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  • Open Access

    ARTICLE

    IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

    Dinesh Mavaluru1,*, Chettupally Anil Carie2, Ahmed I. Alutaibi3, Satish Anamalamudi2, Bayapa Reddy Narapureddy4, Murali Krishna Enduri2, Md Ezaz Ahmed1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

    Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… More >

  • Open Access

    ARTICLE

    A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

    Shengyao Sun1,2, Ying Du3, Jiajun Chen4, Xuan Zhang5, Jiwei Zhang6,*, Yiyi Xu7

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2879-2900, 2023, DOI:10.32604/cmc.2023.037483

    Abstract In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key… More >

  • Open Access

    ARTICLE

    A Hybrid Heuristic Service Caching and Task Offloading Method for Mobile Edge Computing

    Yongxuan Sang, Jiangpo Wei*, Zhifeng Zhang, Bo Wang

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2483-2502, 2023, DOI:10.32604/cmc.2023.040485

    Abstract Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud computing. In response to these challenges, mobile edge computing (MEC) has emerged as a new paradigm that extends the computational, caching, and communication capabilities of cloud computing. By caching certain services on edge nodes, computational support can be provided for requests that are offloaded to the edges. However, previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by services. This oversight can lead to problems, such as high delays in task executions and invalidation of offloading decisions. To optimize… 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

    A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

    Kaili Shao1, Hui Fu1, Ying Song2, Bo Wang3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

    Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… More >

  • Open Access

    ARTICLE

    Survey of Resources Allocation Techniques with a Quality of Service (QoS) Aware in a Fog Computing Environment

    Wan Norsyafizan W. Muhamad1, Kaharudin Dimyati2, Muhammad Awais Javed3, Suzi Seroja Sarnin1,*, Divine Senanu Ametefe1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1291-1308, 2023, DOI:10.32604/cmc.2023.037214

    Abstract The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network.… More >

  • Open Access

    ARTICLE

    Connected Vehicles Computation Task Offloading Based on Opportunism in Cooperative Edge Computing

    Duan Xue1,2, Yan Guo1,*, Ning Li1, Xiaoxiang Song1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 609-631, 2023, DOI:10.32604/cmc.2023.035177

    Abstract The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud.… More >

  • Open Access

    ARTICLE

    Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

    Sanghyuck Nam1, Suhwan Kwak1, Jaehwan Lee2, Sangoh Park1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 659-670, 2023, DOI:10.32604/csse.2023.034994

    Abstract Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process computation tasks within 100 ms which… More >

  • Open Access

    ARTICLE

    An Optimized Offloaded Task Execution for Smart Cities Applications

    Ahmad Naseem Alvi1, Muhammad Awais Javed1,*, Mozaherul Hoque Abul Hasanat2, Muhammad Badruddin Khan2, Abdul Khader Jilani Saudagar2, Mohammed Alkhathami2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6321-6334, 2023, DOI:10.32604/cmc.2023.029913

    Abstract Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes… More >

  • Open Access

    ARTICLE

    Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

    Alla Abbas Khadir1, Seyed Amin Hosseini Seno1,2,*, Baydaa Fadhil Dhahir2,3, Rahmat Budiarto4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2223-2234, 2023, DOI:10.32604/csse.2023.032316

    Abstract Fog computing became a traditional OffLad Destination (OLD) to compute the offloaded tasks of the Internet of Vehicles (IoV). Nevertheless, the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud. Thus, the IoV will incur additional offloading costs. In this paper, we propose a new offloading scheme by utilizing RoadSide Parked Vehicles (RSPV) as an alternative OLD for IoV. The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud. Finally, a performance evaluation of the proposed scheme… More >

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