VANET/V2X Routing From the Viewpoint of Non-Learning- and Learning-Based Approaches: A Survey

PROJECT TITLE :

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

ABSTRACT:

The widespread adoption of intelligent transportation systems (ITSs) can be attributed to the fact that these systems facilitate efficient coordination among connected vehicles. ITSs provide an integrated methodology for exchanging pertinent information in order to enhance the safety, efficiency, and dependability of road transportation systems. An integral part of intelligent transportation systems (ITSs) is a subset of mobile ad-hoc networks (MANETs) known as vehicular ad-hoc networks (VANETs). Vehicle-to-vehicle (VANET) networks are made up of vehicles that are connected to one another and equipped with sensing capabilities. These vehicles share data with one another regarding traffic, positioning, weather, and emergency services. Generally speaking, the term "vehicle-to-everything" (V2X) refers to Communications between any entity and a vehicle. The entity could be another vehicle, a cloud-based network, a pedestrian, or roadside equipment. The reliable and timely circulation of information among vehicular nodes is one of the most significant challenges faced by V2X. This is necessary in order to provide drivers with the ability to make decisions that will improve road safety. In this context, reliable and safe VANETs are supported by efficient V2X routing protocols, which play a key role in enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics that can significantly affect the routing in the network. These characteristics include high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size. All of these characteristics can be found in VANETs. There are many different routing protocols for V2X Communication that can be found in the open technical literature. Existing V2X routing protocols, as well as their contributions to and impacts on VANET performance, are discussed in this survey. The routing mechanisms are separated into non-learning and learning-based approaches, and both are categorized in this survey. The learning-based approach necessitates the application of various Machine Learning algorithms in this scenario. This survey also provides a summary of open challenges in the design of effective V2X routing protocols as well as future research directions that should be considered when developing intelligent routing mechanisms for next-generation VANET technologies.

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