A Comparison of Axiomatic Distance-Based Collective Intelligence Methods for Wireless Sensor Network State Estimation in the Presence of Information Injection

J. Kyle Skolfield, Romena Yasmin, Adolfo R. Escobedo, Lauren M. Huie

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

Abstract

Wireless sensor networks are a cost-effective means of data collection, especially in areas which may not have significant infrastructure. There are significant challenges associated with the reliability of measurements, in particular due to their distributed nature. As such, it is important to develop methods that can extract reliable state estimation results in the presence of errors. This work proposes and compares methods based on collective intelligence ideas, namely consensus ranking and rating models, which are founded on axiomatic distances and intuitive social choice properties. The efficacy of these methods to assess a transmitted signal's strength with varying quantity and quality of incompleteness in the network's readings is tested.

Original languageEnglish (US)
Title of host publicationIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155036
DOIs
StatePublished - Jun 2020
Event6th IEEE World Forum on Internet of Things, WF-IoT 2020 - New Orleans, United States
Duration: Jun 2 2020Jun 16 2020

Publication series

NameIEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings

Conference

Conference6th IEEE World Forum on Internet of Things, WF-IoT 2020
Country/TerritoryUnited States
CityNew Orleans
Period6/2/206/16/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Computational Mechanics
  • Instrumentation

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