Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song^*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3585-3603, 2023, DOI:10.32604/cmc.2023.040068

Abstract Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by… More >

Hatem A. Alharbi¹, Barzan A. Yosuf², Mohammad Aldossary^3,*, Jaber Almutairi⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1299-1319, 2023, DOI:10.32604/csse.2023.039367

Abstract In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of efficiently processing and placing IoMT… More >

Yogesh Kumar^1,*, Jitender Kumar¹, Poonam Sheoran²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5785-5800, 2023, DOI:10.32604/cmc.2023.039276

Abstract On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains. Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’ cognitive capacity. However, the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines (VMs). Furthermore, any delays at the cloud end would further aggravate the miseries of real-time tasks. To resolve these issues, this paper proposes an auto-scaling framework (ACF) that strives to maintain the quality of service (QoS) for the… More >

Jyoti Yadav^*, Suman

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1063-1078, 2023, DOI:10.32604/iasc.2023.032883

Abstract Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the… 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

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 >

Shen Li¹, Fang Liu^1,*, 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 efficient edge computing accelerated architecture.… More >

Bokyun Jo¹, Md. Jalil Piran^2,*, Daeho Lee³, Doug Young Suh^4,*

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 enhance video without requiring much… More >

Yifei Wei^1,*, 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 learns through interactions with the… More >