Classification of Heterogenous M2M/IoT Traffic Based on C-plane and U-plane Data

This paper is motivated by the observation that M2M/IoT traffic is rather heterogeneous. By using traffic data collected in a real scenario, we prove that some M2M devices might generate a signaling traffic more similar to the traffic of a smartphone than to the traffic of a traditional M2M device used for metering applications. This makes difficult the task of a mobile operator to understand the impact of the introduction of M2M/IoT devices into the network. The paper presents a classification of the M2M/IoT heterogeneous world in three classes. The classification has been performed using the C-plane attributes and its effectiveness has been assessed by performing a clustering analysis over the U-plane data, which represent the ground truth. We found a good matching between the results of the classification task carried out by using the C-plane data and the results of the clustering task carried out by exploiting the U-plane data. This means that, by using the C-plane data (simpler with respect to using U-plane data) the network operator can understand with a good accuracy which type of M2M devices are active on the network, and what are the applications that they run and the data traffic that they generate. Therefore, the results may be useful for a proper dimensioning and management of the evolution of an EPC network.