Improved ACO For Node Deployment In VANET

Mobile ad hoc network (Ad Hoc) is a kind of temporary autonomous system, also known as non-infrastructure network or multi-hop network. Support and control the whole network has no center node, each node in the network is self management and can move, so the topology changes, real time no center and self organize into this kind of network has characteristics. Node is independent of each node, and other nodes to transmit information between routers, cannot directly communicate with other nodes to relay packets forwarding indirect communication. The vehicular ad hoc networks (Vehicular Ad hoc Network, VANET) is a mobile ad hoc network inone of the most popular application traffic management and city road construction.As the nodes in ad hoc networks is a special type of moving vehicles in the city road, not only affected the individual needs and social factors, there are great differences and unpredictable, and the network communication quality is also affected by the surrounding buildings, traffic and communication ability of nodes,the data transmission performance of the vehicle is affected, cause tremendous network delay. Therefore, the use of static auxiliary nodes to improve the communication performance of the vehicle between nodes, reduce the end-to-end delay, VANET is one of the important research directions in the field of research to improve communication quality.This paper first introduces the basic concept of VANET and related content, put forward to improve the success rate of data transmission in real road intersection set static auxiliary nodes, reduce the transmission delay of ideas, and for the static auxiliary node deployment problem, methods using grid will be the actual city road model, to cover all the nodes when the vehicle signal integrity minimal use of auxiliary nodes as the optimization objective, the ant colony and the particle swarm algorithm find the optimized deployment, influence and model grid number selection and the actual results of the grid to do the research, finally use the Matlab simulation software to verify the performance of the proposed method, proved the feasibility of the improved ant colony algorithm in sensor deployment problem in the study of.