CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Dejan Vukobratovic
  • Dusan Jakovetic
  • Vitaly Skachek
  • Dragana Bajovic
  • Dino Sejdinovic
  • Gunes Karabulut Kurt
  • Camilla Hollanti

  • Ingo Fischer

Research units

  • University of Tartu
  • BioSense Institute
  • University of Oxford
  • University of Novi Sad
  • Istanbul Technical University
  • Institute for Cross-Disciplinary Physics and Complex Systems

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.

Details

Original languageEnglish
Pages (from-to)3360-3378
Number of pages19
JournalIEEE Access
Volume4
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

    Research areas

  • Internet of things (IoT), big data, network coding, network function computation, machine learning, wireless communications, SOFTWARE-DEFINED NETWORKING, WIRELESS SENSOR NETWORKS, MACHINE-TYPE COMMUNICATIONS, MULTIPLE-ACCESS CHANNELS, HARNESSING INTERFERENCE, FUNCTION COMPUTATION, INFORMATION-FLOW, SYSTEMS, COMMUNICATION, ALGORITHMS

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