Nonparametric distributed detection using bootstrapping and fisher's method

Topi Halme, Visa Koivunen, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper addresses the problem of distributed decision making when there is no or very vague knowledge about the probability models associated with the hypotheses. Such scenarios occur for example in the Internet of Things (IoT), data analytics, radio spectrum monitoring, sensor networks, environmental surveillance. It may not be feasible to specify accurate probability models needed in inference for a large number of distributed sensors. The probability models are learned from the data via empirical distributions that provide an accurate approximation of the true model. The bootstrap method is employed to approximate the distributions with high accuracy. The Anderson-Darling test is employed in each sensor and the computed p-values are communicated to the Fusion Center (FC) that makes the final decision. The FC employs the Fisher's method to fuse the local p-values. The decision is based on the distribution of p-values instead of actual p-values. The proposed method detects changes in probability model even if the distributions differ only slightly. Numerical simulations demonstrate that the Fisher's method evaluating the distribution of obtained local p-values consistently outperforms widely used Boolean fusion rules.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Publication series

Name2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Halme, T., Koivunen, V., & Poor, H. V. (2018). Nonparametric distributed detection using bootstrapping and fisher's method. In 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018 (pp. 1-6). (2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2018.8362316