Sensing Interpolation Strategies for a Mobile Crowdsensing Platform

Michele Girolami, Stefano Chessa, Gaia Adami, Mauro Dragone, Luca Foschini

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

3 Citations (Scopus)

Abstract

Mobile Crowd Sensing (MCS) allows an efficient collection of heterogeneous data over large areas, leveraging on the cooperation of MCS subscribers that offer services on their smartphones to this purpose. However, the coverage that a MCS platform can provide for a given area depends on the availability of subscribers and on their mobility in that area. To guarantee a better coverage, a MCS platform may employ a combination of static and mobile sensors and interpolation strategies that may provide meaningful data for all the area under observation. We discuss how two mechanisms (mixing static and mobile sensors and interpolation) can be combined together by using the large-scale mobility datasets of ParticipAct and the Weather Underground dataset.

Original languageEnglish
Title of host publication2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)
PublisherIEEE
Pages102-108
Number of pages7
ISBN (Electronic)9781509063253
DOIs
Publication statusPublished - 12 Jun 2017
Event5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering 2017 - San Francisco, United States
Duration: 7 Apr 20179 Apr 2017

Conference

Conference5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering 2017
Abbreviated titleMobileCloud 2017
CountryUnited States
CitySan Francisco
Period7/04/179/04/17

Keywords

  • Human Mobility
  • Kriging
  • Mobile Crowdsensing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

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  • Cite this

    Girolami, M., Chessa, S., Adami, G., Dragone, M., & Foschini, L. (2017). Sensing Interpolation Strategies for a Mobile Crowdsensing Platform. In 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) (pp. 102-108). IEEE. https://doi.org/10.1109/MobileCloud.2017.8