Demo abstract: Extrapolation from participatory sensing data

H. Liu, S. Gu, C. Pan, W. Zheng, S. Li, S. Hu, S. Wang, D. Wang, T. Amin, L. Su, Z. Xie, R. Govindan, A. Barnoy, T. Abdelzaher

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

Abstract

In this demo, a learning system, called Metis, is presented that extrapolates missing pieces in participatory sensing data. The work addresses the challenge of incomplete coverage in participatory sensing applications, where lack of complete control over participant mobility and sensing patterns may create coverage gaps in space and in time. Metis learns the underlying spatiotemporal patterns of the measured phe- nomenon from available incomplete observations, and uses these patterns to infer missing data. We describe the overall system design and demonstrate the system using data collected during the New York City gas crisis in the aftermath of Hurricane Sandy.

Original languageEnglish (US)
Title of host publicationSenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery
ISBN (Print)9781450320276
DOIs
StatePublished - Jan 1 2013
Event11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013 - Rome, Italy
Duration: Nov 11 2013Nov 15 2013

Publication series

NameSenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Other

Other11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013
Country/TerritoryItaly
CityRome
Period11/11/1311/15/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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