大尺度三维稀疏分布网络信息覆盖优化仿真

Simulation of Network Information Coverage Optimization for Large Scale 3D Sparse Distribution Scenes

为了解决当前方法受传感器节点数目、节点感知半径和节点单次移动距离影响较大,平均网络信息覆盖率时较低的问题,提出了基于大尺度三维稀疏分布网络信息覆盖优化方法。方法在建立大尺度三维稀疏分布网络传感器节点感知模型和部署模型基础上,利用鱼群觅食、聚群和追尾行为构造鱼群个体的底层行为。通过觅食鱼群的个体局部寻优,达到整个种群协作寻优的目的;针对人工鱼群算法存在收敛速度不快、容易陷入局部最优的缺点,引入混沌搜索和反馈机制实现了大尺度三维稀疏分布网络信息覆盖优化。仿真测试结果证明,当实验测试环境中的传感器节点数量为250个、节点感知半径为7m、节点单次移动距离为4m时平均网络信息覆盖率最优。

At present,the method is greatly affected by the number of sensor nodes,the perception radius of node and the single moving distance of node.The average coverage rate of network information is low.Therefore,a method to optimize information coverage based on large scale 3D sparse distribution network was proposed.Based on the establishment of sensor node perception model and deployment model of large scale 3D sparse distribution network,this method used the foraging behavior,clustering behavior and following behavior to construct the individual bottom behavior of fish swarm.Then,the cooperative optimization of the whole population was achieved by the individual local optimization of foraging fish swarm.Because the convergence speed of artificial fish swarm algorithm was low and it was easy to fall into local optimum,the chaotic search and feedback mechanism were introduced to achieve the information coverage optimization of large scale 3D sparse distribution network.Simulation results show that the average network information coverage is the best when the number of sensor nodes in the simulation environment is 250,the sensing radius of the nodes is 7m,and the single moving distance of node is 4m.