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  • 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

    5G Data Offloading Using Fuzzification with Grasshopper Optimization Technique

    V. R. Balaji1,*, T. Kalavathi2, J. Vellingiri3, N. Rajkumar4, Venkat Prasad Padhy5

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 289-301, 2022, DOI:10.32604/csse.2022.020971

    Abstract Data offloading at the network with less time and reduced energy consumption are highly important for every technology. Smart applications process the data very quickly with less power consumption. As technology grows towards 5G communication architecture, identifying a solution for QoS in 5G through energy-efficient computing is important. In this proposed model, we perform data offloading at 5G using the fuzzification concept. Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption. Two base stations, small (SB) and macro (MB) stations, are initialized and the first tasks randomly… More >

  • Open Access

    ARTICLE

    Exploring and Modelling IoT Offloading Policies in Edge Cloud Environments

    Jaber Almutairi1, Mohammad Aldossary2,*

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 611-624, 2022, DOI:10.32604/csse.2022.018112

    Abstract The Internet of Things (IoT) has recently become a popular technology that can play increasingly important roles in every aspect of our daily life. For collaboration between IoT devices and edge cloud servers, edge server nodes provide the computation and storage capabilities for IoT devices through the task offloading process for accelerating tasks with large resource requests. However, the quantitative impact of different offloading architectures and policies on IoT applications’ performance remains far from clear, especially with a dynamic and unpredictable range of connected physical and virtual devices. To this end, this work models the performance impact by exploiting a… More >

  • Open Access

    ARTICLE

    An Energy Aware Algorithm for Edge Task Offloading

    Ao Xiong1, Meng Chen1,*, Shaoyong Guo1, Yongjie Li2, Yujing Zhao2, Qinghai Ou3, Chuan Liu4, Siwen Xu5, Xiangang Liu6

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1641-1654, 2022, DOI:10.32604/iasc.2022.018881

    Abstract To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of… More >

  • Open Access

    ARTICLE

    Resource Management and Task Offloading Issues in the Edge–Cloud Environment

    Jaber Almutairi1, Mohammad Aldossary2,*

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 129-145, 2021, DOI:10.32604/iasc.2021.018480

    Abstract With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler for issues related to latency… More >

  • Open Access

    ARTICLE

    Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

    Mahmoud Alawi1, Raed Alsaqour2, Abdi Abdalla3, Maha Abdelhaq4,*, Mueen Uddin5

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2313-2337, 2021, DOI:10.32604/cmc.2021.018282

    Abstract The growing demands of vehicular network applications, which have diverse networking and multimedia capabilities that passengers use while traveling, cause an overload of cellular networks. This scenario affects the quality of service (QoS) of vehicle and non-vehicle users. Nowadays, wireless fidelity access points Wi-Fi access point (AP) and fourth generation long-term evolution advanced (4G LTE-A) networks are broadly accessible. Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks. However, utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task. This condition is due… More >

  • 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

    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 network. Also, it introduces 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

    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 improve the rate of requests… 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

    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, and the optimal solution is… 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

    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. For remote execution, recent studies… More >

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