Research On FANET Group Mobility Model Based On Swarm Intelligence

Flying Ad Hoc Network(FANET)is a special mobile Ad Hoc Network composed of several unmanned aerial vehicles,which is widely used in the occasion of temporary communication demand.Due to the high cost and complex technology of FANET networking,most of the current research in this field is based on simulation.As the key component of simulation experiment,the mobile model affects the effectiveness and reliability of routing protocol design and other algorithm performance evaluation.However,the proposed movement model does not consider the movement characteristics of Fan ET nodes and the motion correlation between nodes,nor does it consider the node obstacle avoidance in the environment of obstacles.This paper studies FANET group mobility model based on swarm intelligence,and the main research contents are as follows:(1)A Fanet group movement model(IBGM)based on improved BOIDS is proposed.Based on SAC rule,the following principle and aggregation principle were introduced to optimize the standard Boids biologic swarm movement strategy.The nodes only need to follow the fixed five movement rules(scatter,align,converge,follow,and converge)to complete the scatter,aggregation and transition,and effectively avoid the nodes being scattered into several small groups.At the same time,a new fitness function is proposed and applied to the genetic algorithm to solve the model parameters.By establishing the cost function for each motion rule,the optimization efficiency of the model parameters is improved.Experimental results show that the optimal parameters make the model exhibit good characteristics of swarm motion.Finally,through the simulation comparison with RW model,RPGM model and standard BOIDS cluster motion model,it can be seen that IBGM model has better performance in both time dependence and space dependence,as well as link connectivity.(2)Aiming at obstacle environment,IBGM model is further optimized and node path planning strategy based on improved genetic algorithm is proposed.Firstly,the initial path generation strategy is optimized,and the RRT algorithm is introduced and applied to raster map to improve the success rate of solving the initial path.Secondly,more randomness is added to the mutation operator to ensure the diversity of the population and enhance the global optimization ability of the algorithm.Thirdly,the interpolation operator is optimized to make the connection between nodes more successful and the connection path shorter.Finally,an optimization operator is added to the traditional genetic operation to eliminate redundant paths caused by repeated nodes and unnecessary turns.Compared with the standard genetic algorithm,the path planning strategy proposed in this paper has shorter execution time and better path than the path planning strategy based on standard genetic algorithm.