Roadside unit (RSU) recruitment method based on roadside parking vehicles

Abstract

The invention discloses a roadside unit (RSU) recruitment method based on vehicles parked on the roadside, and relates to the field of roadside units. The method comprises the following steps: firstly, dividing a vehicle type and a city map; wherein the vehicles are divided into three types, namely, a parking roadside unit (PCRSU) alternative parking vehicle, a PCRSU and a user vehicle; the city map is divided into different types of point of interest (POI) areas according to different characteristics, and the POI areas are divided into different types of POI areas according to different characteristics. Secondly, estimating the parking time of the vehicle according to the position of the vehicle; and finally, taking the vehicle parking time as a decision-making element, and selecting the parking vehicle with the highest score as the PCRSU. According to the method, the parking time of the vehicles is estimated through a K-means-based Least Square (KLS) clustering method, the parking time is used as a decision element for selecting the PCRSU, and the parked vehicles are recruited. And under the condition that the coverage conditions are almost the same, the switching times of the system are reduced, the network stability is improved, and the correct transmission probability of the system is improved.

Description

technical field [0001] The invention relates to the field of Internet of Vehicles, in particular to an RSU recruitment method based on roadside parking vehicles. Background technique [0002] With the development of edge computing and Internet of Vehicles technologies, in order to meet users' needs for time-sensitive and computationally intensive tasks such as real-time traffic conditions, surrounding environment information, and online in-vehicle entertainment, it is necessary to continuously increase the construction of RSUs. It is estimated that the installation cost of only installing RSUs at intersections in American cities is as high as 18.63 billion US dollars, and the annual maintenance cost is about 1.1 billion US dollars. High installation and maintenance costs make dense deployment of RUS difficult. [0003] The vehicle itself has storage, computing and communication capabilities, and information can be cached and distributed from the data center in advance. Veh...

AI Technical Summary

Problems solved by technology

PCRSU has a great impact on the system service quality. If the performance of the recruited parked vehicles is poor in all aspects, it will lead to failure to provide satisfactory services to vehicle users and cause waste of system resources.

[0004] The current recruitment methods for parked vehicles do not consider the randomness of parked vehicles, nor do they consider the stability of the network

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