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Journal articles on the topic 'Edge Computation Offloading'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Patel, Minal Parimalbhai, and Sanjay Chaudhary. "Edge Computing." International Journal of Fog Computing 3, no. 1 (2020): 64–74. http://dx.doi.org/10.4018/ijfc.2020010104.

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In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.

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Xiao, Yong, Ling Wei, Junhao Feng, and Wang En. "Two-tier end-edge collaborative computation offloading for edge computing." Journal of Computational Methods in Sciences and Engineering 22, no. 2 (2022): 677–88. http://dx.doi.org/10.3233/jcm-215923.

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Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative off

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Man, Junfeng, Longqian Zhao, Bowen Xu, Cheng Peng, Junjie Jiang, and Yi Liu. "Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode." Journal of Database Management 34, no. 1 (2023): 1–29. http://dx.doi.org/10.4018/jdm.318451.

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Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates

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4

Shan, Nanliang, Yu Li, and Xiaolong Cui. "A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing." Mathematical Problems in Engineering 2020 (June 27, 2020): 1–17. http://dx.doi.org/10.1155/2020/4124791.

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Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed tha

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Li, Feixiang, Chao Fang, Mingzhe Liu, Ning Li, and Tian Sun. "Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing." Electronics 12, no. 5 (2023): 1254. http://dx.doi.org/10.3390/electronics12051254.

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Edge computing is a promising technology to enable user equipment to share computing resources for task offloading. Due to the characteristics of the computing resource, how to design an efficient computation incentive mechanism with the appropriate task offloading and resource allocation strategies is an essential issue. In this manuscript, we proposed an intelligent computation offloading mechanism with content cache in mobile edge computing. First, we provide the network framework for computation offloading with content cache in mobile edge computing. Then, by deriving necessary and suffici

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Maftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai, and Ali Ouacha. "Intelligent task processing using mobile edge computing: processing time optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 143. http://dx.doi.org/10.11591/ijai.v13.i1.pp143-152.

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<p>The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capab

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Maftah, Sara, Ghmary Mohamed El, Bouabidi Hamid El, Mohamed Amnai, and Ali Ouacha. "Intelligent task processing using mobile edge computing: processing time optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 143–52. https://doi.org/10.11591/ijai.v13.i1.pp143-152.

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The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities,

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8

Lin, Li, Xiaofei Liao, Hai Jin, and Peng Li. "Computation Offloading Toward Edge Computing." Proceedings of the IEEE 107, no. 8 (2019): 1584–607. http://dx.doi.org/10.1109/jproc.2019.2922285.

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Sheng, Jinfang, Jie Hu, Xiaoyu Teng, Bin Wang, and Xiaoxia Pan. "Computation Offloading Strategy in Mobile Edge Computing." Information 10, no. 6 (2019): 191. http://dx.doi.org/10.3390/info10060191.

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Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy

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10

Huang, Yan-Yun, and Pi-Chung Wang. "Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing." Sensors 23, no. 3 (2023): 1155. http://dx.doi.org/10.3390/s23031155.

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Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of

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Rijal Abdullah, Noorulsadiqin Azbiya Yaacob, Anas A. Salameh, Nur Amalina Mohamad Zaki, and Nur Fadhilah Bahardin. "Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 11 (2024): 80–91. http://dx.doi.org/10.3991/ijim.v18i11.49051.

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Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource av

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Abbas, Aamir, Ali Raza, Farhan Aadil, and Muazzam Maqsood. "Meta-heuristic-based offloading task optimization in mobile edge computing." International Journal of Distributed Sensor Networks 17, no. 6 (2021): 155014772110230. http://dx.doi.org/10.1177/15501477211023021.

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With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has b

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Khan, Prince Waqas, Khizar Abbas, Hadil Shaiba, Ammar Muthanna, Abdelrahman Abuarqoub, and Mashael Khayyat. "Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach." Electronics 9, no. 6 (2020): 1010. http://dx.doi.org/10.3390/electronics9061010.

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Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having m

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Khera, Nikhil, Krishna Sarthak Tiwari, Vaibhav Tripathi, G. Sai Vinit, and Nagaraja J. "Literature Survey of Computation Offloading for Mobile Applications in Mobile Edge Computation." Journal of Computer Science Engineering and Software Testing 8, no. 1 (2022): 65–74. http://dx.doi.org/10.46610/jocses.2022.v08i01.005.

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Despite their progress and improvements, mobile devices are still regarded as restricted computer devices. Users are growing more discerning, expecting to be able to run computationally intensive applications on their smartphones or tablets. As a result, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing in order to use offloading techniques to increase the capabilities of mobile devices. Although offloading programmes to the cloud might improve mobile device performance, it can also increase processing delay. Inevitably, the quality of user service (QoS) suffers as a

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Lan, Yanwen, Xiaoxiang Wang, Chong Wang, Dongyu Wang, and Qi Li. "Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems." Electronics 8, no. 12 (2019): 1430. http://dx.doi.org/10.3390/electronics8121430.

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system,

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Shi, Yongpeng, Yujie Xia, and Ya Gao. "Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing." Information 11, no. 2 (2020): 96. http://dx.doi.org/10.3390/info11020096.

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As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to

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17

Gu, Xiaohui, Li Jin, Nan Zhao, and Guoan Zhang. "Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing." Mobile Information Systems 2019 (December 16, 2019): 1–9. http://dx.doi.org/10.1155/2019/3613250.

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Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme th

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Myyara, Marouane, Oussama Lagnfdi, Anouar Darif, and Abderrazak Farchane. "A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4186. http://dx.doi.org/10.11591/ijai.v13.i4.pp4186-4194.

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The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel gene

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Marouane, Myyara, Lagnfdi Oussama, Darif Anouar, and Farchane Abderrazak. "A new approach based on genetic algorithm for computation offloading optimization in multi-access edge computing networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4186–94. https://doi.org/10.11591/ijai.v13.i4.pp4186-4194.

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The proliferation of smart devices and the increasing demand for resource intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, optimizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic

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20

Liu, Xiaokai, Fangmin Xu, Ye Xiao, et al. "Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach." Mathematical Problems in Engineering 2022 (September 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/1486580.

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To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge comp

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Li, Xianwei, and Baoliu Ye. "Latency-Aware Computation Offloading for 5G Networks in Edge Computing." Security and Communication Networks 2021 (September 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/8800234.

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With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a c

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R., Aishwarya, and Mathivanan G. "Improved salp swarm algorithm based optimization of mobile task offloading." PeerJ Computer Science 11 (May 7, 2025): e2818. https://doi.org/10.7717/peerj-cs.2818.

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Background The realization of computation-intensive applications such as real-time video processing, virtual/augmented reality, and face recognition becomes possible for mobile devices with the latest advances in communication technologies. This application requires complex computation for better user experience and real-time decision-making. However, the Internet of Things (IoT) and mobile devices have computational power and limited energy. Executing these computational-intensive tasks on edge devices may result in high energy consumption or high computation latency. In recent times, mobile

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Divyashree, M., H. G. Rangaraju, and C. R. Revanna. "Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 1229–41. https://doi.org/10.11591/ijece.v15i1.pp1229-1241.

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The rise of intelligent and connected vehicles has led to new vehicularapplications, but vehicle computing capabilities remain limited. Mobileedge computing (MEC) can mitigate this by offloading computation tasksto the network's edge. However, limited computational capacities invehicles lead to increased latency and energy consumption. To address this,roadside units (RSUs) with cloud servers, known as edge computingdevices (ECDs), can be expanded to provide energy-efficient schedulingfor task computation. A new energy-efficient scheduling method calledmulti-objective optimization energy comput

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Xing, Yongli, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, and Zihui Liu. "A computational offloading optimization scheme based on deep reinforcement learning in perceptual network." PLOS ONE 18, no. 2 (2023): e0280468. http://dx.doi.org/10.1371/journal.pone.0280468.

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Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Exper

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Divyashree, M., H. G. Rangaraju, and C. R. Revanna. "Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 1229. http://dx.doi.org/10.11591/ijece.v15i1.pp1229-1241.

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The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energ

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Yang, Shicheng, Gongwei Lee, and Liang Huang. "Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks." Sensors 22, no. 11 (2022): 4088. http://dx.doi.org/10.3390/s22114088.

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This paper investigates the computation offloading problem in mobile edge computing (MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the MEC network by jointly optimizing the offloading decision and bandwidth allocation problems. The optimization of joint offloading decisions and bandwidth allocation is formulated as a mixed-integer programming (MIP) problem. In general, the problem can be efficiently generated by deep learning-based algorithms for offloading decisions and then solved by using traditional optimization methods. However, these methods are weak

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Sun, Chao, and Zhihua Li. "Multi-UAV-Assisted MEC Offloading-Optimization Method on Deep Reinforcement Learning." International Journal on Semantic Web and Information Systems 21, no. 1 (2025): 1–31. https://doi.org/10.4018/ijswis.368839.

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In multi-UAV-assisted mobile edge computing (MEC), insufficient consideration of collaborative computation in inter-UAV communication can significantly reduce computational service capabilities. For this problem, we present a multi-UAV-assisted MEC offloading optimization model that jointly optimizes task offloading decision, UAV resource allocation, UAV trajectories and establish collaborative computation through inter-UAV communication. First, to solve the multi-UAV-assisted MEC offloading optimization issue, we define a weighted utility function that balances delay and energy consumption. T

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Zhang, Peiying, Yu Su, Boxiao Li, et al. "Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing." Drones 7, no. 3 (2023): 213. http://dx.doi.org/10.3390/drones7030213.

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Traditional multi-access edge computing (MEC) often has difficulty processing large amounts of data in the face of high computationally intensive tasks, so it needs to offload policies to offload computation tasks to adjacent edge servers. The computation offloading problem is a mixed integer programming non-convex problem, and it is difficult to have a good solution. Meanwihle, the cost of deploying servers is often high when providing edge computing services in remote areas or some complex terrains. In this paper, the unmanned aerial vehicle (UAV) is introduced into the multi-access edge com

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Wang, Yanyan, Lin Wang, Ruijuan Zheng, Xuhui Zhao, and Muhua Liu. "Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes." Sensors 21, no. 7 (2021): 2347. http://dx.doi.org/10.3390/s21072347.

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In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time s

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Maray, Mohammed, and Junaid Shuja. "Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues." Mobile Information Systems 2022 (June 28, 2022): 1–17. http://dx.doi.org/10.1155/2022/1121822.

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Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offl

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Sun, Dingmi, Yimin Chen, and Hao Li. "Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning." Mathematics 12, no. 3 (2024): 424. http://dx.doi.org/10.3390/math12030424.

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As distributed computing evolves, edge computing has become increasingly important. It decentralizes resources like computation, storage, and bandwidth, making them more accessible to users, particularly in dynamic Telematics environments. However, these environments are marked by high levels of dynamic uncertainty due to frequent changes in vehicle location, network status, and edge server workload. This complexity poses substantial challenges in rapidly and accurately handling computation offloading, resource allocation, and delivering low-latency services in such a variable environment. To

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Fayi, Sharifah Yaqoub, and Zhengguo Sheng. "A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain." Open Research Europe 3 (July 7, 2023): 110. http://dx.doi.org/10.12688/openreseurope.16189.1.

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Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new

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Fayi, Sharifah Yaqoub, and Zhengguo Sheng. "A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain." Open Research Europe 3 (October 25, 2023): 110. http://dx.doi.org/10.12688/openreseurope.16189.2.

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Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new

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Babar, Mohammad, and Muhammad Sohail Khan. "ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems." International Journal of Distributed Sensor Networks 17, no. 7 (2021): 155014772110353. http://dx.doi.org/10.1177/15501477211035332.

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Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT de

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Wu, Jian, Min Jia, Liang Zhang, and Qing Guo. "DNNs Based Computation Offloading for LEO Satellite Edge Computing." Electronics 11, no. 24 (2022): 4108. http://dx.doi.org/10.3390/electronics11244108.

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Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user’s offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus

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Huang, Binbin, Yangyang Li, Zhongjin Li, et al. "Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing." Wireless Communications and Mobile Computing 2019 (December 23, 2019): 1–20. http://dx.doi.org/10.1155/2019/3816237.

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With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping a

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Khisa, Shreya, and Sangman Moh. "Dynamic Computation Offloading Based on Q-Learning for UAV-Based Mobile Edge Computing." Korean Institute of Smart Media 12, no. 3 (2023): 68–76. http://dx.doi.org/10.30693/smj.2023.12.3.68.

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Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-cons

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Lu, Yanfei, Zengzi Chen, Qinghe Gao, Tao Jing, and Jin Qian. "A Mobility-Aware and Sociality-Associate Computation Offloading Strategy for IoT." Wireless Communications and Mobile Computing 2021 (June 25, 2021): 1–12. http://dx.doi.org/10.1155/2021/9919541.

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Mobile edge computing, a promising paradigm, brings services closer to a user by leveraging the available resources in an edge network. The crux of MEC is to reasonably allocate resources to satisfy the computing requirements of each node in the network. In this paper, we investigate the service migration problem of the offloading scheme in a power-constrained network consisting of multiple mobile users and fixed edge servers. We propose an affinity propagation-based clustering-assisted offloading scheme by taking into account the users’ mobility prediction and sociality association between mo

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Chen, Shuang, Ying Chen, Xin Chen, and Yuemei Hu. "Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks." Complexity 2020 (May 4, 2020): 1–14. http://dx.doi.org/10.1155/2020/7016307.

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With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission

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Wang, Mingzhi, Tao Wu, Xiaochen Fan, Penghao Sun, Yuben Qu, and Panlong Yang. "TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks." Wireless Communications and Mobile Computing 2021 (November 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/3877285.

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With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth

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Myyara, Marouane, Oussama Lagnfdi, Anouar Darif, and Abderrazak Farchane. "Quality of Experience Improvement and Service Time Optimization through Dynamic Computation Offloading Algorithms in Multi-access Edge Computing Networks." International Journal of Computer Network and Information Security 16, no. 4 (2024): 1–16. http://dx.doi.org/10.5815/ijcnis.2024.04.01.

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Multi-access Edge Computing optimizes computation in proximity to smart mobile devices, addressing the limitations of devices with insufficient capabilities. In scenarios featuring multiple compute-intensive and delay-sensitive applications, computation offloading becomes essential. The objective of this research is to enhance user experience, minimize service time, and balance workloads while optimizing computation offloading and resource utilization. In this study, we introduce dynamic computation offloading algorithms that concurrently minimize service time and maximize the quality of exper

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Changyuan Xu, Changyuan Xu, Cheng Zhan Changyuan Xu, Jingrui Liao Cheng Zhan, and Bin Zeng Jingrui Liao. "UAV-Enabled Mobile Edge Computing with Binary Computation Offloading and Energy Constraints." 網際網路技術學刊 23, no. 5 (2022): 947–54. http://dx.doi.org/10.53106/160792642022092305003.

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<p>Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading and energy constraints, where an energy-limited UAV is employed as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum comput

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Hasanin, Tawfiq, Aisha Alsobhi, Adil Khadidos, Ayman Qahmash, Alaa Khadidos, and Gabriel Ayodeji Ogunmola. "Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm." Applied Bionics and Biomechanics 2021 (November 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/9014559.

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Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensi

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Maftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai, and Ali Ouacha. "Optimal Task Processing and Energy Consumption Using Intelligent Offloading in Mobile Edge Computing." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 20 (2022): 130–42. http://dx.doi.org/10.3991/ijim.v16i20.34373.

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The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable para

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Huang, Mengxing, Qianhao Zhai, Yinjie Chen, Siling Feng, and Feng Shu. "Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing." Sensors 21, no. 8 (2021): 2628. http://dx.doi.org/10.3390/s21082628.

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Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have

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Feng, Xue, Chi Xu, Xi Jin, Changqing Xia, and Jing Jiang. "Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning." Applied Sciences 14, no. 23 (2024): 11160. http://dx.doi.org/10.3390/app142311160.

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To address the end-edge computation offloading challenge in the multi-terminal and multi-server environment, this paper proposes an intelligent computation offloading algorithm based on Lyapunov optimization and deep reinforcement learning. We formulate a network computation rate maximization problem while balancing constraints including offloading time, CPU frequency, energy consumption, transmission power, and data queue stability. Due to the fact that the problem is mixed integer nonlinear programming, we transform it into a deterministic problem based on Lyapunov optimization theory, and t

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Chen, Long, Jiaqi Du, and Xia Zhu. "Mobility-Aware Task Offloading and Resource Allocation in UAV-Assisted Vehicular Edge Computing Networks." Drones 8, no. 11 (2024): 696. http://dx.doi.org/10.3390/drones8110696.

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The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict veh

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Liu, Jun, Xiaohui Lian, and Chang Liu. "Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network." Future Internet 13, no. 5 (2021): 128. http://dx.doi.org/10.3390/fi13050128.

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In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the moveme

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Chu, Xiao, and Ze Leng. "Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment." Wireless Communications and Mobile Computing 2022 (March 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/6107893.

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As traditional cloud computing is not efficient enough to support large-scale computational task execution in IoT environments, a task offloading and resource allocation algorithm for mobile edge computing (MEC) is proposed in this paper. First, a multiuser computation offloading model is constructed, including a communication model and computation offloading model, which is transformed into the minimization of users’ time delay and energy consumption (i.e., total system overhead) in the MEC system. Then, the task offloading model is formulated into a Markov decision process, and an offloading

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Zheng, Yi-fan, Ning Wei, and Yi Liu. "Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge Computing." Mobile Information Systems 2022 (July 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/4840801.

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Computation offloading and caching strategy is a well-established concept for allowing mobile applications that are high in resources. Furthermore, the unloaded duties can be replicated when several customers are within easy access because of the rising mobile cooperation applications. However, the problematic characteristics of offloading and caching strategy delay bandwidth transfer from mobile computing devices to cloud computing. A new technical approach to restrict the issues and unwanted functions in offloading and caching is called the intellectual power computing framework (IPCF). IPCF

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