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Information analysis of local suppression scheme based on a spatial-temporal model

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  • Dal Ho Kim
  • Jayoun Lee
  • Yongku Kim

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.

Suggested Citation

  • Dal Ho Kim & Jayoun Lee & Yongku Kim, 2018. "Information analysis of local suppression scheme based on a spatial-temporal model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(16), pages 2929-2942, December.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2929-2942
    DOI: 10.1080/02664763.2018.1445703
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