TY  - EJOU
AU  - Chen, Qingkun 
AU  - Wu, Qinmu 

TI  - Dynamic Networking Method of Vehicles in VANET
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - Vehicular Ad-hoc Networks (VANETs) make it easy to transfer information between vehicles, and this feature is utilized to enable collaborative decision-making between vehicles to enhance the safety, economy, and entertainment of vehicle operation. The high mobility of vehicles leads to a time-varying topology between vehicles, which makes inter-vehicle information transfer challenging in terms of delay control and ensuring the stability of collaborative decision-making among vehicles. The clustering algorithm is a method aimed at improving the efficiency of VANET communication. Currently, most of the research based on this method focuses on maintaining the stability of vehicle clustering, and few methods focus on the information interaction and collaborative decision-making of vehicles in the region. In this context, this paper proposes a networking method for intra-regional vehicle information interaction, through an efficient information transmission mechanism, vehicles can quickly obtain the required information and make more accurate decisions. Firstly, this networking method utilizes DBSCAN and the proposed vehicle scoring model to form clusters, ensuring the stability and adaptability of clusters; secondly, in the process of interacting with the information, the cosine similarity is utilized to check the similarity of the information to eliminate the highly similar information, effectively reducing redundant information; and lastly, in the case of a consensus reached by the cluster, the frequency of broadcasting of information between vehicles is reduced as a way to minimize the waste of communication resources. The proposed method is simulated based on Python and Sumo platforms, and several metrics such as cluster clustering situation, information volume, and state change rate are analyzed. The results show that the method maintains better cluster stability with a 60% and 92% reduction in information overhead compared to the FVC and HCAR algorithms, respectively.
KW  - Networking; VANET; messaging optimization

DO  - 10.32604/cmc.2024.054799