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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Vukobratovic, Dejan Jakovetic, Dusan Skachek, Vitaly Bajovic, Dragana Sejdinovic, Dino Kurt, Gunes Karabulut Hollanti, Camilla Fischer, Ingo 2017-05-31T05:57:04Z 2017-05-31T05:57:04Z 2016
dc.identifier.citation Vukobratovic , D , Jakovetic , D , Skachek , V , Bajovic , D , Sejdinovic , D , Kurt , G K , Hollanti , C & Fischer , I 2016 , ' CONDENSE : A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT ' IEEE ACCESS , vol 4 , pp. 3360-3378 . DOI: 10.1109/ACCESS.2016.2585468 en
dc.identifier.issn 2169-3536
dc.identifier.other PURE UUID: 10e02be8-efb5-41b4-98f4-de7339e0a4c7
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE FILEURL:
dc.description.abstract In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the knowledge buried in the data can significantly improve the quality of life and foster economic growth. However, a critical bottleneck for realizing the efficient IoT is the pressure it puts on the existing communication infrastructures, requiring transfer of enormous data volumes. Aiming at addressing this problem, we propose a novel architecture dubbed Condense which integrates the IoT-communication infrastructure into the data analysis. This is achieved via the generic concept of network function computation. Instead of merely transferring data from the IoT sources to the cloud, the communication infrastructure should actively participate in the data analysis by carefully designed en-route processing. We define the Condense architecture, its basic layers, and the interactions among its constituent modules. Furthermore, from the implementation side, we describe how Condense can be integrated into the Third Generation Partnership Project (3GPP) machine type communications (MTCs) architecture, as well as the prospects of making it a practically viable technology in a short time frame, relying on network function virtualization and software-defined networking. Finally, from the theoretical side, we survey the relevant literature on computing atomic functions in both analog and digital domains, as well as on function decomposition over networks, highlighting challenges, insights, and future directions for exploiting these techniques within practical 3GPP MTC architecture. en
dc.format.extent 19
dc.format.extent 3360-3378
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries IEEE ACCESS en
dc.relation.ispartofseries Volume 4 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title CONDENSE en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department University of Novi Sad
dc.contributor.department BioSense Institute
dc.contributor.department University of Tartu
dc.contributor.department University of Oxford
dc.contributor.department Istanbul Technical University
dc.contributor.department Department of Mathematics and Systems Analysis
dc.contributor.department Institute for Cross-Disciplinary Physics and Complex Systems
dc.subject.keyword Internet of things (IoT)
dc.subject.keyword big data
dc.subject.keyword network coding
dc.subject.keyword network function computation
dc.subject.keyword machine learning
dc.subject.keyword wireless communications
dc.subject.keyword FUNCTION COMPUTATION
dc.subject.keyword INFORMATION-FLOW
dc.subject.keyword SYSTEMS
dc.subject.keyword COMMUNICATION
dc.subject.keyword ALGORITHMS
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201705315101
dc.identifier.doi 10.1109/ACCESS.2016.2585468
dc.type.version acceptedVersion

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