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  • Open Access

    ARTICLE

    Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

    Jaber Almutairi1, Mohammad Aldossary2,*,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4143-4160, 2021, DOI:10.32604/cmc.2021.018145 - 06 May 2021

    Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the More >

  • Open Access

    ARTICLE

    State-Based Offloading Model for Improving Response Rate of IoT Services

    K. Sakthidasan1, Bhekisipho Twala2, S. Yuvaraj3, K. Vijayan3,*, S. Praveenkumar3, Prashant Mani4, C. Bharatiraja3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3721-3735, 2021, DOI:10.32604/cmc.2021.014321 - 01 March 2021

    Abstract The Internet of Things (IoT) is a heterogeneous information sharing and access platform that provides services in a pervasive manner. Task and computation offloading in the IoT helps to improve the response rate and the availability of resources. Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing. In this paper, the state-based task offloading method (STOM) is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities. The proposed method is designed using the Markov decision-making model to… More >

  • Open Access

    ARTICLE

    An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

    Yuxin Xu1, Zilong Jin1,2,*, Xiaorui Zhang1, Lejun Zhang3

    Journal on Internet of Things, Vol.2, No.4, pp. 163-173, 2020, DOI:10.32604/jiot.2020.011792 - 22 September 2020

    Abstract The vehicle edge network (VEN) has become a new research hotspot in the Internet of Things (IOT). However, many new delays are generated during the vehicle offloading the task to the edge server, which will greatly reduce the quality of service (QOS) provided by the vehicle edge network. To solve this problem, this paper proposes an evolutionary algorithm-based (EA) task offloading and resource allocation scheme. First, the delay of offloading task to the edge server is generally defined, then the mathematical model of problem is given. Finally, the objective function is optimized by evolutionary algorithm, More >

  • Open Access

    ARTICLE

    Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

    Nithya Rekha Sivakumar1, *, Sara Ghorashi1, Mona Jamjoom1, Mai Alduailij1

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1925-1943, 2020, DOI:10.32604/cmc.2020.011505 - 16 September 2020

    Abstract In today’s world, smart phones offer various applications namely face detection, augmented-reality, image and video processing, video gaming and speech recognition. With the increasing demand for computing resources, these applications become more complicated. Cloud Computing (CC) environment provides access to unlimited resource pool with several features, including on demand self-service, elasticity, wide network access, resource pooling, low cost, and ease of use. Mobile Cloud Computing (MCC) aimed at overcoming drawbacks of smart phone devices. The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance.… More >

  • Open Access

    ARTICLE

    Edge Computing-Based Tasks Offloading and Block Caching for Mobile Blockchain

    Yong Yan1, Yao Dai2, *, Zhiqiang Zhou3, Wei Jiang4, Shaoyong Guo2

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 905-915, 2020, DOI:10.32604/cmc.2020.07425

    Abstract Internet of Things (IoT) technology is rapidly evolving, but there is no trusted platform to protect user privacy, protect information between different IoT domains, and promote edge processing. Therefore, we integrate the blockchain technology into constructing trusted IoT platforms. However, the application of blockchain in IoT is hampered by the challenges posed by heavy computing processes. To solve the problem, we put forward a blockchain framework based on mobile edge computing, in which the blockchain mining tasks can be offloaded to nearby nodes or the edge computing service providers and the encrypted hashes of blocks… More >

  • Open Access

    ARTICLE

    Optimization of Face Recognition System Based on Azure IoT Edge

    Shen Li1, Fang Liu1,*, Jiayue Liang1, Zhenhua Cai1, Zhiyao Liang2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1377-1389, 2019, DOI:10.32604/cmc.2019.06402

    Abstract With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture “client-cloud” face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and… More >

  • Open Access

    ARTICLE

    Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

    Yeting Guo1, Fang Liu2,*, Nong Xiao1, Zhengguo Chen1,3

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 777-792, 2019, DOI:10.32604/cmc.2019.06366

    Abstract Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes… More >

  • Open Access

    ARTICLE

    Efficient Computation Offloading in Mobile Cloud Computing for Video Streaming Over 5G

    Bokyun Jo1, Md. Jalil Piran2,*, Daeho Lee3, Doug Young Suh4,*

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 439-463, 2019, DOI:10.32604/cmc.2019.08194

    Abstract In this paper, we investigate video quality enhancement using computation offloading to the mobile cloud computing (MCC) environment. Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs. To do so, we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation (5G) cellular networks. In the proposed framework, the mobile client offloads the computational burden for the video enhancement to the cloud, which renders the side information needed to… More >

  • Open Access

    ARTICLE

    Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

    Yifei Wei1,*, Zhaoying Wang1, Da Guo1, F. Richard Yu2

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 89-104, 2019, DOI:10.32604/cmc.2019.04836

    Abstract To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user… More >

  • Open Access

    ARTICLE

    Seed Selection for Data Offloading Based on Social and Interest Graphs

    Ying Li1, Jianbo Li1,*, Jianwei Chen1, Minchao Lu1, Caoyuan Li2,3

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

    Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user More >

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