Abednego Acheampong¹, Yiwen Zhang^1,*, Xiaolong Xu², Daniel Appiah Kumah²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 35-88, 2023, DOI:10.32604/cmes.2022.021394 - 24 August 2022

Abstract Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system’s operational environment. This study provides a thorough examination of current task offloading… More >

Darpan Majumder^1,*, S. Mohan Kumar²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1201-1217, 2023, DOI:10.32604/csse.2023.024868 - 15 June 2022

Abstract Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized… More >

Mohamed H. Mousa^1,2,*, Mohamed K. Hussein²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 413-434, 2022, DOI:10.32604/cmes.2022.020639 - 21 July 2022

Abstract Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution… More >

Chaebeen Nam¹, Sa Math¹, Prohim Tam¹, Seokhoon Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4087-4102, 2022, DOI:10.32604/cmc.2022.030575 - 16 June 2022

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… More >

Mohamed K. Hussein^1,*, Mohamed H. Mousa^1,2

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3685-3703, 2022, DOI:10.32604/cmc.2022.026370 - 16 June 2022

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 More >

Yuping Deng¹, Tao Wu¹, Xi Chen^2,*, Amir Homayoon Ashrafzadeh³

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1157-1174, 2022, DOI:10.32604/iasc.2022.022606 - 03 May 2022

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… More >

Nawaf Alhebaishi¹, Abdulrhman M. Alshareef¹, Tawfiq Hasanin¹, Raed Alsini¹, Gyanendra Prasad Joshi², Seongsoo Cho³, Doo Ill Chul^4,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5233-5250, 2022, DOI:10.32604/cmc.2022.025596 - 21 April 2022

Abstract In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this… More >

Fadwa Alrowais¹, Ahmed S. Almasoud², Radwa Marzouk³, Fahd N. Al-Wesabi^4,5, Anwer Mustafa Hilal^6,*, Mohammed Rizwanullah⁶, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2783-2795, 2022, DOI:10.32604/cmc.2022.025204 - 29 March 2022

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 More >

M. Vinoth Kumar¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1547-1559, 2022, DOI:10.32604/iasc.2022.023961 - 24 March 2022

Abstract Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim… More >

Suzhen Wang^1,*, Wenli Wang¹, Zhiting Jia¹, Chaoyi Pang²

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1241-1255, 2022, DOI:10.32604/csse.2022.024021 - 08 February 2022

Abstract With the rapid development and popularization of 5G and the Internet of Things, a number of new applications have emerged, such as driverless cars. Most of these applications are time-delay sensitive, and some deficiencies were found during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved at present. In 5 g environments, edge computing can better meet the needs of low delay and wide connection applications, and support the fast request of terminal users. However, edge computing only has the… More >