Zobrazeno 1 - 10
of 2 278
pro vyhledávání: '"multi-access edge computing"'
Publikováno v:
Alexandria Engineering Journal, Vol 109, Iss , Pp 176-190 (2024)
In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult fo
Externí odkaz:
https://doaj.org/article/0763ab4cdb3f402b93363a7f690ae719
Publikováno v:
ICT Express, Vol 10, Iss 3, Pp 620-625 (2024)
Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main cha
Externí odkaz:
https://doaj.org/article/26e01730ce2b4f93a5f7071d7ba534b8
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-24 (2024)
Abstract The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm h
Externí odkaz:
https://doaj.org/article/34d162c1282748dda8541d32a5f7da22
Autor:
Minh-Ngoc Tran, Younghan Kim
Publikováno v:
IEEE Access, Vol 12, Pp 163382-163395 (2024)
Serverless computing is a recent emerged trending technology in the 5G edge computing landscape. Serverless computing’s on-demand dynamic scalability allows efficient utilization of limited available resources at the edge. To realize the benefit se
Externí odkaz:
https://doaj.org/article/740de6feb3224360a1aed293b509341c
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 5, Pp 656-668 (2024)
In this paper, we address the limitations of existing deep learning (DL) methods for local misbehavior detection (LMBD) in vehicle-to-everything (V2X) communication systems by proposing an approach that combines rule-based and DL-based techniques. Co
Externí odkaz:
https://doaj.org/article/af94f964d215409d997ee9949b63176b
Publikováno v:
IEEE Access, Vol 12, Pp 147209-147219 (2024)
This paper designs a novel Hierarchical Federated Learning (HFL) management scheme, enabled by deep reinforcement learning (DRL), for multi-access edge computing (MEC) environments to accelerate convergence. To do this, the proposed scheme controls t
Externí odkaz:
https://doaj.org/article/1f03a7147ab048529adb57db322fc677
Publikováno v:
IEEE Access, Vol 12, Pp 130983-130994 (2024)
This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple networ
Externí odkaz:
https://doaj.org/article/38a4a2ea6da84ac88c6d6fd7476c05a3
Publikováno v:
IEEE Access, Vol 12, Pp 91634-91648 (2024)
Next-generation cellular networks offer enhanced-mobile broadband, ultra-reliable low latency, and massive machine-type communications. Conventional technology may not meet these demands due to complexity and dynamicity of the network and diverse tra
Externí odkaz:
https://doaj.org/article/c35185f5cd894ff18ccd4cfa80b31230
Publikováno v:
IEEE Access, Vol 12, Pp 69258-69275 (2024)
In modern emergency responses, unmanned aerial vehicles (UAVs) play a crucial role in redefining disaster management through diverse task execution (e.g., object detection). However, UAVs are usually resource-constrained. To address this issue, UAV m
Externí odkaz:
https://doaj.org/article/290406afd5ff46fbab18b7083b35b0db
Autor:
Tong Wang, Chuanchuan You
Publikováno v:
IEEE Access, Vol 12, Pp 63548-63567 (2024)
Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as promising solutions for enhancing the computational capabilities and reducing latency in next-generation wireless networks. However, the finite energy capacity
Externí odkaz:
https://doaj.org/article/2b0b781bc91c4a06ab572778e217f843