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  • Open Access

    ARTICLE

    Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

    Maryam Alhassan, Abdul Raouf Khan*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2727-2740, 2023, DOI:10.32604/cmc.2023.037129

    Abstract Preserving privacy is imperative in the new unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture to ensure that sensitive information is protected and kept secure throughout the communication. Simultaneously, efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained. This study proposes a lightweight and efficient authentication scheme for the UAV-assisted MEC environment. The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardware-based solution with a… More >

  • Open Access

    ARTICLE

    Resource Management in UAV Enabled MEC Networks

    Muhammad Abrar1, Ziyad M. Almohaimeed2,*, Ushna Ajmal1, Rizwan Akram2, Rooha Masroor3, Muhammad Majid Hussain4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4847-4860, 2023, DOI:10.32604/cmc.2023.030242

    Abstract Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously.… More >

  • Open Access

    ARTICLE

    Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

    Abdelhamied A. Ateya1, Amel Ali Alhussan2,*, Hanaa A. Abdallah3, Mona A. Al duailij2, Abdukodir Khakimov4, Ammar Muthanna5

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031841

    Abstract Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration… More >

  • Open Access

    ARTICLE

    Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

    G. Pandiyan*, E. Sasikala

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 617-632, 2023, DOI:10.32604/iasc.2023.029337

    Abstract Mobile Edge Computing (MEC) assists clouds to handle enormous tasks from mobile devices in close proximity. The edge servers are not allocated efficiently according to the dynamic nature of the network. It leads to processing delay, and the tasks are dropped due to time limitations. The researchers find it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes. The challenge relies on the offloading decision on selection of edge nodes for offloading in a centralized manner. This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service… More >

  • Open Access

    ARTICLE

    Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning

    Shaoxuan Yun, Ying Chen*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1815-1835, 2023, DOI:10.32604/cmes.2022.022797

    Abstract Nowadays, with the widespread application of the Internet of Things (IoT), mobile devices are renovating our lives. The data generated by mobile devices has reached a massive level. The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load. Mobile Edge Computing (MEC) has been proposed to solve these problems. Because of limited computation ability and battery capacity, tasks can be executed in the MEC server. However, how to schedule those tasks becomes a challenge, and is the main topic of this piece. In this paper, we design an efficient intelligent algorithm… More >

  • Open Access

    ARTICLE

    Intelligent Resource Allocations for Software-Defined Mission-Critical IoT Services

    Chaebeen Nam1, Sa Math1, Prohim Tam1, Seokhoon Kim1,2,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4087-4102, 2022, DOI:10.32604/cmc.2022.030575

    Abstract Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated… More >

  • Open Access

    ARTICLE

    Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

    Mohamed K. Hussein1,*, Mohamed H. Mousa1,2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3685-3703, 2022, DOI:10.32604/cmc.2022.026370

    Abstract As the Internet of Things (IoT) and mobile devices have rapidly proliferated, their computationally intensive applications have developed into complex, concurrent IoT-based workflows involving multiple interdependent tasks. By exploiting its low latency and high bandwidth, mobile edge computing (MEC) has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices. In this study, we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment. The proposed task-based offloading strategy consists of an optimization problem that includes task dependency, communication costs, workflow constraints, device energy consumption, and the heterogeneous characteristics… More >

  • Open Access

    ARTICLE

    Multi-Classification and Distributed Reinforcement Learning-Based Inspection Swarm Offloading Strategy

    Yuping Deng1, Tao Wu1, Xi Chen2,*, Amir Homayoon Ashrafzadeh3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1157-1174, 2022, DOI:10.32604/iasc.2022.022606

    Abstract In meteorological and electric power Internet of Things scenarios, in order to extend the service life of relevant facilities and reduce the cost of emergency repair, the intelligent inspection swarm is introduced to cooperate with monitoring tasks, which collect and process the current scene data through a variety of sensors and cameras, and complete tasks such as emergency handling and fault inspection. Due to the limitation of computing resources and battery life of patrol inspection equipment, it will cause problems such as slow response in emergency and long time for fault location. Mobile Edge Computing is a promising technology, which… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

    Fadwa Alrowais1, Ahmed S. Almasoud2, Radwa Marzouk3, Fahd N. Al-Wesabi4,5, Anwer Mustafa Hilal6,*, Mohammed Rizwanullah6, Abdelwahed Motwakel6, Ishfaq Yaseen6

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2783-2795, 2022, DOI:10.32604/cmc.2022.025204

    Abstract Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling… More >

  • Open Access

    ARTICLE

    IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

    K. R. Uthayan1,*, G. Lakshmi Vara Prasad2, V. Mohan3, C. Bharatiraja4, Irina V. Pustokhina5, Denis A. Pustokhin6, Vicente García Díaz7

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5161-5177, 2022, DOI:10.32604/cmc.2022.021300

    Abstract Recent trends in communication technologies and unmanned aerial vehicles (UAVs) find its application in several areas such as healthcare, surveillance, transportation, etc. Besides, the integration of Internet of things (IoT) with cloud computing environment offers several benefits for the UAV communication. At the same time, aerial scene classification is one of the major research areas in UAV-enabled MEC systems. In UAV aerial imagery, efficient image representation is crucial for the purpose of scene classification. The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results. Therefore, the current research work… More >

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