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Intelligent data-intensive IoT: A survey
Stockholm University, Sweden.
Stockholm University, Sweden.
Beijing University of Posts and Telecommunications, China.
RISE., Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID-id: 0000-0001-8952-3542
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2016 (Engelska)Ingår i: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, s. 2362-2368, artikel-id 7925122Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The IoT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or IoT, the data-intensive feature of IoT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence, intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle IoT data and different intelligence enablers for IoT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive IoT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive IoT to tackle the challenges.

Ort, förlag, år, upplaga, sidor
2016. s. 2362-2368, artikel-id 7925122
Nyckelord [en]
Context, Data provision, Data-intensive, Intelligence enabler, Internet of things, Big data, Surveys, Data intensive, Intelligent data
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-30927DOI: 10.1109/CompComm.2016.7925122Scopus ID: 2-s2.0-85020198230ISBN: 978-1-4673-9026-2 (digital)OAI: oai:DiVA.org:ri-30927DiVA, id: diva2:1139214
Konferens
2nd IEEE International Conference on Computer and Communications (ICCC 2016), October 14-17, 2016, Chengdu, China
Tillgänglig från: 2017-09-07 Skapad: 2017-09-07 Senast uppdaterad: 2020-12-01Bibliografiskt granskad

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Gillblad, Daniel

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