数据收集传感器网络移动节点自定位系统研究

Research on mobile node self-localization system in data collection sensor network

传统数据收集传感器网络移动节点自定位系统节点数量相对较多,定位信息繁杂,易出现系统定位精度低、耗时久等问题。因此结合遗传算法对网络移动节点自定位系统进行设计,对系统未知节点的移动距离及采集区域节点分布密度的权值进行计算。根据遗传算法原理和采集区域节点分布密度的权值对可用节点进行计算,以便删减冗余节点,并对系统硬件内部的传感单元、数据处理单元等进行优化设计,达到通过少量可用节点完成系统定位流程的设计目标。最后通过仿真实验证实,设计的数据收集传感器网络移动节点自定位系统可以有效减少移动节点定位系统的运行时间,系统的运行效率得到了大幅度提升,对比传统的自定位系统精度提高了60%以上。

The traditional mobile node self-localization system of data collection sensor network is relatively influenced by the environment,which easily leads to the problems of low positioning accuracy and time-consuming of the system,and it is difficult to meet the current requirements of people on the use of data collection and positioning system. Therefore,the network mobile node self-localization system is optimized with genetic algorithm. firstly,the sensor network mobile nodes are sampled and analyzed,and the moving distance of unknown nodes in the system and the weights of node distribution density in the acquisition area are calculated with genetic algorithm. According to the node distribution density,the unknown node distribution position information in the system is fixed,the propagation speed of the data collection mobile node self-localization system is detected,the redundant nodes in the system are deleted by comparing the detection results,and the problems of data deviation and time consumption caused by complex node positioning accuracy information in the traditional method are solved by reducing the number of unknown nodes. Finally,the simulation results show that the genetic positioning algorithm can effectively reduce the running time of the mobile node positioning system,and the operating efficiency of the system has been greatly improved,compared with traditional methods,the positioning accuracy has been improved by more than 60 %location coverage and so on,and is suitable for large-scale mobile wireless sensor networks.