基于DV-Hop修正算法的移动物联感知传感定位

Mobile object sensing location based on DV-Hop correction algorithm

摘要:

为解决经典距离向量跳段(DV-Hop)定位算法存在较大误差问题，提出一种基于多通信半径修正跳数计算未知节点位置的DV-Hop改进扩展算法。通过对无线传感器网络(WSN)中多通信半径与信邻/信标节点间跳数分级细化，精确移动物联感知传感定位跳数，修正网络拓扑结构不规则多级通信半径。研究结果表明：不同通信半径下，该算法定位误差较传统DV-Hop算法、基于改进的樽海鞘群算法的DV-Hop(ISSA_DV-Hop)算法、基于差分进化的DV-Hop(DE_DV-Hop)算法分别降低约36.78%、10.63%、21.15%；不同信标节点数下，该算法定位误差比上述3种算法定位误差平均减小约33.17%、15.36%、21.07%。由此说明，基于DV-Hop修正算法可提高移动物联感知传感定位精确度，在无需添加硬件情况下能够减少数据误差，并确保 WSN 中未知节点平均跳距更符合 DV-Hop 定位算法实际和网络传感要求。

Abstract:

In order to solve the problem of significant errors in the classic Distance Vector-Hop(DV-Hop) positioning algorithm, an improved DV-Hop extension algorithm based on multi-communication radius correction for calculating unknown node positions is proposed. By grading and refining the number of hops between multiple communication radii and neighboring/beacon nodes in Wireless Sensor Network(WSN), the precise number of hops for mobile Internet of Things(IoT) sensing positioning is determined, which corrects the irregular multi-level communication radii of the network topology. The research results show that under different communication radii, the positioning error of this algorithm is reduced by about 36.78%, 10.63% and 21.15% compared to that of traditional DV-Hop, Improved Salp Swarm Algorithm DV-Hop(ISSA_DV-Hop) and Differential Evolution DV-Hop(DEDV-Hop) algorithms, respectively; under different numbers of beacon nodes, the positioning error of this algorithm is reduced by an average of about 33.17%, 15.36%, and 21.07% compared to that of the three algorithms mentioned above. This indicates that the DV-Hop correction algorithm can improve the positioning accuracy of mobile IoT sensing, reduce data errors without adding hardware, and ensure that the average hop distance of unknown nodes in WSN is more in line with the actual DV-Hop positioning algorithm and network sensing requirements.