VANET clustering method combining historical credit of vehicle with current state of vehicle

Abstract

The invention relates to the technical field of mobile communication and discloses a VANET clustering method combining the historical credit of a vehicle with the current state of the vehicle. According to the method, the historical credit R of the vehicle is modeled according to the transmission bandwidth, queue length and time for acting as the cluster head of the vehicle in a historical period; the current state function C of the vehicle is modeled according to the current state information, including the node degree and the speed and position of the vehicle relative to neighboring vehicles, of the vehicle; a utility function U=alpha R + (1-alpha)C is modeled according to the historical credit and the current state function of the vehicle, a candidate cluster head the corresponding utility function of which is the largest is selected to serve as a target cluster head, and then an adjacent user of the target cluster head is selected as a cluster member. By the adoption of the method, a VANET topological structure can be kept stable relatively, and service transmission performance can be optimized.

Description

technical field [0001] The invention relates to a clustering method of a vehicle self-organizing network, and belongs to the technical field of vehicle communication. Background technique [0002] In recent years, intelligent transportation systems based on VANET (Vehicular Ad-hoc NETwork) have received widespread attention at home and abroad. VANET adopts advanced communication and network technology, which can provide data communication between vehicles and between vehicles and roadside infrastructure. VANET combines Global Positioning System (GPS) and wireless communication technologies, such as wireless local area network (WLAN), cellular network, etc., to provide high-speed data access services for vehicles in high-speed movement, and to support information interaction between vehicles. It has become an effective technology to ensure vehicle driving safety and provide high-speed data communication, intelligent traffic management and in-vehicle entertainment. [0003] ...

AI Technical Summary

Problems solved by technology

[0005] The above clustering methods only consider vehicle speed, location and other characteristics, but do not comprehensively consider factors such as candidate cluster head resource availability and business transmission efficiency, which may lead to low network topology stability and severe limitations in data transmission performance.

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