Recursive truth estimation of time-varying sensing data from online open sources

Hang Cui, Tarek Abdelzaher, Lance Kaplan

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

Abstract

This paper is motivated by prospective Internet of Things (IoT) applications that exploit inputs from online open sources whose reliability may be uncertain. Unlike physical signal fusion (that can leverage solid analytic foundations derived from physical properties of fused signals), data reliability assessment from arbitrary online open sources is a harder problem. At least two difficulties arise. First, source reliability is harder to estimate from first principles due to lack of visibility into the sensing and subsequent processing stages for published data. Second, by virtue of being open, some sources can be copied by others, leading to correlated errors at a large scale. This paper presents a recursive truth estimator for online public data streams that addresses the above two problems. We focus on categorical data. Many truth-finding systems were developed to cope with unreliable categorical data. Most of them are designed for batch analysis of bulk datasets. This work extends previous efforts by developing an online recursive estimator. Unlike previous recursive fact-finders, ours is the first that can jointly handle (i) changes in the population of sources over time, (ii) changes in the ground-truth state of the physical phenomenon being observed (that result in the appearance of conflicting claims), and (iii) correlated errors due to potential copying among sources. Results show that our algorithm not only outperforms other recursive fact-finders in the case of changing ground-truth state, but also improves estimation accuracy of static state.

Original languageEnglish (US)
Title of host publicationProceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-34
Number of pages10
ISBN (Electronic)9781538654705
DOIs
StatePublished - Oct 25 2018
Event14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018 - Bronx, United States
Duration: Jun 18 2018Jun 19 2018

Publication series

NameProceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018

Other

Other14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
CountryUnited States
CityBronx
Period6/18/186/19/18

Keywords

  • Computer Science
  • Noise Measurement
  • Observers
  • Reliability
  • Sensors

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management

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