Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks DOI
Yuhui Wang, Muhammad Junaid Farooq

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 11(8), P. 14727 - 14738

Published: Dec. 19, 2023

The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool this context, improving network performance through the Integrated access and backhaul (IAB) feature 5G. However, existing approaches to UAV-assisted enhancement face limitations dynamically adapting varying user locations demands. This paper introduces novel approach leveraging deep reinforcement learning (DRL) optimize UAV placement real-time, adjusting changing conditions requirements. Our method focuses on intricate balance between fronthaul links, critical aspect often overlooked current solutions. unique contribution work lies its ability autonomously position UAVs way that not only ensures robust connectivity ground users but also maintains seamless integration with central infrastructure. Through various simulated scenarios, we demonstrate how our effectively addresses these challenges, coverage areas. research fills significant gap 5G networks, providing scalable adaptive solution future mobile networks.

Language: Английский

Multi-objective deep reinforcement learning for computation offloading in UAV-assisted multi-access edge computing DOI
Xu Liu,

Zheng-Yi Chai,

Yalun Li

et al.

Information Sciences, Journal Year: 2023, Volume and Issue: 642, P. 119154 - 119154

Published: May 18, 2023

Language: Английский

Citations

26
Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems DOI Creative Commons
Ibrahim A. Elgendy, Souham Meshoul, Mohamed Hammad

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(4), P. 2625 - 2625

Published: Feb. 17, 2023

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive computation-intensive mobile applications services. Therefore, the Unmanned Aerial Vehicle (UAV) edge computing (MEC) paradigm offers low latency communication, computation, storage capabilities, which makes it an attractive way mitigate these limitations by offloading them. Nevertheless, majority schemes let IoT devices send intensive tasks connected server, predictably limits performance gain due overload. this paper, besides integrating task load balancing, we study resource allocation problem for multi-tier UAV-aided MEC systems. First, efficient load-balancing algorithm is designed optimizing among ground servers through handover process as well hovering UAVs over crowded areas are still loaded fixed location base stations server (GBSs). Moreover, formulate joint offloading, integer minimize system cost. Furthermore, based on deep reinforcement learning techniques proposed derive solution. Finally, experimental results show that approach not only has a fast convergence but also significantly lower cost when compared benchmark approaches.

Language: Английский

Citations

20
Unmanned Aerial Vehicle-based Applications in Smart Farming: A Systematic Review DOI Open Access
El Mehdi Raouhi, Mohamed Lachgar, Hamid Hrimech

et al.

International Journal of Advanced Computer Science and Applications, Journal Year: 2023, Volume and Issue: 14(6)

Published: Jan. 1, 2023

On one hand, the emergence of cutting-edge technologies like AI, Cloud Computing, and IoT holds immense potential in Smart Farming Precision Agriculture. These enable real-time data collection, including high-resolution crop imagery, using Unmanned Aerial Vehicles (UAVs). Leveraging these advancements can revolutionize agriculture by facilitating faster decision-making, cost reduction, increased yields. Such progress aligns with precision principles, optimizing practices for right locations, times, quantities. other integrating UAVs faces obstacles related to technology selection deployment, particularly acquisition image processing. The relative novelty UAV utilization Agriculture contributes lack standardized workflows. Consequently, widespread adoption implementation farming are hindered. This paper addresses challenges conducting a comprehensive review recent applications It explores common applications, types, techniques, processing methods provide clear understanding each technology's advantages limitations. By gaining insights into associated UAV-based Agriculture, this study aims contribute development workflows improve technologies.

Language: Английский

Citations

20
A Contract-Theory-Based Incentive Mechanism for UAV-Enabled VR-Based Services in 5G and Beyond DOI
Tri Nguyen Dang, Aunas Manzoor, Yan Kyaw Tun

et al.

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 10(18), P. 16465 - 16479

Published: April 19, 2023

The proliferation of novel infotainment services such as Virtual Reality(VR)-based has fundamentally changed the existing mobile networks. These bandwidth-hungry expanded at a tremendously rapid pace, thus, generating burden data traffic in To cope with this issue, one can use Multi-access Edge Computing (MEC) to bring resource edge. By doing so, we release core network by taking communication, computation, and caching resources nearby end-users (UEs). Nevertheless, due vast adoption VR-enabled devices, MEC might be insufficient peak times or dense settings. overcome these challenges, propose system model where service provider (SP) rent Unmanned Area Vehicles (UAVs) from UAV providers (USPs) serve micro-based stations (UBSs) that expand area improve spectrum efficiency. In which, pre-cached certain sets VR-based contents UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable 5G B5G communication interfaces, they communicate UAVs A2G links. significantly reduce considerable amount order successfully enable kinds services, an attractive incentive mechanism is required. Therefore, contract theory-based for UAV-assisted which offers reward serving UBS specific location time slots. We then derive optimal contract-based scheme individual rationality compatibility conditions. numerical findings show our proposed approach outperforms Linear Pricing (LP) technique close solution terms social welfare. Additionally, enhanced fairness utility asymmetric information problems.

Language: Английский

Citations

12
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

4
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

0
KJA: Kookaburra Jellyfish Algorithm Based Task Offloading in UAV‐Enabled Mobile Edge Computing Network DOI Open Access
Anand R. Umarji, Dharamendra Chouhan

International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(4)

Published: Feb. 10, 2025

ABSTRACT Mobile edge computing (MEC) is extensively utilized for supporting diverse mobile applications and the Internet of Things (IoT). One MEC's prime operations utilizing unmanned aerial vehicles (UAVs) included with MEC servers providing computational aids offloaded tasks by users in temporal hotspot regions or a few emerging situations like sports areas environmental disaster regions. However, despite various merits UAVs executed servers, it constrained their insufficient sensible energy consumption resources. Furthermore, owing to complication UAV‐aided systems, optimizations computation resource cannot be obtained better conventional optimization schemes. In this research, kookaburra jellyfish algorithm (KJA) presented task offloading UAV‐enabled network. The main objective enhance efficiency networks optimizing consumption, resources, communication time using KJA. Initially, network model simulated. Next, performed, thereafter, uploading carried out. Then, KJA consideration multiobjective models, namely, time, cost. Moreover, devised integrating (KOA) search optimizer (JSO). Afterward, process data transmission are conducted. addition, minimum energy, load, 0.448 J, 0.122, 1.036 s.

Language: Английский

Citations

0
Adaptive federated deep reinforcement learning for edge offloading in heterogeneous AGI-MEC networks DOI
Chenchen Fan, Qingling Wang

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)

Published: March 27, 2025

Language: Английский

Citations

0
Optimizing Task Offloading for Collaborative Unmanned Aerial Vehicles (UAVs) in Fog–Cloud Computing Environments DOI Creative Commons
Mohammad Aldossary

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 74698 - 74710

Published: Jan. 1, 2024

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications dynamic geographically distributed, which may require extensive real-time processing capabilities. Thus, data locally can be challenging due to limited computing To overcome such limitations, fog cloud facilitate application development by providing additional resource capacities when needed. Despite this, designing sophisticated efficient task offloading strategies that collaborate with technologies considering service latency energy consumption, is rarely addressed the literature. Therefore, a collaborative strategy for presented this work, leveraging advantages This approach aims minimize UAVs' as well provide required resources services real time. In addition, decisions formulated using Mixed-Integer Linear Programming (MILP) model reduce consumption of entire UAV-fog-cloud system optimizing allocation computation communication requested each UAV. The simulation results demonstrate proposed significantly 15.38%, 35.29%, 59.26%, decrease overall (including networking) 3.3%, 7.37%, 12% compared alternative standalone (namely UAV, fog, cloud).

Language: Английский

Citations

3
Resource-aware and computation offloading based on space–air–ground–sea integrated network DOI
Yanli Xu,

Jingxin Xu

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(5)

Published: March 18, 2025

Language: Английский

Citations

0