Paper Info
Title
Multi-attribute compressive data gathering
Abstract
The data gathering is a fundamental operation in wireless sensor networks. Among approaches of the data gathering, the compressive data gathering (CDG) is an effective solution, which exploits the spatiotemporal correlation of raw sensory data. However, in the multi-attribute scenario, the performance of CDG decreases in every attribute's capacity because more measurements are on demand. In this paper, under the general framework of CDG, we propose a multi-attribute compressive data gathering protocol, taking into account the observed interattribute correlation in the multi-attribute scenario. Firstly, we find that 1) the rapid growth of the demand on measurements may decline the network capacity, 2) according to the compressive sensing theory, correlations among attributes can be utilized to reduce the demand on measurements without the loss of accuracy, and 3) such correlations can be found on real data sets. Secondly, motivated by these observations, we propose our approach to decline measurements. Finally, the real-trace simulation shows that our approach outperforms the original CDG under multiattribute scenario. Compared to the CDG, our approach can save 16% demand on measurements.
Year
DOI
Venue
2014
10.1109/WCNC.2014.6952647
WCNC
Keywords
Field
DocType
network capacity,protocols,compressive sensing,data compression,multiattribute compressive data gathering protocol,compressed sensing,wireless sensor networks,humidity,temperature measurement,ocean temperature,accuracy,entropy,sensors
Data collection,Data set,On demand,Computer science,Real-time computing,Exploit,Correlation,Wireless sensor network,Compressed sensing
Conference
ISSN
Citations 
PageRank 
1525-3511
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Guangshuo Chen1223.55
Xiaoyang Liu227034.49
Linghe Kong377072.44
Jia-Liang Lu412116.62
Min-you Wu51600140.81