Efficient and Reliable Car-to-Cloud Data Transfer Empowered by BBR-enabled Network Coding

In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.

Abstract:

Nowadays, vehicles are not only merely used as a transportation medium, but also as a highly mobile Internet of Things (IoT) node, collecting data from all types of sources. The delivery of aggregated vehicle sensor data into the cloud for further analysis is very fragile, as vehicles move fast in their environment and channel conditions vary heavily. Next to mastering possible packet loss, communication protocols need to quickly adapt to the frequent data rate changes. Random Linear Network Coding (RLNC) has been proven as an efficient and robust mechanism for reliable data transfer especially on lossy channels. In this paper, the authors extend the Scalable Network Coding (ScalaNC) framework with the novel congestion control Bottleneck, Bandwidth and Round-Trip Time (BBR) in order to quickly adapt to the frequent changes in data rate and allow effective transfer of high amounts of data from vehicles and into the cloud. ScalaNC is validated in a Hardware-in-the-Loop field test consisting of a Long Term Evolution (LTE) base station and channel emulator.