Delay-energy-aware joint multi-cell association, service caching, and task offloading in hybrid-task heterogeneous edge computing networks DOI
Bassant Tolba, Maha Elsabrouty, Mohammed Abo‐Zahhad

et al.

Computer Networks, Journal Year: 2025, Volume and Issue: unknown, P. 111231 - 111231

Published: March 1, 2025

Language: Английский

A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning DOI
Arash Heidari, Nima Jafari Navimipour, Mohammad Ali Jabraeil Jamali

et al.

Sustainable Computing Informatics and Systems, Journal Year: 2023, Volume and Issue: 39, P. 100899 - 100899

Published: July 18, 2023

Language: Английский

Citations

38
Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature DOI Creative Commons
Vasilios Patsias, Petros Amanatidis, Dimitris Karampatzakis

et al.

Future Internet, Journal Year: 2023, Volume and Issue: 15(8), P. 254 - 254

Published: July 28, 2023

Task allocation in edge computing refers to the process of distributing tasks among various nodes an network. The main challenges task include determining optimal location for each based on requirements such as processing power, storage, and network bandwidth, adapting dynamic nature Different approaches centralized, decentralized, hybrid, machine learning algorithms. Each approach has its strengths weaknesses choice will depend specific application. In more detail, selection most methods depends architecture configuration type, like mobile (MEC), cloud-edge, fog computing, peer-to-peer etc. Thus, is a complex, diverse, challenging problem that requires balance trade-offs between multiple conflicting objectives energy efficiency, data privacy, security, latency, quality service (QoS). Recently, increased number research studies have emerged regarding performance evaluation optimization devices. While several survey articles described current state-of-the-art methods, this work focuses comparing contrasting different algorithms, well types are frequently used systems.

Language: Английский

Citations

25
Fog Offloading and Task Management in IoT-Fog-Cloud Environment: Review of Algorithms, Networks, and SDN Application DOI Creative Commons
Mohammad Reza Rezaee, Nor Asilah Wati Abdul Hamid, Masnida Hussin

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 39058 - 39080

Published: Jan. 1, 2024

The proliferation of Internet Things (IoT) devices and other IT forms in almost every area human existence has resulted an enormous influx data that must be managed stored. One viable solution to this issue is store handle massive amounts cloud environments. Real-time analysis always been critical. However, it becomes even more crucial as technology the IoT develop, new applications emerge, such autonomous cars, smart cities, for healthcare, agriculture, industries. Given volume data, moving a remote time-consuming produces severe network congestion, rendering administration rapid processing difficult. Fog computing provides close-to-device at network's periphery, fog can analyze near real-time. increased amount gadgets they produce formidable challenge nodes. Task offloading may enhance by excess nodes due restricted resources fog. Management tasks optimized devices. This review article overviews related works on task IoT-Fog-Cloud Environment. In addition, we discuss about networks Software-defined (SDN) challenges offloading.

Language: Английский

Citations

12
Task offloading strategies for mobile edge computing: A survey DOI
Shi Dong,

Junxiao Tang,

Khushnood Abbas

et al.

Computer Networks, Journal Year: 2024, Volume and Issue: unknown, P. 110791 - 110791

Published: Sept. 1, 2024

Language: Английский

Citations

12
Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey DOI Creative Commons

Tanmay Baidya,

Ahmadun Nabi,

Sangman Moh

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(6), P. 1837 - 1837

Published: March 13, 2024

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support Internet Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided computing, offloading decision plays central role optimizing overall system performance. However, trajectory directly affects decision. general, IoT devices use ground offload computation-intensive tasks on servers. The UAVs plan their trajectories based task generation rate. Therefore, researchers are attempting to optimize along trajectory, numerous studies ongoing determine impact decisions. this survey, we review existing trajectory-aware techniques by focusing design concepts, operational features, outstanding characteristics. Moreover, they compared terms principles Open issues research challenges discussed, future directions.

Language: Английский

Citations

8
Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues DOI
Ehzaz Mustafa, Junaid Shuja, Faisal Rehman

et al.

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 226, P. 103886 - 103886

Published: April 24, 2024

Language: Английский

Citations

7
MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network DOI

Yinzhi Guo,

Xiaolong Xu, Fu Xiao

et al.

Computer Networks, Journal Year: 2024, Volume and Issue: 245, P. 110352 - 110352

Published: March 30, 2024

Language: Английский

Citations

6
A comprehensive review on internet of things task offloading in multi-access edge computing DOI Creative Commons

Wang Dayong,

Kamalrulnizam Bin Abu Bakar,

Babangida Isyaku

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e29916 - e29916

Published: April 22, 2024

With the rapid development of Internet Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks higher-performance servers, thereby solving problems insufficient capacity and battery consumption TD. The emergence Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs access networks through multiple communication technologies supports mobility terminal devices. Review studies on offloading MEC have been extensive, but none them focus in MEC. To fill this gap, paper a comprehensive in-depth understanding algorithms mechanisms network. For each paper, main solved by mechanism, technical classification, evaluation methods, supported parameters extracted analyzed. Furthermore, shortcomings current research future trends discussed. This review will help potential researchers quickly understand panorama approaches find appropriate paths.

Language: Английский

Citations

6
A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches DOI
Peng Peng, Weiwei Lin, Wentai Wu

et al.

Computer Science Review, Journal Year: 2024, Volume and Issue: 53, P. 100656 - 100656

Published: June 29, 2024

Language: Английский

Citations

6
Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions DOI
Mobasshir Mahbub, Raed M. Shubair

Journal of Network and Computer Applications, Journal Year: 2023, Volume and Issue: 219, P. 103726 - 103726

Published: Aug. 26, 2023

Language: Английский

Citations

15