Measurement-Based Signaling Management Strategies for Cellular IoT

In the future, all devices that benefit from an Internet connection will be connected. Internet of Things technologies are key enablers of this vision by moving beyond basic connectivity machine-to-machine (M2M) communications brings to more intelligent interconnection of physical things on a massive scale. This anticipated growth is expected to challenge the planning and operation of cellular networks due to new diverse traffic models and high signaling loads. In this paper, we conduct a detailed experimental study using state-of-the-art drive testing equipment in order to measure, quantify, and analyze the signaling overhead of two classes of M2M services that resemble smart metering and vehicular applications. Two practical signaling reduction techniques are proposed and analyzed, with focus on aggregation as an efficient approach to overcome the resulting surge in signaling load. We complement the experimental results with an analytical evaluation to quantify the tradeoffs between M2M data transmission delay and the level of aggregation. Moreover, we present a novel case study to assess the potential negative impact of M2M signaling traffic on network planning and operation in 4G cellular networks