Bayesian Multiple Hypothesis Testing for Distributed Detection in Sensor Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


In this paper we present a method for multiple hypothesis testing in sensor networks. Standard multiple testing methods ignore spatial dependencies between sensors, and thus suffer from a drastic loss of detection sensitivity. We introduce a Bayesian approach for taking the underlying spatial structure into account. By assigning a Gaus-sian process prior to a latent variable over the field, we locate areas where most of the received test statistics are caused by true signals. In these regions we relax the significance threshold in a manner which improves detection sensitivity while controlling overall false discovery rate at a tolerated level. The proposed method requires only minimal assumptions, but allows the user to incorporate their possible prior knowledge in to the model to refine inference. The benefits of the proposed method are demonstrated in simulation by comparing to standard multiple testing methods.

Original languageEnglish
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Number of pages5
ISBN (Electronic)9781728107080
Publication statusPublished - 1 Jun 2019
MoE publication typeA4 Conference publication
EventIEEE Data Science Workshop - Minneapolis, United States
Duration: 2 Jun 20195 Jun 2019


WorkshopIEEE Data Science Workshop
Abbreviated titleDSW
Country/TerritoryUnited States


  • Gaussian Processes
  • IoT
  • Multiple Hypothesis Testing
  • Sensor Networks
  • Spatial Inference


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