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  • Open Access

    ARTICLE

    A Virtual Probe Deployment Method Based on User Behavioral Feature Analysis

    Bing Zhang, Wenqi Shi*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.067470 - 09 December 2025

    Abstract To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments, which results from anomaly detection mechanisms in location-based service (LBS) applications, this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis. The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns. First, we design an automated data extraction algorithm that recognizes graphical user interface (GUI) elements to collect spatio-temporal behavior data. Then, by analyzing the automatically collected user data, we identify normal users’ spatio-temporal patterns and extract their… More >

  • Open Access

    ARTICLE

    The Research on Low-Light Autonomous Driving Object Detection Method

    Jianhua Yang*, Zhiwei Lv, Changling Huo

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068442 - 10 November 2025

    Abstract Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target… More >

  • Open Access

    ARTICLE

    Optimized Deployment Method for Finite Access Points Based on Virtual Force Fusion Bat Algorithm

    Jian Li1,*, Qing Zhang2, Tong Yang2, Yu’an Chen2, Yongzhong Zhan3

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3029-3051, 2025, DOI:10.32604/cmes.2025.068644 - 30 September 2025

    Abstract In the deployment of wireless networks in two-dimensional outdoor campus spaces, aiming at the problem of efficient coverage of the monitoring area by limited number of access points (APs), this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm (VFBA) using the classical four-node regular distribution as an entry point. The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity, reduce the premature maturity problem caused by population convergence, avoid the over aggregation of individuals in the local optimal region, and enhance the superiority… More >

  • Open Access

    ARTICLE

    IECC-SAIN: Innovative ECC-Based Approach for Secure Authentication in IoT Networks

    Younes Lahraoui1, Jihane Jebrane2, Youssef Amal1, Saiida Lazaar1, Cheng-Chi Lee3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 615-641, 2025, DOI:10.32604/cmes.2025.067778 - 31 July 2025

    Abstract Due to their resource constraints, Internet of Things (IoT) devices require authentication mechanisms that are both secure and efficient. Elliptic curve cryptography (ECC) meets these needs by providing strong security with shorter key lengths, which significantly reduces the computational overhead required for authentication algorithms. This paper introduces a novel ECC-based IoT authentication system utilizing our previously proposed efficient mapping and reverse mapping operations on elliptic curves over prime fields. By reducing reliance on costly point multiplication, the proposed algorithm significantly improves execution time, storage requirements, and communication cost across varying security levels. The proposed authentication… More >

  • Open Access

    ARTICLE

    Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

    Peiying Zhang1,2,*, Yihong Yu1,2, Jing Liu3, Chong Lv1,2, Lizhuang Tan4,5, Yulin Zhang6,7,8

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654 - 19 May 2025

    Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

  • Open Access

    ARTICLE

    Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning

    Seyha Ros1,#, Seungwoo Kang1,#, Taikuong Iv1, Inseok Song1, Prohim Tam2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1649-1665, 2025, DOI:10.32604/cmc.2025.062716 - 16 April 2025

    Abstract Recently, Network Functions Virtualization (NFV) has become a critical resource for optimizing capability utilization in the 5G/B5G era. NFV decomposes the network resource paradigm, demonstrating the efficient utilization of Network Functions (NFs) to enable configurable service priorities and resource demands. Telecommunications Service Providers (TSPs) face challenges in network utilization, as the vast amounts of data generated by the Internet of Things (IoT) overwhelm existing infrastructures. IoT applications, which generate massive volumes of diverse data and require real-time communication, contribute to bottlenecks and congestion. In this context, Multi-access Edge Computing (MEC) is employed to support resource… More >

  • Open Access

    REVIEW

    A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment

    Muhammad Arif1,*, Muhammad Rashid2

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 13-64, 2025, DOI:10.32604/cmc.2025.062819 - 26 March 2025

    Abstract Edge Machine Learning (EdgeML) and Tiny Machine Learning (TinyML) are fast-growing fields that bring machine learning to resource-constrained devices, allowing real-time data processing and decision-making at the network’s edge. However, the complexity of model conversion techniques, diverse inference mechanisms, and varied learning strategies make designing and deploying these models challenging. Additionally, deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors. These factors underscore the necessity for a comprehensive literature review, as current reviews do not systematically encompass the most recent findings on these topics. Consequently, it provides… More >

  • Open Access

    ARTICLE

    A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

    Aiguo Li1, Yunfei Jia2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4573-4591, 2025, DOI:10.32604/cmc.2025.059184 - 06 March 2025

    Abstract In the context of security systems, adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel. Most studies focus on optimizing base station deployment under the assumption of static obstacles, aiming to maximize the perception coverage of wireless RF (Radio Frequency) signals and reduce positioning blind spots. However, in practical security systems, obstacles are subject to change, necessitating the consideration of base station deployment in dynamic environments. Nevertheless, research in this area still needs to be conducted. This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm (DIE-BDA)… More >

  • Open Access

    ARTICLE

    Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment

    Shumin Li1, Qifang Luo1,2,*, Yongquan Zhou1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1955-1994, 2025, DOI:10.32604/cmes.2025.059738 - 27 January 2025

    Abstract Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research. However, the current research on wireless sensor network deployment problems uses overly simplistic models, and there is a significant gap between the research results and actual wireless sensor networks. Some scholars have now modeled data fusion networks to make them more suitable for practical applications. This paper will explore the deployment problem of a stochastic data fusion wireless sensor network (SDFWSN), a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in… More >

  • Open Access

    ARTICLE

    Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

    Jiaying Shen1, Donglin Zhu1, Yujia Liu2, Leyi Wang1, Jialing Hu1, Zhaolong Ouyang1, Changjun Zhou1, Taiyong Li3,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 345-369, 2025, DOI:10.32604/cmc.2024.060335 - 03 January 2025

    Abstract The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life. The development of the Internet of Things (IoT) relies on the support of base stations, which provide a solid foundation for achieving a more intelligent way of living. In a specific area, achieving higher signal coverage with fewer base stations has become an urgent problem. Therefore, this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization (EPSO)… More >

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