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.
Highlights
The vehicular network is the main component in smart mobility and is the main source of information and communication technology (ICT) in smart cities [1]
This paper presents a survey of machine learning algorithms implemented in vehicular network resource allocation
We mainly focused on the role of machine learning in the resource allocation strategy
Summary
The vehicular network is the main component in smart mobility and is the main source of information and communication technology (ICT) in smart cities [1]. Media, and communication technologies involved in V2X will require a mechanism for managing and assigning network resources so that the processing and exchanging of information can run properly. The vehicular network has a dynamic topology that is influenced by the movement of nodes so that the data transmission process must be executed during a very short period Due to these characteristics, the role of a dynamic resource allocation mechanism that can quickly adjust its allocation policy according to network conditions is needed so that network resources can be efficiently utilized. After perusing the papers, we found that a specific discussion on the role of AI algorithm implementation for a vehicular network resource allocation mechanism has never been performed before. Application of deep reinforcement learning (DRL) for communication and networking
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have