Spatial Inference in Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering

Martin Gölz, Michael Muma, Topi Halme, Abdelhak Zoubir, Visa Koivunen

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

5 Citations (Scopus)
105 Downloads (Pure)


The problem of statistical inference in large-scale sensor networks observing spatially varying fields is addressed. A method based on multiple hypothesis testing and Bayesian clustering is proposed. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. High detection power is achieved while keeping false positives at a tolerable level. A variant of the EM-algorithm is employed to associate sensors with clusters. The performance of the method is studied in simulation using different detection theoretic criteria.
Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-9-0827-9703-9
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Coruna, Spain
Duration: 2 Sept 20196 Sept 2019

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO


  • IoT
  • p-values
  • Distributed Inference
  • Statistical Signal Processing
  • Large-Scale Sensor Networks
  • BIC


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