基于蝙蝠算法的无线传感器网络定位研究

Research on Wireless Sensor Network Location Based on Bat Algorithm

针对无线传感器网络节点定位精度不足问题,提出了一种改进的蝙蝠算法(Bat Algorithm)与DV-Hop(Distance Vector-Hop)定位算法相结合的SLBADV-Hop(Self Learning Bat Algorithm Distance Vector-Hop)算法。首先,用蝙蝠算法取代DV-Hop算法第三步的最小二乘法计算未知节点坐标,提高算法的定位精度;其次,改变蝙蝠算法频率计算公式中的参数β,提高种群多样性,避免早熟;最后,引入自学习思想,使蝙蝠个体的飞行速度随着蝙蝠位置的变化而变化,进一步提高定位精度。仿真结果显示,改进后的算法与应用于DV-Hop算法的蝙蝠算法相比,在参考节点比例、网络节点总数和定位区域面积等方面的定位精度均有提高,而能量消耗基本保持不变。仿真结果表明,SLBA-DV-Hop算法能够有效提高定位精度。

Aiming at the problem of insufficient positioning accuracy of wireless sensor network nodes,an improved Bat Algorithm and DV-Hop(Distance Vector-Hop)localization algorithm SLBADV-Hop(Self Learning Bat Algorithm Distance Vector-Hop)is proposed.algorithm.Firstly,the bat algorithm is used to replace the DV-Hop algorithm in the third step of the least squares method to calculate the unknown node coordinates,and improve the positioning accuracy of the algorithm.Secondly,change the parameter β in the bat algorithm frequency calculation formula to improve the diversity of the population and avoid premature;Introduce self-learning ideas,so that the flying speed of bat individuals changes with the position of the bat,further improving the positioning accuracy.The simulation results show that compared with the bat algorithm applied to DV-Hop,the improved algorithm can improve the positioning accuracy in terms of the proportion of reference nodes,the total number of network nodes and the area of the location area,while the energy consumption remains basically unchanged.The simulation results show that the SLBA-DV-Hop algorithm can effectively improve the positioning accuracy.