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  • Open Access


    Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

    Yonghao Zhang1,3, Yongtang Wu2, Tao Li1, Hui Zhou1,3, Yuling Chen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 345-361, 2023, DOI:10.32604/cmes.2023.026920

    Abstract The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a third party for key distribution… More >

  • Open Access


    Deep Consensus Network for Recycling Waste Detection in Smart Cities

    Manar Ahmed Hamza1,*, Hanan Abdullah Mengash2, Noha Negm3, Radwa Marzouk2, Abdelwahed Motwakel1, Abu Sarwar Zamani1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050

    Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of… More >

  • Open Access


    PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

    Yiqun Zhu1, Shuxian Sun1, Chunyu Liu1, Xinyi Tian1, Jingyi He2, Shuai Xiao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691

    Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based federated learning method is proposed… More >

  • Open Access


    Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

    Murat Dener*, Sercan Gulburun

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1235-1251, 2023, DOI:10.32604/cmc.2023.036357

    Abstract One of the most common types of threats to the digital world is malicious software. It is of great importance to detect and prevent existing and new malware before it damages information assets. Machine learning approaches are used effectively for this purpose. In this study, we present a model in which supervised and unsupervised learning algorithms are used together. Clustering is used to enhance the prediction performance of the supervised classifiers. The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score. In the first stage of the model, the… More >

  • Open Access


    Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

    Mingjun Jiang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 487-515, 2023, DOI:10.32604/cmes.2023.024014

    Abstract Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a… More >

  • Open Access


    Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

    G. Sharmila1,*, M. K. Kavitha Devi2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1743-1758, 2023, DOI:10.32604/csse.2023.032127

    Abstract Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve… More >

  • Open Access


    Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

    Huiwen Xue1, Jiwei Wen1,*, Akshya Kumar Swain1, Xiaoli Luan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127

    Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore,… More >

  • Open Access


    Proof-of-Improved-Participation: A New Consensus Protocol for Blockchain Technology

    N. Anita*, M. Vijayalakshmi, S. Mercy Shalinie

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2007-2018, 2023, DOI:10.32604/csse.2023.025516

    Abstract The Internet of Things (IoT) is converting today’s physical world into a complex and sophisticated network of connected devices on an enormous scale. The existing malicious node detection mechanism in traditional approaches lacks in transparency, availability, or traceability of the detection phase. To overcome these concerns, we provide a decentralized technique using blockchain technology. Despite the fact that blockchain technology is applicable to create that type of models, existing harmony set of instructions are susceptible to do violence to such as DoS and Sybil, making blockchain systems unfeasible. Here, a new Proof-of-Improved-Participation (PoIP) harmony instruction was suggested that benefits the… More >

  • Open Access


    Enhancing Blockchain Security Using Ripple Consensus Algorithm

    A. Baseera1, Abeer Abdullah Alsadhan2,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4713-4726, 2022, DOI:10.32604/cmc.2022.029538

    Abstract In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained. Blockchain is used in providing security by encrypting the sharing of information. Blockchain is applied in the peer-to-peer (P2P) network and it has a decentralized ledger. Providing security against unauthorized breaches in the distributed network is required. To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity. Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network. This… More >

  • Open Access


    Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

    Xiaoyun Lu1, Jiuying Dong2,3,*, Hecheng Li1, Shuping Wan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 881-907, 2022, DOI:10.32604/cmes.2022.020598

    Abstract Based on the analyses of existing preference group decision-making (PGDM) methods with intuitionistic fuzzy preference relations (IFPRs), we present a new PGDM framework with incomplete IFPRs. A generalized multiplicative consistent for IFPRs is defined, and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs. Moreover, in this study, another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs. For group decisionmaking (GDM) with incomplete IFPRs, three reliable sources inuencing the weights of experts are identified. Subsequently, a method for determining the weights of experts is developed by simultaneously considering… More >

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