MABASR—A Robust Wireless Interface Selection Policy for Heterogeneous Vehicular Networks

Abstract

Connectivity is rapidly becoming a core feature of modern vehicles to enable the provision of intelligent services that promote safer transport networks, real-time traffic infotainment systems and remote asset monitoring. As such, a reliable communications back-bone is required to connect vehicles that deliver real-time data to smart services deployed at cloud or edge architecture tiers. Hence, reliable uplink connectivity becomes a necessity. Next-generation vehicles will be equipped with multiple wireless interfaces, and require robust mechanisms for reliable and efficient management of such communication interfaces. In this context, the contribution of this article is a learning based approach for interface selection known as the Multi-Armed Bandit Adaptive Similarity-based Regressor (MABASR). MABASR takes advantage of the underlying linear relationship between channel quality parameters and uplink data rate to realise a robust interface selection policy. It is shown how this approach outperforms algorithms developed in prior work, achieving up to two orders of magnitude lower standard deviation of the obtained reward when trained on different data sets. Thus, higher reliability and less dependency on the structure of the training data are achieved. The approach is tested in mobile, static, and artificial static scenarios where severe network congestion is simulated. All data sets used for the evaluation are made publicly available.

Keywords

• Wireless interface selection
• vehicle-to-infrastructure (V2I)
• reinforcement learning (RL)