A novel blockchain enabled resource allocation and task offloading strategy in cloud computing environment DOI Creative Commons

G. Senthilkumar,

K. N. Madhusudhan,

Y. Jeyasheela

и другие.

Automatika, Год журнала: 2024, Номер 65(3), С. 973 - 982

Опубликована: Март 12, 2024

Large amounts of processing resources are required for the sensed raw big data during generation process. Furthermore, as typically privacy sensitive, blockchain technology can be used to ensure concerns. This study examines a multiuser mobile offloading network that consists cloud server located remotely and an edge node. We formulate problem joint optimization task decision making all users, computation resource allocation among executing applications, radio assignment remote-processing applications. The goal is minimize maximum weighted cost users. When compared other benchmark approaches, simulation results show proposed algorithm achieves optimal in terms both energy consumption delay result collaboration. Finally strategy with 93% efficiency obtained.

Язык: Английский

MCOTM: Mobility-aware computation offloading and task migration for edge computing in industrial IoT DOI
Wei Qin, Haiming Chen, Lei Wang

и другие.

Future Generation Computer Systems, Год журнала: 2023, Номер 151, С. 232 - 241

Опубликована: Окт. 10, 2023

Язык: Английский

Процитировано

24

An overview of mobility awareness with mobile edge computing over 6G network: Challenges and future research directions DOI Creative Commons
Soule Issa Loutfi, Ibraheem Shayea, Ufuk Türeli

и другие.

Results in Engineering, Год журнала: 2024, Номер 23, С. 102601 - 102601

Опубликована: Июль 22, 2024

The evolution of science has given rise to many technologies that have changed the world. upcoming Six-Generation (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is paradigm technology emerges as key major supporter mobility awareness. computing offers improved efficiency for service migration from edge node user. However, management MEC complex challenge seamless handovers between nodes must be efficiently executed ensure uninterrupted devices, demanding intricate coordination low-latency decision-making. To best author's knowledge, there been no comprehensive work on most recent developments awareness using 6G. paper aims present general overview intersection over 6G networks. concept networks comprehensively introduced. This will highlight integration bringing more efficient edge, reducing latency, user experience. Meanwhile, survey discusses augmented reality with applications. applications emphasizes need results providing communication during serving base station target station. study contributes understanding trends enable operation communication. Furthermore, we delve into challenges future research directions networks, underlining complexities potentials integrating computing.

Язык: Английский

Процитировано

11

A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study DOI Creative Commons

Ahmed A. Ismail,

Nour Eldeen M. Khalifa, Reda A. El-Khoribi

и другие.

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

2

Reinforcement learning for intelligent online computation offloading in wireless powered edge networks DOI
Ehzaz Mustafa, Junaid Shuja, Kashif Bilal

и другие.

Cluster Computing, Год журнала: 2022, Номер 26(2), С. 1053 - 1062

Опубликована: Авг. 12, 2022

Язык: Английский

Процитировано

37

Energy efficient offloading scheme for MEC-based augmented reality system DOI
Abdelhamied A. Ateya, Ammar Muthanna, Andrey Koucheryavy

и другие.

Cluster Computing, Год журнала: 2023, Номер 26(1), С. 789 - 806

Опубликована: Янв. 9, 2023

Язык: Английский

Процитировано

18

Machine learning-based computation offloading in edge and fog: a systematic review DOI

Sanaz Taheri-abed,

Amir Masoud Eftekhari Moghadam, Mohammad Hossein Rezvani

и другие.

Cluster Computing, Год журнала: 2023, Номер 26(5), С. 3113 - 3144

Опубликована: Июль 21, 2023

Язык: Английский

Процитировано

18

Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues DOI
Ehzaz Mustafa, Junaid Shuja, Faisal Rehman

и другие.

Journal of Network and Computer Applications, Год журнала: 2024, Номер 226, С. 103886 - 103886

Опубликована: Апрель 24, 2024

Язык: Английский

Процитировано

7

Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare DOI Creative Commons
Muhammad Mateen Yaqoob, Muhammad Nazir, Abdullah Yousafzai

и другие.

Applied Sciences, Год журнала: 2022, Номер 12(23), С. 12080 - 12080

Опубликована: Ноя. 25, 2022

Heart disease is one of the lethal diseases causing millions fatalities every year. The Internet Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection disease. biomedical data collected using IoMT contains personalized information about patient this has serious privacy concerns. To overcome issues, several protection laws are proposed internationally. These created huge problem for techniques used traditional machine learning. We propose framework on federated matched averaging with modified Artificial Bee Colony (M-ABC) optimization algorithm to issues improve method prediction heart paper. technique improves accuracy, classification error, communication efficiency as compared state-of-the-art learning algorithms real-world dataset.

Язык: Английский

Процитировано

28

DQN-enabled content caching and quantum ant colony-based computation offloading in MEC DOI
Chunlin Li, Yong Zhang, Youlong Luo

и другие.

Applied Soft Computing, Год журнала: 2022, Номер 133, С. 109900 - 109900

Опубликована: Дек. 2, 2022

Язык: Английский

Процитировано

25

Deep Reinforcement Learning for Multi-Hop Offloading in UAV-Assisted Edge Computing DOI
Tiến Hoa Nguyễn, Do Van Dai, Le Lan

и другие.

IEEE Transactions on Vehicular Technology, Год журнала: 2023, Номер 72(12), С. 16917 - 16922

Опубликована: Июль 6, 2023

In this paper, we propose a unmanned aerial vehicle (UAV)-assisted multi-hop edge computing (UAV-assisted MEC) system in which UE can offload its task to multiple UAVs fashion. particular, the offloads nearby UAV, and UAV execute part of received remaining neighboring UAV. The offloading process continues until execution is finished. benefit multihop that be finished faster, load shared among UAVs, thus avoiding overloading congestion. Each node, i.e., or needs determine size for minimize cumulative energy consumption latency over nodes. We formulate stochastic optimization problem under dynamics uncertainty UAV-assisted MEC system. Then, deep reinforcement learning (DRL) algorithm solve problem. Simulation results are provided demonstrate effectiveness DRL algorithm.

Язык: Английский

Процитировано

15