计算机工程与应用Issue(4)：91-95,5.DOI:10.3778/j.issn.1002-8331.1304-0302
一种基于自适应采样优化的WSN定位算法
Localization algorithm of WSN using adaptive sampling optimization scheme
摘要
Abstract
Due to limitations of Monte Carlo localization algorithm, such as low sampling efficiency and big sampling number, LAASO algorithm is proposed based on adaptive sampling optimization. Anchor box and prediction area are used to further optimize sampling area. Sampling number is adaptive defined by sampling area. Curve fitting in SOMCL algorithm is take to optimize the weight of samples. Simulation test results show, in the condition that speed change ratio is 25 m/s and the maximum speed is less than 60 m/s, location accuracy of nodes is respectively increased by 40%and 36%than that of MCL and SOMCL, while sampling number is decreased by 20%and 31.5%compared with that of MCL and SOMCL. LAASO is more suitable for high operation environment.关键词
无线传感器网络/蒙特卡罗/定位/采样优化/自适应Key words
wireless sensor networks/Monte Carlo/localization/samping optimization/adaptive分类
信息技术与安全科学引用本文复制引用
杨冰,邓曙光,李稳国..一种基于自适应采样优化的WSN定位算法[J].计算机工程与应用,2015,(4):91-95,5.基金项目
湖南省科技计划资助项目（No.2012FJ3025）；湖南省教育厅科研基金（No.12C0585）；益阳市科技计划基金（No.2011JZ48）；湖南城市学院科技计划项目（No.2010xj011）。 （）
