Journal article Open Access

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui, Nicola; Cesana, Matteo; Hosseini, S. Amir; Liao, Qi; Malanchini, Ilaria; Widmer, Joerg


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  <identifier identifierType="DOI">10.5281/zenodo.556828</identifier>
  <creators>
    <creator>
      <creatorName>Bui, Nicola</creatorName>
      <givenName>Nicola</givenName>
      <familyName>Bui</familyName>
      <affiliation>IMDEA Networks Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Cesana, Matteo</creatorName>
      <givenName>Matteo</givenName>
      <familyName>Cesana</familyName>
      <affiliation>Politecnico di Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Hosseini, S. Amir</creatorName>
      <givenName>S. Amir</givenName>
      <familyName>Hosseini</familyName>
      <affiliation>NYU Tandon School of Engineering</affiliation>
    </creator>
    <creator>
      <creatorName>Liao, Qi</creatorName>
      <givenName>Qi</givenName>
      <familyName>Liao</familyName>
      <affiliation>Nokia Bell Labs</affiliation>
    </creator>
    <creator>
      <creatorName>Malanchini, Ilaria</creatorName>
      <givenName>Ilaria</givenName>
      <familyName>Malanchini</familyName>
      <affiliation>Nokia Bell Labs</affiliation>
    </creator>
    <creator>
      <creatorName>Widmer, Joerg</creatorName>
      <givenName>Joerg</givenName>
      <familyName>Widmer</familyName>
      <affiliation>IMDEA Networks Institute</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-04-24</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/556828</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_monroe</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today’s digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.&lt;/p&gt;</description>
  </descriptions>
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