Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm

Shaobo Li, Chenglong Zhang, Jinglei Qu
{"title":"Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm","authors":"Shaobo Li, Chenglong Zhang, Jinglei Qu","doi":"10.3991/IJOE.V14I11.9544","DOIUrl":null,"url":null,"abstract":"The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.","PeriodicalId":387853,"journal":{"name":"Int. J. Online Eng.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Online Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3991/IJOE.V14I11.9544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于三维自适应果蝇优化算法的智能车间无线传感器网络位置优化
现代制造业的生产过程是复杂多变的,制造资源具有广泛的动态特征。为了有效地管理和控制制造资源,实现智能车间位置数据的实时采集,提出了一种基于无线传感器网络的制造资源位置感知体系结构。为保证智能车间制造资源位置数据的实时性,设计了一种三维自适应果蝇优化算法来估计位置坐标,该算法引入自适应惯性权重系数,保留了果蝇优化算法局部搜索能力强的优点,提高了全局优化能力。有效地解决了智能车间的三维定位问题。仿真结果表明,本文算法应用于三角测量的定位计算,定位误差较小,运行时间较短,提高了定位数据的准确性,满足了智能车间人员、物料、物流车辆等制造资源的实时定位要求,便于资源感知和调度管理,从而提高了管理水平和产品质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Infrared-based Short-Distance FSO Sensor Network System Real-Time Image Transmission Algorithm in WSN with Limited Bandwidth Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method Computer Assisted E-Laboratory using LabVIEW and Internet-of-Things Platform as Teaching Aids in the Industrial Instrumentation Course Towards Simulation Aided Online Teaching: Material Design for Applied Fluid Mechanics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1