Neighbor Discovery for VANET With Gossip Mechanism and Multipacket Reception


Neighbor discovery, also known as ND, is an essential first step in the configuration of a network and a prerequisite for vehicular ad hoc networks (VANET). Nevertheless, the convergence efficiency of ND is being challenged by the requirements of multivehicle fast NetWorking of VANET with frequent changes in topology. The gossip-based information dissemination and sensing information-assisted ND with multipacket reception (GSIM-ND) algorithm for VANET is proposed in this article. In the event that multiple packets are received, the GSIM-ND algorithm makes use of gossip to facilitate the efficient dissemination of information (MPR). In addition, thanks to the multitarget detection function of the multiple sensors that are installed in roadside units (RSU), these units are able to detect the distribution of vehicles and assist individual vehicles in determining the distribution of their immediate surroundings. As a result, the GSIM-ND algorithm also makes use of the dissemination of sensing information. The critical metric that is used to evaluate the performance of the GSIM-ND algorithm is the expected number of neighbors that are discovered within a given period of time. This number is theoretically derived. In addition to this, the expected bounds of the number of time slots that must pass before a particular number of neighbors can be found have also been derived. The results of the simulation provide evidence that the theoretical derivation is accurate. It has been discovered that the GSIM-ND algorithm that is proposed in this article is always capable of quickly reaching the short-term convergence. In addition, in comparison to the completely random algorithm (CRA), the scan-based algorithm (SBA), and the gossip-based algorithm, the GSIM-ND algorithm demonstrates superior levels of efficiency and stability. The amount of time it takes for the GSIM-ND algorithm to converge is between 40 and 90 percent less than that required by these other algorithms, and this is true for both low density and high density networks. Therefore, GSIM-ND has the potential to increase the effectiveness of the ND algorithm.

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