@inproceedings{573c8c6a178e47d490c7fe40bf8574cd,
title = "Spatial Inference in Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering",
abstract = "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.",
keywords = "IoT, p-values, Distributed Inference, Statistical Signal Processing, Large-Scale Sensor Networks, BIC",
author = "Martin G{\"o}lz and Michael Muma and Topi Halme and Abdelhak Zoubir and Visa Koivunen",
year = "2019",
doi = "10.23919/EUSIPCO.2019.8902986",
language = "English",
series = "European Signal Processing Conference",
publisher = "IEEE",
pages = "1--5",
booktitle = "2019 27th European Signal Processing Conference (EUSIPCO)",
address = "United States",
note = "European Signal Processing Conference, EUSIPCO ; Conference date: 02-09-2019 Through 06-09-2019",
}