Information analysis of local suppression scheme based on a spatial-temporal model
Dal Ho Kim,
Jayoun Lee and
Yongku Kim
Journal of Applied Statistics, 2018, vol. 45, issue 16, 2929-2942
Abstract:
In a wireless sensor network, data collection is relatively cheap whereas data transmission is relatively expensive. Thus, preserving battery life is critical. If the process of interest is sufficiently predictable, the suppression in transmission can be adopted to improve efficiency of sensor networks because the loss of information is not great. The prime interest lies in finding an inference-efficient way to support suppressed data collection application. In this paper, we present a suppression scheme for a multiple nodes setting with spatio-temporal processes, especially when process knowledge is insufficient. We also explore the impact of suppression schemes on the inference of the regional processes under various suppression levels. Finally, we formalize the hierarchical Bayesian model for these schemes.
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2018.1445703 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:16:p:2929-2942
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20
DOI: 10.1080/02664763.2018.1445703
Access Statistics for this article
Journal of Applied Statistics is currently edited by Robert Aykroyd
More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().