Smart City Gnosys
Smart city article details
| Title | A Systematic Literature Review In Distributed Resource Allocation For C-V2X |
|---|---|
| ID_Doc | 5420 |
| Authors | Al-Najjar A.N.; Rasid M.F.A.; Hashim F.; Ahmad F.A.; Jamalipour A. |
| Year | 2024 |
| Published | Ingenierie des Systemes d'Information, 29, 3 |
| DOI | http://dx.doi.org/10.18280/isi.290301 |
| Abstract | Vehicular networks are the key paradigm of the Internet of Vehicles (IoV) as the extension of the Internet of Things (IoT) notion in Intelligent Transportation Systems (ITS) which can assist in the development of autonomous driving in smart cities. This technology can provide a wide variety of onboard data services, such as road safety, and increase traffic efficiency by connecting vehicles with road infrastructure and pedestrians. However, it is a challenging task to provide a satisfactory quality of service (QoS) to this network due to a number of limiting factors such as resource collision, resource interference, and congested channels because of the network topology and rapid changes produced by the high mobility as well as hardware imperfections and the anticipated growth of vehicular network devices. As a result, it will be essential to ensure that the resources of the available cellular network are allocated and used in the most efficient possible way. To achieve these goals, 3GPP has standardized the cellular vehicle to everything (C-V2X) with two versions, the long-term evolution-V2X (LTE-V2X) in Release 14 and the new radio-V2X (NR-V2X) in Release 16, as prominent technologies to improve resource allocation for vehicular networks. In order to capture the continuous effort for improving resource allocation, we present a systematic literature review (SLR) on distributed resource allocation (DRA) schemes for the two cellular-based vehicular network technologies. First, we discuss the technical configuration of resource allocation in the light of LTE-V2X and NR-V2X technologies and classify the state-of-the-art for each technology. Afterward, we explain the impact of machine learning (ML) and congestion control (CC) on the DRA. Then, we point out the primary performance metrics and simulation tools that were used in the related work. Ultimately, we highlight the challenges, open issues, and opportunities for DRA in C-V2X and outline several promising future research directions. © 2024 The authors. |
| Author Keywords | cellular vehicle to everything; congestion control; distributed resource allocation; long-term evolution-V2X; machine learning; new radio-V2X |
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