Enabling in-network aggregation by diffusion units for urban scale M2M networks

Machine-to-Machine (M2M) applications, which currently are growing rapidly, will be an important source of traffic on 5G cellular networks. A typical working model of M2M networks is a machine which is equipped with sensors or meters and directly delivers sensed data (e.g. temperature readings or inventory levels) to other machines that take actions based on the data. In such a working model, machines producing readings are referred to as sources, while those consuming the readings are called sinks. In this study, we consider a multiple-source multiple-sink scenario in M2M networks, in which a source may be demanded by multiple sinks and a sink may consume readings generated by several source nodes. However, how to efficiently process and reduce communication traffic in an urban-scale M2M network has been an engineering challenge. To solve the problem, in this paper, we propose a novel in-network aggregation scheme, called Diffusion Unit (DU), which encodes readings and aggregates data collected from its neighbors to effectively reduce transmitted data in a M2M network. We will show how existing distinct counting algorithms, like Linear Counting and Flajolet-Martin Counting, are adapted to implement a DU and introduce a family of techniques to optimize a DU's space efficiency. Furthermore, a comprehensive performance evaluation on the proposed techniques and some existing state-of-the-art techniques is presented. The results demonstrate that our techniques significantly outperform existing ones.