Exploiting Map Topology Knowledge for Context-Predictive Multi-Interface Car-to-Cloud Communication

Abstract

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1807.01042

• http://arxiv.org/pdf/1807.01042

• http://xplorestaging.ieee.org/ielx7/8672788/8690547/08690753.pdf?arnumber=8690753,

http://dx.doi.org/10.1109/vtcfall.2018.8690753

• https://dblp.uni-trier.de/db/journals/corr/corr1807.html#abs-1807-01042,

https://ui.adsabs.harvard.edu/abs/2018arXiv180701042S/abstract,

https://arxiv.org/pdf/1807.01042,

https://arxiv.org/abs/1807.01042,

https://export.arxiv.org/pdf/1807.01042,

https://fr.arxiv.org/pdf/1807.01042,

https://export.arxiv.org/abs/1807.01042,

https://fr.arxiv.org/abs/1807.01042,

https://za.arxiv.org/abs/1807.01042,

https://academic.microsoft.com/#/detail/2810862882