rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: When Compressive Sensing Meets Mobile Crowdsensing
  name:
    name part: Kong, Linghe.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Wang, Bowen.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Chen, Guihai.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 127 p. 39 illus., 35 illus. in color. online resource.
  abstract: This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution. .
  table of contents: Introduction -- Mathematical Theory of Compressive Sensing -- Basic Compressive Sensing for Data Reconstruction -- Bayesian Compressive Sensing for Task Allocation -- Adaptive Compressive Sensing for Incentive Mechanism -- Encoded Compressive Sensing for Privacy Preservation -- Iterative Compressive Sensing for Fault Detection -- Conclusion.
  note: by Linghe Kong, Bowen Wang, Guihai Chen.
  subject:
    topic: Mobile computing
  subject:
    topic: Computer communication systems
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Computers
  subject:
    topic: Mobile Computing
  subject:
    topic: Computer Communication Networks
  subject:
    topic: Data Structures and Information Theory
  subject:
    topic: Information Systems and Communication Service
  classification: QA76.59
  classification: 004.167
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811377761
  identifier: https://doi.org/10.1007/978-981-13-7776-1
  record info:
    record creation date: 190608
    record change date: 20191027151735.0
    record identifier: 978-981-13-7776-1