Scalable social sensing of interdependent phenomena

Shiguang Wang, Lu Su, Shen Li, Shaohan Hu, Tanvir Amin, Hongwei Wang, Shuochao Yao, Lance Kaplan, Tarek Abdelzaher

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

Abstract

The proliferation of mobile sensing and communication devices in the possession of the average individual generated much recent interest in social sensing applications. Significant advances were made on the problem of uncovering ground truth from observations made by participants of unknown reliability. The problem, also called fact-finding commonly arises in applications where unvetted individuals may opt in to report phenomena of interest. For example, reliability of individuals might be unknown when they can join a participatory sensing campaign simply by downloading a smartphone app. This paper extends past social sensing literature by offering a scalable approach for exploiting dependencies between observed variables to increase fact-finding accuracy. Prior work assumed that reported facts are independent, or incurred exponential complexity when dependencies were present. In contrast, this paper presents the first scalable approach for accommodating dependency graphs between observed states. The approach is tested using real-life data collected in the aftermath of hurricane Sandy on availability of gas, food, and medical supplies, as well as extensive simulations. Evaluation shows that combining expected correlation graphs (of outages) with reported observations of unknown reliability, results in a much more reliable reconstruction of ground truth from the noisy social sensing data. We also show that correlation graphs can help test hypotheses regarding underlying causes, when different hypotheses are associated with different correlation patterns. For example, an observed outage profile can be attributed to a supplier outage or to excessive local demand. The two differ in expected correlations in observed outages, enabling joint identification of both the actual outages and their underlying causes.

Original languageEnglish (US)
Title of host publicationIPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages202-213
Number of pages12
ISBN (Electronic)9781450334754
DOIs
StatePublished - Apr 13 2015
Event14th International Symposium on Information Processing in Sensor Networks, IPSN 2015 - Seattle, United States
Duration: Apr 13 2015Apr 16 2015

Publication series

NameIPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)

Other

Other14th International Symposium on Information Processing in Sensor Networks, IPSN 2015
CountryUnited States
CitySeattle
Period4/13/154/16/15

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Keywords

  • Data reliability
  • Expectation maximization
  • Maximum likelihood estimators
  • Social sensing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Cite this

Wang, S., Su, L., Li, S., Hu, S., Amin, T., Wang, H., Yao, S., Kaplan, L., & Abdelzaher, T. (2015). Scalable social sensing of interdependent phenomena. In IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week) (pp. 202-213). (IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/2737095.2737114