scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
chathuranga basnayaka
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Lessons Learned ◽  
Low Latency ◽  
Focal Points ◽  
Future Directions ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
chathuranga basnayaka
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Lessons Learned ◽  
Low Latency ◽  
Focal Points ◽  
Future Directions ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
Chathuranga M. Wijerathna Basnayaka ◽  
Dushantha Nalin K. Jayakody
Keyword(s):  
Research Work ◽  
Communication Technologies ◽  
Lessons Learned ◽  
Optimization Techniques ◽  
Network Architectures ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies ◽  
Communication Methods

With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods. Encryption and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are discussed. Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications. Our literature review reveals the need for more research work on drone−to−drone and drone−to−device communications.

Ultra-Reliable and Low-Latency Communications in Unmanned Aerial Vehicle Communication Systems

2019 ◽  
Vol 67 (5) ◽  
pp. 3768-3781 ◽  
Author(s):  
Changyang She ◽  
Chenxi Liu ◽  
Tony Q. S. Quek ◽  
Chenyang Yang ◽  
Yonghui Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Communication Systems ◽  
Low Latency ◽  
Vehicle Communication ◽  
Aerial Vehicle

scholarly journals Machine Learning and Radiogenomics: Lessons Learned and Future Directions

2018 ◽  
Vol 8 ◽  
Author(s):  
John Kang ◽  
Tiziana Rancati ◽  
Sangkyu Lee ◽  
Jung Hun Oh ◽  
Sarah L. Kerns ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lessons Learned ◽  
Future Directions

Unmanned Aerial Vehicle Operational Test and Evaluation Lessons Learned

2003 ◽  
Author(s):  
Lee Carr ◽  
Kristen Lambrecht ◽  
Scott Shaw ◽  
Greg Whittier ◽  
Catherine Warner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Lessons Learned ◽  
Test And Evaluation ◽  
Operational Test ◽  
Aerial Vehicle ◽  
Operational Test And Evaluation

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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