PoS - Proceedings of Science
Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session II - Wireless communication
Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks
L. Zhao* and F. Zheng
Full text: pdf
Pre-published on: July 17, 2017
Published on: September 06, 2017
Due to external interference or fault, the collected sensor data
is often missed or abnormal. It’s significant to reconstruct the missing data, especially the large-scale missing data. In this paper, a missing sensor data reconstruction method based on
the adaptively updated dictionary is presented. The K-SVD algorithm is used to train the historical data frames which are collected at different time to generate the original dictionary atoms. Moreover, in order to meet the real-time, continuous characteristics of sensor data, an adaptive dictionary update algorithm is studied which . It calculates the correlation between the current reconstructed data frame and the largest weight frame in the training dictionary to update the dictionary. The experimental results are fully analyzed by the open data
set. The results show that the proposed method has higher
reconstructed precision especially the interval of data frames which is more than 60 minutes compared with other commonly used methods.
DOI: https://doi.org/10.22323/1.299.0040
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.