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An IoT control plane model and its impact analysis on a virtualized MME for connected cars

IoT drives the future of Connected Cars including smart cars and it will transform the way we interact with our vehicles. With the emergence of millions of connected cars in the horizon, the wireless infrastructure needed to support this capability has to scale efficiently. To better understand the impact on the resource utilization of the wireless core infrastructure, we provide a detailed statistical model of the control plane/signaling interactions in connected cars. Specifically, our model is based on a 40K sample data set spanning more than 2100 IoT vehicles collected over 20 hours from a national telecommunications provider. The control plane model quantifies the additional load that the infrastructure (e.g., MME) needs to handle compared to an average busy hour LTE traffic model. We identify the heavy hitters of the control plane events and run real experiments based on our models in a testbed to characterize the resource utilization for supporting total event loadings using a real world high performance virtualized MME. No personally identifiable information (PII) was gathered or used in conducting this study. To the extent any data was analyzed, it was anonymous and/or aggregated data.