吉林大学学报(信息科学版) ›› 2023, Vol. 41 ›› Issue (3): 539-544.

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油气物联网高效能耗算法研究

刘 苗1 , 霍卓苗2 , 孙振兴1   

  1. 1. 东北石油大学秦皇岛校区 电气信息工程系, 河北 秦皇岛 066004; 2. 东北石油大学 物理与电子工程学院, 黑龙江 大庆 163318
  • 收稿日期:2022-08-25 出版日期:2023-06-08 发布日期:2023-06-15
  • 作者简介:刘苗(1980— ), 女, 乌鲁木齐人, 东北石油大学教授, 博士生导师, 主要从事油气认知物联网研究, ( Tel) 86- 18603379341(E-mail)lm_jlu@ 163. com.
  • 基金资助:
    黑龙江省自然科学基金资助项目 (LH2022F004))

Research on Efficient Energy Consumption Algorithm for Oil and Gas IoT

LIU Miao 1 , HUO Zhuomiao 2 , SUN Zhenxing 1   

  1. 1. Department of Electrical Information Engineering, Northeast Petroleum University-Qinhuangdao, Qinhuangdao 066004, China; 2. School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing 163318, China
  • Received:2022-08-25 Online:2023-06-08 Published:2023-06-15

摘要: 针对油气物联网中无效能量消耗和网络寿命短等问题, 提出了一种新的数据筛选和融合算法。 该算法 通过分析数据与正常数据的偏离程度自适应地判断数据的异常程度, 对数据进行簇内筛选和簇间融合, 避免了 网络信息的冗余以及能量的过度消耗。 实验结果表明, 与传统方案相比, 该方案能有效改善油气物联网的通信 质量和能耗效率。

关键词: 油气物联网,  , 自适应算法,  , 能耗算法,  , 效率优化

Abstract: A new data filtering and fusion algorithm are proposed for the problems of ineffective energy consumption and short network lifetime in oil and gas IoT( Internet of Things). This algorithm can adaptively judge the degree of data abnormality, filter and fusion data, avoiding redundant network information and excessive energy consumption. The algorithm adaptively determines the abnormality of the data by judging the deviation degree between the monitoring data and the normal data, performs intra-cluster filtering and inter-cluster fusion on the data. Compared with the traditional scheme, the proposed scheme can effectively improve the communication quality and energy consumption efficiency of oil and gas IoT.

Key words: oil and gas internet of things ( IoT ),  , adaptive algorithm,  , energy consumption algorithm,  , efficiency optimization

中图分类号: 

  • TP393