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

TL;DR

The paper proposes Condense, a reconfigurable architecture integrating data analysis into IoT communication infrastructure to efficiently handle massive data volumes in 5G IoT networks.

Contribution

It introduces the Condense architecture that embeds data processing within network infrastructure using network function computation, enabling more efficient IoT data handling.

Findings

Condense can be integrated into 3GPP MTC architecture.

Utilizes NFV and SDN for practical deployment.

Surveys relevant literature on function computation and decomposition.

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 realising 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 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. Further, from the implementation side, we describe how Condense can be integrated into the 3rd Generation Partnership Project (3GPP) Machine Type Communications (MTC) architecture, as well as the prospects of making it a practically viable technology in a short time frame, relying on Network Function Virtualization (NFV) and Software Defined Networking (SDN). 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.