期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2011
卷号：2011
DOI：10.1155/2011/928958
出版社：Hindawi Publishing Corporation
摘要：The constrained power source given by batteries
has become one of the biggest hurdles for wireless sensor
networks to prevail. A common technique to reduce energy
consumption is to put sensors to sleep between duties. It leads
to a tradeoff between making a fewer number of observations
for saving energy while obtaining sufficient and more valuable
sensing information. In this paper, we employ two model-based
approaches for tackling the sensor scheduling problem. The
first approach is to apply our corrected VoIDP algorithm on
a chain graphical model for selecting a subset of observations
that minimizes the overall uncertainty. The second approach is
to find a selection of observations based on Gaussian process
model that maximizes the entropy and the mutual information
criteria, respectively. We compare their performances in terms
of predictive accuracies for the unobserved time points based
on their selections of observations. Experimental results show
that the Gaussian process model-based method achieves higher
predictive accuracy if sensor data are accurate. However, when
observations have errors, its performance degrades quickly. In
contrast, the graphical model-based approach is more robust and
error tolerant.
