Dynamic Data Transmission Mechanism Based on Prediction Revision

杨军 1; 2; 张德运 1

您当前的位置：

首页 >

文章列表页 >

Dynamic Data Transmission Mechanism Based on Prediction Revision

更新时间：2025-12-08

- Dynamic Data Transmission Mechanism Based on Prediction Revision
- Vol. 42, Issue 6, Pages: 655-658(2008)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 宁夏大学数学与计算机科学学院,银川,750021
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Online First：09 June 2008，
  
  Published：2008
- 稿件说明：
移动端阅览
杨军 1, 2, 张德运 1. Dynamic Data Transmission Mechanism Based on Prediction Revision[J]. 2008, 42(6): 655-658.
DOI：

杨军 1, 2, 张德运 1. Dynamic Data Transmission Mechanism Based on Prediction Revision[J]. 2008, 42(6): 655-658. DOI：

摘要

利用无线传感器网络中节点感知数据的相关性

提出了一种基于预测修正的动态数据传送机制.核心思想是:将数据预测与模型计算分离

接收节点先对数据进行平稳化处理

然后建立模型或动态更新模型

再把模型参数发送给采样节点.采样节点用精简的预测修正算法预测数据

通过比较法确定需要发送的采样数据

从而减少了数据传送的次数

延长了网络的生命期.仿真结果表明

所提算法可以滤除真实采样观测序列中83%的冗余数据

预测精度较一般预测算法提高了22%

它适用于能量约束性较强的无线传感器网络.

Abstract

A dynamic data transmission mechanism based on prediction revision is presented according to the inherent correlation of sampling data between nodes in WSNs. The core of the dynamic data transmission mechanism is to separate the data prediction and the model computing. The sampling data in sink node are firstly stabilized

the model is built or updated dynamically

and then the parameters are sent to sensor nodes by sink. The predictions in sensor nodes are made using a simplified prediction revision algorithm

and then

the sampling data that needs to be sent out is determined based on a comparison between the prediction data and the sampling data. The times of data transmission can be reduced and the lifetime of WSNs can be prolonged. Simulation studies show that the proposed algorithm can decrease times on sending data by more than 83% and the prediction precision can be increased by about 22% compared with the traditional prediction algorithms in real sampling data series. The algorithm is suitable for WSNs which are strictly constrained by energy.

关键词

Keywords

references

MA Chi, MA Ming, YANG Yuanyuan. Data-centric energy efficient scheduling for densely deployed sensor networks [C]∥Proceedings of IEEE International Conference on Communications. Piscataway, NJ, USA: IEEE, 2004:3652-3656.

DESHPANDE A, GUESTRIN C, MADDEN S R, et al. Model-driven-data acquisition in sensor networks [J]. The VLDB Journal, 2004, 14(4):417-443.

LAZARIDIS I, MEHROTRA S. Capturing sensor-generated time series with quality guarantees [C]∥Proceedings of IEEE International Conference on Data Engineering. Piscataway, NJ, USA: IEEE,2003:429-440.

王燕. 应用时间序列分析 [M]. 北京:中国人民大学出版社, 2005:90-97.

BOX G, JENKINS G M, REINSEL G. Time series analysis forecasting and control [M]. 3rd ed. San Francisco, CA, USA: Prentice Hall, 1994:89-120.

PANDIT S M, WU Shienming. Time series and system analysis with applications [M]. New York, USA: John Wiley and Sons, 1983:118-199.

MCPHADEN M J. Tropical atmosphere ocean project [EB/OL].[2007-02-06]. http:∥www.pmel.noaa.gov/tao/index.shtml.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prolonging Lifecycle of Energy Balance Routing in Real-Time Wireless Sensor Networks

Secure Directed Diffusion Protocol Based on Random Key Predistribution Model

Directed Diffusion Algorithm Based on Dynamic Threshold

Proportional Fairness Scheduling Algorithm Used for Wireless Sensor Network

Survivable Model for Base Station in Wireless Sensor Network with Rejuvenation Technology

Related Author

赵保华 1

李培龙 1

高存皓 1

周颢 1

宋飞 1

谷雨 1

张德运 1

Related Institution

Anhui Province Key Laboratory of Software in Computing and Communication

Department of Computer Science and Technology, University of Science and Technology of China

School of Mathematics and Computer Science, Ningxia University

Computer Center, Henan University

Department of Computer Science and Technology, Xi'an Jiaotong University

⁰