Paper Info
Title
Multiple attributes-based data recovery in wireless sensor networks.
Abstract
In wireless sensor networks (WSNs), since many basic scientific works heavily rely on the complete sensory data, data recovery is an indispensable operation against the data loss. Several works have studied the missing value problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a multiple attributes-based recovery algorithm which can provide high accuracy. Firstly, based on two real datasets, the Intel Indoor project and the GreenOrbs project, we reveal that such correlations are strong, e.g., the change of temperature and light illumination usually has strong correlation. Secondly, motivated by this observation, we develop a Multi-Attribute-assistant Compressive-Sensing-based (MACS) algorithm to optimize the recovery accuracy. Finally, real trace-driven simulation is performed. The results show that MACS outperforms the existing solutions. Typically, MACS can recover all data with less than 5% error when the loss rate is less than 60%. Even when losing 85% data, all missing data can be estimated by MACS with less than 10% error.
Year
DOI
Venue
2013
10.1109/GLOCOM.2013.6831055
IEEE Global Communications Conference
Keywords
Field
DocType
compressed sensing,matrix decomposition,wireless sensor networks,accuracy,estimation,correlation
Key distribution in wireless sensor networks,Data loss,Computer science,Matrix decomposition,Computer network,Real-time computing,Correlation,Missing data,Data recovery,Wireless sensor network,Compressed sensing
Conference
ISSN
Citations 
PageRank 
2334-0983
4
0.43
References 
Authors
10
7
Name
Order
Citations
PageRank
Guangshuo Chen1223.55
Xiaoyang Liu227034.49
Linghe Kong377072.44
Jia-Liang Lu412116.62
Yu Gu 0001533322.96
Wei Shu666961.88
Min-you Wu71600140.81