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

Vukobratovic D., JAKOVETIC D., SKACHEK V., BAJOVIC D., Sejdinovic D., KURT G. K., ...More

IEEE ACCESS, vol.4, pp.3360-3378, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 4
  • Publication Date: 2016
  • Doi Number: 10.1109/access.2016.2585468
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3360-3378
  • Istanbul Technical University Affiliated: No


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.