Research On Swarm Intelligence Routing Optimization Technology Based On Connectivity Prediction In Vehicular Communication

The fast-changing Internet of Things(IoT)has led to the rapid development of service-driven network intelligence.As a key application of Intelligent Transport System(ITS),the vehicular communication has drawn much attention.The notable characteristic of vehicular communication network is the time-varying characteristic of the network topology caused by the rapid movement of nodes,so how to obtain high communication link connectivity and efficient data transmission efficiency under such a high dynamic network environment is a key issue to be solved urgently.Focusing on the characteristics and business requirements of vehicular communication network,this paper focuses on the swarm intelligence routing optimization technology based on connectivity prediction of vehicular ad hoc network.Main work of this paper is as follows:(1)This paper introduces the research status of vehicular communication network,summarizes the network architecture and network characteristics,and analyzes the research difficulties and research directions.The characteristics of some kinds of mainstream swarm intelligence algorithms are summarized,and the feasibility and practicability of using ant colony algorithm for route optimization in vehicular ad hoc network are discussed.(2)A connectivity prediction model based on channel transmission environment for a vehicular ad hoc network is studied in this paper.Under the premise of interference of signals between vehicles,the prediction of connectivity probability under complex dynamic network environment is realized by research on the relationship between the number of vehicle nodes,the signal interference and connectivity probability through traffic flow modeling and channel analysis based on Nakagami fading.The simulation results show that increasing the number of vehicle nodes within a certain range can improve the connectivity between vehicles.However,when the number exceeds the threshold,connectivity will actually decrease.This provides a precondition for the vehicular network to maintain reliable link connectivity and select an efficient data transmission path.(3)Ant Colony Routing Optimization Algorithm with Optimal Energy Efficiency Based on Connectivity Prediction is studied based on connectivity prediction.In order to solve the problem of large routing overhead and unreasonable resource utilization in vehicular ad hoc network,an ant colony routing optimization algorithm with optimal energy efficiency is proposed.The energy consumption of nodes in the network and the number of routing hops are multi-scale mathematically described and then mapped to a heuristic ant colony algorithm to achieve adaptive optimal routing and network resource balance.Subsequently,the connectivity factor is extracted from the connectivity probability prediction model and combined with the energy efficiency optimized ant colony optimization algorithm to achieve the overall planning of inter-vehicle connectivity prediction and route optimization.On this basis,a pheromone updating method based on ant colony characteristics is proposed to speed up the algorithm convergence and improve its global convergence.Finally,results of co-simulation with VanetMobiSim and OPNET show that the proposed algorithm has the advantages of reducing network resource consumption and improving data transmission success rate,compared with the two traditional routing algorithms.