Cited by Computational offloading into UAV swarm networks: a systematic literature review

Comprehensive survey on resource allocation for edge-computing-enabled metaverse DOI

Tanmay Baidya,

Sangman Moh

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100680 - 100680

Published: Sept. 9, 2024

Language: Английский

Citations

Comprehensive survey on reinforcement learning-based task offloading techniques in aerial edge computing DOI

Ahmadun Nabi,

Tanmay Baidya,

Sangman Moh

et al.

Internet of Things, Journal Year: 2024, Volume and Issue: 28, P. 101342 - 101342

Published: Aug. 23, 2024

Language: Английский

Citations

Offloading decision and resource allocation in aerial computing: A comprehensive survey DOI

Ahmadun Nabi,

Sangman Moh

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100734 - 100734

Published: Feb. 7, 2025

Language: Английский

Citations

Variations in Multi-Agent Actor–Critic Frameworks for Joint Optimizations in UAV Swarm Networks: Recent Evolution, Challenges, and Directions DOI

Muhammad Morshed Alam,

Sayma Akter Trina,

Tamim Hossain

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(2), P. 153 - 153

Published: Feb. 19, 2025

Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can efficiently perform surveillance, connectivity, computing, and energy transfer services for ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-hop selection, resource allocation, including transmit power, bandwidth, timeslots, caching, computing resources, to enhance network performance. Owing the highly dynamic topology, limited stringent quality of service requirements, lack global knowledge, optimizing performance in UAVSNs is very intricate. To address this, an adaptive joint optimization framework required handle both discrete continuous decision variables, ensuring optimal under various constraints. A multi-agent deep reinforcement learning-based actor–critic offers effective solution by leveraging its ability extract hidden features through agent interactions, generate hybrid actions uncertainty, adaptively learn with scalable generalization conditions. This paper explores recent evolutions frameworks deal problems proposing a novel taxonomy based on modifications internal neural structure. Additionally, key open research challenges are identified, potential solutions suggested as directions future UAVSNs.

Language: Английский

Citations

A question-centric review on DRL-based optimization for UAV-assisted MEC sensor and IoT applications, challenges, and future directions DOI

Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Huda Althumali

et al.

Vehicular Communications, Journal Year: 2025, Volume and Issue: unknown, P. 100899 - 100899

Published: Feb. 1, 2025

Language: Английский

Citations

Performance enhancement of UAV-enabled MEC systems through intelligent task offloading and resource allocation DOI

Mohsen Darchini-Tabrizi,

Amirali Pakdaman-Donyavi,

Reza Entezari‐Maleki

et al.

Computer Networks, Journal Year: 2025, Volume and Issue: unknown, P. 111280 - 111280

Published: April 1, 2025

Language: Английский

Citations

Energy-Efficient Trajectory Optimization Algorithm Based on K-Medoids Clustering and Gradient-Based Optimizer for Multi-UAV-Assisted Mobile Edge Computing Systems DOI

Mohamed Abdel‐Basset, Reda Mohamed, Doaa El-Shahat

et al.

Sustainable Computing Informatics and Systems, Journal Year: 2024, Volume and Issue: unknown, P. 101045 - 101045

Published: Oct. 1, 2024

Language: Английский

Citations

Computational offloading into UAV swarm networks: a systematic literature review DOI

Asrar Ahmed Baktayan, Ammar T. Zahary, Axel Sikora

et al.

EURASIP Journal on Wireless Communications and Networking, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Sept. 7, 2024

Language: Английский

Citations