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  • Open Access

    ARTICLE

    A Trusted Edge Resource Allocation Framework for Internet of Vehicles

    Yuxuan Zhong1, Siya Xu1, Boxian Liao1, Jizhao Lu2, Huiping Meng2, Zhili Wang1, Xingyu Chen1,*, Qinghan Li3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2629-2644, 2023, DOI:10.32604/cmc.2023.035526

    Abstract With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving… More >

  • Open Access

    ARTICLE

    Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

    V. Dhilip Kumar1, J. Praveenchandar2, Muhammad Arif3,*, Adrian Brezulianu4, Oana Geman5, Atif Ikram3,6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2179-2188, 2023, DOI:10.32604/cmc.2023.034764

    Abstract Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage, processing power, and other computer system resources. It is also referred to as a system that will let the consumers utilize computational resources like databases, servers, storage, and intelligence over the Internet. In a cloud network, load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency. It is also known as a server pool or server farm. When a single node is overwhelmed, balancing the workload is needed to manage unpredictable workflows. The load balancer sends the load… More >

  • Open Access

    ARTICLE

    An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

    Chumei Wen1, Delu Zeng2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1617-1636, 2024, DOI:10.32604/cmes.2023.029864

    Abstract With the rapid development of Network Function Virtualization (NFV), the problem of low resource utilization in traditional data centers is gradually being addressed. However, existing research does not optimize both local and global allocation of resources in data centers. Hence, we propose an adaptive hybrid optimization strategy that combines dynamic programming and neural networks to improve resource utilization and service quality in data centers. Our approach encompasses a service function chain simulation generator, a parallel architecture service system, a dynamic programming strategy for maximizing the utilization of local server resources, a neural network for predicting the global utilization rate of… More >

  • Open Access

    ARTICLE

    Resource Allocation for IRS Assisted mmWave Wireless Powered Sensor Networks with User Cooperation

    Yonghui Lin1, Zhengyu Zhu2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 663-677, 2024, DOI:10.32604/cmes.2023.028584

    Abstract In this paper, we investigate IRS-aided user cooperation (UC) scheme in millimeter wave (mmWave) wireless-powered sensor networks (WPSN), where two single-antenna users are wireless powered in the wireless energy transfer (WET) phase first and then cooperatively transmit information to a hybrid access point (AP) in the wireless information transmission (WIT) phase, following which the IRS is deployed to enhance the system performance of the WET and WIT. We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots, power allocations, and the phase shifts of the IRS. Due to the non-convexity of the original problem, a semidefinite programming… More >

  • Open Access

    ARTICLE

    Satellite-Air-Terrestrial Cloud Edge Collaborative Networks: Architecture, Multi-Node Task Processing and Computation

    Sai Liu1, Zhenjiang Zhang1,*, Guangjie Han2, Bo Shen1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2651-2668, 2023, DOI:10.32604/iasc.2023.038477

    Abstract Integrated satellite-terrestrial network (ISTN) has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere. Being a promising paradigm, cloud computing and mobile edge computing (MEC) have been identified as key technology enablers for ISTN to further improve quality of service and business continuity. However, most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks, ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites. In addition, most computing tasks are transferred to cloud servers or offloaded to… More >

  • Open Access

    ARTICLE

    Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

    Ilyоs Abdullaev1, Natalia Prodanova2, K. Aruna Bhaskar3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Jungeun Kim8,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417

    Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,… More >

  • Open Access

    ARTICLE

    Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

    S. Kanmani1,*, M. Murali2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2583-2598, 2023, DOI:10.32604/csse.2023.037265

    Abstract The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks (DCN). As the network has the characteristics like decentralized and dynamic, few nodes in the network may not associate with other nodes. These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes. Namely, the nodes cause congestion, high delay, security concerns, and resource depletion. This study presents an effective selfish node detection method to address these problems. The Price of Anarchy (PoA) and the Price of Stability (PoS) in Game Theory with the Presence of… More >

  • Open Access

    ARTICLE

    BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

    A. Richard William1,*, J. Senthilkumar2, Y. Suresh2, V. Mohanraj2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

    Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the… More >

  • Open Access

    ARTICLE

    PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

    Zhenfang Liu*, Haibo Liu, Dongmei Zhang

    Energy Engineering, Vol.120, No.7, pp. 1717-1727, 2023, DOI:10.32604/ee.2023.027968

    Abstract To improve the operation efficiency of the photovoltaic power station complementary power generation system, an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed. Particle Swarm Optimization and BP neural network are used to establish the forecasting model, the Markov chain model is used to correct the forecasting error of the model, and the weighted fitting method is used to forecast the annual load curve, to complete the optimal allocation of complementary generating capacity of photovoltaic power stations. The experimental results show that this method reduces the average loss of photovoltaic output… More >

  • Open Access

    ARTICLE

    Reliable Scheduling Method for Sensitive Power Business Based on Deep Reinforcement Learning

    Shen Guo*, Jiaying Lin, Shuaitao Bai, Jichuan Zhang, Peng Wang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1053-1066, 2023, DOI:10.32604/iasc.2023.038332

    Abstract The main function of the power communication business is to monitor, control and manage the power communication network to ensure normal and stable operation of the power communication network. Communication services related to dispatching data networks and the transmission of fault information or feeder automation have high requirements for delay. If processing time is prolonged, a power business cascade reaction may be triggered. In order to solve the above problems, this paper establishes an edge object-linked agent business deployment model for power communication network to unify the management of data collection, resource allocation and task scheduling within the system, realizes… More >

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