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  • Open Access

    ARTICLE

    Enhancing Vehicle Overtaking System via LoRa-Enabled Vehicular Communication Approach

    Kwang Chee Seng, Siti Fatimah Abdul Razak*, Sumendra Yogarayan

    Computer Systems Science and Engineering, Vol.49, pp. 239-258, 2025, DOI:10.32604/csse.2024.056582 - 10 January 2025

    Abstract Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads. In most scenarios, insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents. To address these issues, a comprehensive system is required to provide real-time assistance to drivers. Building upon our previous research on a LoRa-based lane change decision-aid system, this study proposes an enhanced Vehicle Overtaking System (VOS). This system utilizes long-range (LoRa) communication for reliable real-time data exchange between vehicles (V2V) and the cloud (V2C). By More >

  • Open Access

    ARTICLE

    Intrumer: A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

    Nazreen Banu A*, S.K.B. Sangeetha

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 579-607, 2025, DOI:10.32604/cmc.2024.059805 - 03 January 2025

    Abstract The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic. Cloud environments pose significant challenges in maintaining privacy and security. Global approaches, such as IDS, have been developed to tackle these issues. However, most conventional Intrusion Detection System (IDS) models struggle with unseen cyberattacks and complex high-dimensional data. In fact, this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system, named INTRUMER, which offers balanced accuracy, reliability, and security in cloud settings by multiple modules working together within it. The traffic captured… More >

  • Open Access

    ARTICLE

    A Verifiable Trust-Based CP-ABE Access Control Scheme for Cloud-Assisted Renewable Energy Systems

    Jiyu Zhang1,*, Kehe Wu1, Ruomeng Yan1, Zheng Tian2, Yizhen Sun2, Yuxi Wu2, Yaogong Guo3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1211-1232, 2025, DOI:10.32604/cmc.2024.055243 - 03 January 2025

    Abstract Renewable Energy Systems (RES) provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers (ES). However, securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic. Additionally, managing attributes, including addition, deletion, and modification, is a crucial issue in the access control scheme for RES. To address these security concerns, a trust-based ciphertext-policy attribute-based encryption (CP-ABE) device access control scheme is proposed for RES (TB-CP-ABE). This… More >

  • Open Access

    ARTICLE

    A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

    Chanhyeong Cho1, Byeori Kim2, Haehyun Cho2, Taek-Young Youn1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 813-834, 2025, DOI:10.32604/cmes.2024.056952 - 17 December 2024

    Abstract With the rise of remote collaboration, the demand for advanced storage and collaboration tools has rapidly increased. However, traditional collaboration tools primarily rely on access control, leaving data stored on cloud servers vulnerable due to insufficient encryption. This paper introduces a novel mechanism that encrypts data in ‘bundle’ units, designed to meet the dual requirements of efficiency and security for frequently updated collaborative data. Each bundle includes updated information, allowing only the updated portions to be re-encrypted when changes occur. The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes, such… More >

  • Open Access

    ARTICLE

    Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach

    Theophile Fozin Fonzin1,2, Halilou Claude Bobo Hamadjida2, Aurelle Tchagna Kouanou2,3,*, Valery Monthe4, Anicet Brice Mezatio5, Michael Sone Ekonde6

    Journal of Cyber Security, Vol.6, pp. 155-177, 2024, DOI:10.32604/jcs.2024.059265 - 09 January 2025

    Abstract Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design… More >

  • Open Access

    REVIEW

    Blockchain-Assisted Electronic Medical Data-Sharing: Developments, Approaches and Perspectives

    Chenquan Gan1,*, Xinghai Xiao2, Qingyi Zhu1, Deepak Kumar Jain3,4, Akanksha Saini5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3421-3450, 2024, DOI:10.32604/cmc.2024.059359 - 19 December 2024

    Abstract Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data. The blockchain is a decentralized digital ledger that ensures data-sharing security, transparency, and traceability through cryptographic technology and consensus algorithms. Consequently, medical blockchain data-sharing methods have garnered significant attention and research efforts. Nevertheless, current methods have different storage and transmission measures for original data in the medical blockchain, resulting in large differences in performance and privacy. Therefore, we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location. More >

  • Open Access

    ARTICLE

    AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

    Praveena Nuthakki1, Pavan Kumar T.1, Musaed Alhussein2, Muhammad Shahid Anwar3,*, Khursheed Aurangzeb2, Leenendra Chowdary Gunnam4

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4743-4756, 2024, DOI:10.32604/cmc.2024.058266 - 19 December 2024

    Abstract Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle More >

  • Open Access

    ARTICLE

    A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

    Juliet A. Murali1,*, Brindha T.2

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4659-4690, 2024, DOI:10.32604/cmc.2024.058115 - 19 December 2024

    Abstract Infrastructure as a Service (IaaS) in cloud computing enables flexible resource distribution over the Internet, but achieving optimal scheduling remains a challenge. Effective resource allocation in cloud-based environments, particularly within the IaaS model, poses persistent challenges. Existing methods often struggle with slow optimization, imbalanced workload distribution, and inefficient use of available assets. These limitations result in longer processing times, increased operational expenses, and inadequate resource deployment, particularly under fluctuating demands. To overcome these issues, a novel Clustered Input-Oriented Salp Swarm Algorithm (CIOSSA) is introduced. This approach combines two distinct strategies: Task Splitting Agglomerative Clustering (TSAC)… More >

  • Open Access

    ARTICLE

    Design and Develop Function for Research Based Application of Intelligent Internet-of-Vehicles Model Based on Fog Computing

    Abduladheem Fadhil Khudhur*, Ayça Kurnaz Türkben, Sefer Kurnaz

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3805-3824, 2024, DOI:10.32604/cmc.2024.056941 - 19 December 2024

    Abstract The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of… More >

  • Open Access

    ARTICLE

    Enhancing Septic Shock Detection through Interpretable Machine Learning

    Md Mahfuzur Rahman1,*, Md Solaiman Chowdhury2, Mohammad Shorfuzzaman3, Lutful Karim4, Md Shafiullah5, Farag Azzedin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2501-2525, 2024, DOI:10.32604/cmes.2024.055065 - 31 October 2024

    Abstract This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided… More >

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