Title
Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.
Abstract
The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.
Year
DOI
Venue
2014
10.3390/s141019095
SENSORS
Keywords
Field
DocType
eco-hydrological wireless sensor network,spatial sampling,hybrid optimization criterion,unconditional stochastic simulation
Stochastic simulation,Spatial analysis,Variogram,Sampling design,Remote sensing,Mean squared error,Ground truth,Sampling (statistics),Engineering,Geostatistics
Journal
Volume
Issue
ISSN
14
10.0
1424-8220
Citations 
PageRank 
References 
15
1.95
6
Authors
6
Name
Order
Citations
PageRank
Jian Kang1738.32
Xin Li2458.71
Rui Jin39016.41
Yong Ge4218.31
Jin-Feng Wang518628.86
Jianghao Wang67310.85