Muntean, Gabriel-Miro ORCID: 0000-0003-2958-7979 and Tal, Irina ORCID: 0000-0001-9656-668X (2017) Towards reasoning vehicles: a survey of fuzzy logic-based solutions in vehicular networks. ACM Computing Surveys, 50 (6). ISSN 0360-0300
Abstract
Vehicular networks and their associated technologies enable an extremely varied plethora of applications and therefore attract increasing attention from a wide audience. However, vehicular networks also have many challenges that arise mainly due to their dynamic and complex environment. Fuzzy Logic, known for its ability to deal with complexity, imprecision, and model non-deterministic problems, is a very promising technology for use in such a dynamic and complex context. This article presents the first comprehensive survey of research on Fuzzy Logic approaches in the context of vehicular networks, and provides fundamental information which enables readers to design their own Fuzzy Logic systems in this context. As such, this article describes the Fuzzy Logic concepts with emphasis on their implementation in vehicular networks, includes classification and thorough analysis of the Fuzzy Logic-based solutions in vehicular networks, and discusses how Fuzzy Logic could be employed in the context of some of the key research directions in the 5G-enabled vehicular networks.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Additional Information: | Article Number 80 |
Uncontrolled Keywords: | Ad-hoc Networks; Mobile ad hoc Networks; Q-learning approach; Electric bicycles; Vanets; Communication architecture; Taxonomy; Fuzzy Logic; Smart Vehicles |
Subjects: | Computer Science > Artificial intelligence Computer Science > Computational complexity |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering |
Publisher: | Association for Computing Machinery (ACM) |
Official URL: | https://dx.doi.org/10.1145/3125640 |
Copyright Information: | © 2017 The Authors |
Funders: | Dublin City University under the Daniel O’Hare Research Scholarship scheme, Science Foundation Ireland grant 10/CE/I1855 to Lero and European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 688503 for the NEWTON project |
ID Code: | 25811 |
Deposited On: | 14 May 2021 10:39 by Vidatum Academic . Last Modified 19 Sep 2023 09:02 |
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