Search
Abstract
Sailboat weather routing is a highly complex problem in terms of both the computational time and memory. The reason for this is a large search resulting in a multitude of possible routes and a variety of user preferences. Analysing all possible routes is only feasible for small sailing regions, low-resolution maps, or sailboat movements on a grid. Therefore, various heuristic approaches are often applied, which can find solutions within an acceptable time, sacrificing their optimality and accuracy. In this study, we propose a different approach based on the parallel implementation of an exact algorithm. Specifically, we present a Sailing Assistance Application (SAA) utilizing a deterministic approach and show how it can be parallelized in a cloud environment to reduce its execution time. The potential of the proposed parallelization method goes beyond the particular presented solution; it can be used to improve the performance of other weather routing tools such as collision avoidance and related applications.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (4)
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3303282
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
IEEE Access
no. 11,
pages 83896 - 83904,
ISSN: 2169-3536 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Życzkowski M., Szłapczyński R., Orzechowski P., Krawczyk H.: Parallel implementation of a Sailing Assistance Application in a Cloud Environment// IEEE Access -, (2023), s.1-1
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3303282
- Sources of funding:
-
- Finansowane z CI Task
- Verified by:
- Gdańsk University of Technology
seen 89 times
Recommended for you
Big Data and the Internet of Things in Edge Computing for Smart City
- J. Balicki,
- H. Balicka,
- P. Dryja
- + 1 authors
Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
- J. Balicki,
- H. Balicka,
- P. Dryja