A unified diversity measure for distributed inference

Prashant Khanduri, Aditya Vempaty, Pramod Kumar Varshney

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

2 Scopus citations

Abstract

Present day distributed inference systems consist of sensors with different modalities working as a system to perform specific tasks. With multiple sensors sensing heterogeneous data over multiple time instants, diversity is an inherent aspect of such systems. In this work, we take the first step to characterize the diversity of a general heterogeneous sensing system performing inference tasks. We provide a unified definition for diversity which can be customized for the system in use. The use of the definition is illustrated by applying it to a specific detection system where the sensors collect data over heterogeneous sensing channels. We assume the data to be both temporally and spatially correlated and analyze the effect of dependence on the diversity of the detection system.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3934-3938
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • distributed inference
  • diversity
  • heterogeneous sensing
  • internet of things
  • spatio-temporal data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Khanduri, P., Vempaty, A., & Varshney, P. K. (2017). A unified diversity measure for distributed inference. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 3934-3938). [7952894] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952894