algorithm expresses the correlation between instances with the help of Laplacian matrix. Then, the instance correlation is combined with the semantic relationship between labels in the hierarchical structure to construct a hierarchical feature selection model. To prove the effectiveness of the proposed algorithm, a large number of experiments are conducted on hierarchical datasets in different fields, and multiple hierarchical feature selection are compared. The experimental results demonstrate that the proposed algorithm has significant performance superiority.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809820","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":"DARB: A Dynamic Architecture for Data Replica Balancing","authors":"Rhauani Weber Aita Fazul,&nbsp;Odorico Machado Mendizabal,&nbsp;Patrícia Pitthan Barcelos","doi":"10.1002/cpe.70050","DOIUrl":"https://doi.org/10.1002/cpe.70050","url":null,"abstract":"<div>\u0000 \u0000 <p>Distributed file systems, such as HDFS, are designed to support applications that handle large volumes of data. Data replication, which is at the core of the HDFS storage model, is essential for fault tolerance and performance. As new data are loaded into the system, the distribution of data blocks replicated among the nodes may become dissimilar affecting replica balancing and data locality. The HDFS Balancer is the official solution for redistributing the data already stored in the cluster. However, it overlooks the specific needs of the applications during data rearrangement and requires manual intervention by system administrators—a dependency that is often inadequate and inefficient. To address these limitations, this work presents DARB, a Dynamic Architecture for Replica Balancing that combines reactive and proactive strategies. The former uses the Prioritized Replica Balancing Policy to customize the replica balancing through configurable priorities. The latter consists of an event-driven strategy that makes the overall balancing process in HDFS transparent. DARB comprises modular components and a metrics observation model that identifies and determines when corrective actions should be taken. It also automatically triggers the HDFS Balancer based on standardized trigger events. The evaluation results reinforce that the proposed solution removes the need for manual configuration and execution while actively acting to keep the cluster balanced, taking into account performance, reliability, and data availability perspectives. Thus, DARB offers a sophisticated and specialized balancing solution that makes the balancing process seamless and flexible, introducing to the HDFS the concept of context-aware replica balancing.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793415","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}