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工程科学与技术:2022,54(4):228-234
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基于全局人工鱼群算法优化的DV-Hop定位算法
(南华大学 资源环境与安全工程学院,湖南 衡阳 421001)
DV-Hop Localization Algorithm Optimized Based on Global Artificial Fish Swarm Algorithm
(School of Resource Environment and Safety Eng., Univ. of South China, Hengyan 421001, China)
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投稿时间:2021-03-24    修订日期:2022-03-29
中文摘要: 无线传感器网络具有大规模、自组织、可靠性、以数据为中心、集成化等特点,被广泛应用于军事、医疗、矿山监测、安全生产等领域。然而现有的无线传感器网络非测距定位算法存在定位误差较大的问题。针对该问题,本文提出一种基于全局人工鱼群算法优化的DV-Hop(distance vector-hop)定位算法,即DEWF-D定位算法。该算法对非测距定位算法中的DV-Hop算法出现误差的步骤进行优化处理,通过减小算法过程中出现的误差,最终得到较为精准的定位坐标。首先,使信标节点以两种不同的通信半径传递消息,将跳数进行精确化处理,以减少跳数带来的误差;然后,用最小均方误差准则和误差加权方式计算平均每跳距离;最后,利用全局人工鱼群算法替换三边测量法进行坐标计算。仿真验证表明,在不同信标节点密度下,本文提出的DEWF-D算法与DV-Hop算法及其他算法相比定位精度分别提升28.3%、6.9%、12.5%;而在不同通信半径下,定位精度分别提升了24.4%、7.6%、14.8%。证明DEWF-D算法能有效提升定位精度,解决了定位算法中出现的定位误差较大问题。
Abstract:Wireless sensor networks have the characteristics of large scale, self-organization, reliability, data-centricity, integration, etc., and are widely used in military, medical, mine monitoring, safety production and other fields. However, the existing non-ranging positioning algorithms for wireless sensor networks suffer from large positioning errors. Aiming at this problem, a DV-Hop (distance vector-hop) localization algorithm based on the global artificial fish swarm algorithm optimization was proposed, namely DEWF-D localization algorithm. In the algorithm, the error-prone steps of the DV-Hop algorithm were optimized in the non-ranging positioning algorithm, and finally more accurate positioning coordinates were obtained by reducing the errors in the algorithm process. First, messages were transmitted by the beacon nodes with two different communication radii, and the number of hops was precisely processed to reduce the error caused by the number of hops; then, the average distance per hop was calculated using the minimum mean square error criterion and the error weighting method; Finally, the global artificial fish swarm algorithm was used to replace the trilateration method for coordinate calculation. Simulation results showed that, compared with the DV-Hop algorithm and other algorithms, the positioning accuracy of the proposed algorithm is respectively improved by 28.3%, 6.9%, and 12.5% under different beacon node densities; and under different communication radii, the positioning accuracy is improved by 24.4%, 7.6%, 14.8%, respectively. It was demonstrated that the DEWF-D algorithm can effectively improve the positioning accuracy and solve the problem of large positioning errors in previous positioning algorithms.
文章编号:202100251     中图分类号:TP393    文献标志码:
基金项目:湖南省市联合自然科学基金项目(2021JJ50093);湖南省重点研发计划项目(2018SK2055) ;国家自然科学基金项目(11875164);湖南省研究生科研创新项目 (CX20200921)
作者简介:第一作者:余修武(1976-),男,教授,硕士生导师,博士.研究方向:无线传感器网络与智能安全监控、铀矿冶安全与核污染在线监测预警和电气安全工程.E-mail:yxw2008xy@163.com;通信作者:秦晓坤,E-mail:2499856398@qq.com
引用文本:
余修武,秦晓坤,刘永,余昊.基于全局人工鱼群算法优化的DV-Hop定位算法[J].工程科学与技术,2022,54(4):228-234.
YU Xiuwu,QIN Xiaokun,LIU Yong,YU Hao.DV-Hop Localization Algorithm Optimized Based on Global Artificial Fish Swarm Algorithm[J].Advanced Engineering Sciences,2022,54(4):228-234.