Vehicle-mounted ad-hoc network intersection prediction routing method based on CP (Counter Propagation) neural network

Abstract

The invention discloses a vehicle-mounted ad-hoc network intersection prediction routing method based on a CP (Counter Propagation) neural network, and mainly aims to solve the problems of frequent link fracture among nodes in a data packet forwarding section, large data packet transmission delay and low transmission success rate due to high-speed moving of vehicles, frequent change of network topology and a plurality of vehicle distribution densities in a traffic environment. According to the implementation scheme, a next section priority at which data packets are forwarded on an intersection is classified by use of the CP neural network; when a node arrives at the intersection, an average link communication probability of adjacent sections, average node densities of the adjacent sections and a distance ratio of a distance from a next intersection to a destination to a distance from the current intersection to the destination are taken as inputs of the CP neural network in one Hello message period; the priorities of adjacent data packet forwarding sections are taken as outputs; and a section with a highest priority is selected to be an optimal data packet forwarding section. Through adoption of the vehicle-mounted ad-hoc network intersection prediction routing method, the data packet forwarding success rate is increased, and the transmission delay of the data packets is shortened. The method can be applied to a vehicle-mounted ad-hoc network.

Description

technical field

[0001] The invention belongs to the technical field of communication, and mainly relates to a routing method in a vehicle ad hoc network, which can be used for routing decisions at road intersections in urban scenes. Background technique

[0002] In the traffic environment, the high-speed movement of vehicles, the frequently changing network topology and the various distribution of vehicle densities make the node links frequently broken, which has become a problem that VANET routing protocols must face and solve. The existing predictive routing algorithms at intersections have the following problems: First, some protocols do not consider vehicle density, and frequent link breaks occur in selected routes, resulting in data loss or increased delay; second, through statistical measurement methods or historical The average vehicle density is obtained as the optimal route based on statistical data. The vehicle density will change dynamically with the high-speed mo...

Images