Intelligent Unmanned Air Vehicles for Public Safety Networks: Emerging Technologies and Research Directions

Zeeshan Kaleem; Ishtiaq Ahmad; Trung Q. Duong

doi:10.1007/978-981-19-1292-4_1

Intelligent Unmanned Air Vehicles for Public Safety Networks: Emerging Technologies and Research Directions

Zeeshan Kaleem^*, Ishtiaq Ahmad, Trung Q. Duong

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

Abstract

Unmanned air vehicles (UAVs) has shown great potential to enable numerous applications ranging from industry verticals to public safety communications. However, various challenges also arises with its integration into the existing terrestrial networks such as efficient UAV positioning, power allocation, trajectory design, and resource allocation. The existing conventional optimization solutions are not intelligent enough to overcome those challenges. Thus, real-time optimization and machine learning assisted solutions, and emerging technologies are required to overcome those challenges. To address those challenges, we summarized key technologies and research directions for UAV deployment at the edge or in the cell center, the power allocation and localization schemes, and the federated learning solutions.

Original language	English
Title of host publication	Unmanned System Technologies
Publisher	Springer
Pages	1-17
Number of pages	17
DOIs	https://doi.org/10.1007/978-981-19-1292-4_1
State	Published - 2022
Externally published	Yes

Publication series

Name	Unmanned System Technologies
ISSN (Print)	2523-3734
ISSN (Electronic)	2523-3742

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Federated learning
Intelligent UAVs
Machine learning
RIS
UAVs localization

ASJC Scopus subject areas

Control and Systems Engineering
Automotive Engineering
Computer Networks and Communications

Access to Document

10.1007/978-981-19-1292-4_1

Cite this

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abstract = "Unmanned air vehicles (UAVs) has shown great potential to enable numerous applications ranging from industry verticals to public safety communications. However, various challenges also arises with its integration into the existing terrestrial networks such as efficient UAV positioning, power allocation, trajectory design, and resource allocation. The existing conventional optimization solutions are not intelligent enough to overcome those challenges. Thus, real-time optimization and machine learning assisted solutions, and emerging technologies are required to overcome those challenges. To address those challenges, we summarized key technologies and research directions for UAV deployment at the edge or in the cell center, the power allocation and localization schemes, and the federated learning solutions.",

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PY - 2022

Y1 - 2022

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AB - Unmanned air vehicles (UAVs) has shown great potential to enable numerous applications ranging from industry verticals to public safety communications. However, various challenges also arises with its integration into the existing terrestrial networks such as efficient UAV positioning, power allocation, trajectory design, and resource allocation. The existing conventional optimization solutions are not intelligent enough to overcome those challenges. Thus, real-time optimization and machine learning assisted solutions, and emerging technologies are required to overcome those challenges. To address those challenges, we summarized key technologies and research directions for UAV deployment at the edge or in the cell center, the power allocation and localization schemes, and the federated learning solutions.

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KW - Machine learning

KW - RIS

KW - UAVs localization

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ER -

Intelligent Unmanned Air Vehicles for Public Safety Networks: Emerging Technologies and Research Directions

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this