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Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 21, Iss 6542, p 6542 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.
- Subjects :
- Data traffic
Vehicular ad hoc network
business.industry
Computer science
Chemical technology
resource allocation
TP1-1185
Review
Machine learning
computer.software_genre
Biochemistry
vehicular network
Atomic and Molecular Physics, and Optics
survey paper
Analytical Chemistry
Machine Learning
Resource allocation
Artificial intelligence
Electrical and Electronic Engineering
business
Instrumentation
computer
Algorithms
Resource utilization
Dynamic resource
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....30a2c1b04d802fa8e3f332fad18a35c3