Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks

TL;DR

This paper proposes a machine learning approach that predicts communication context to optimize car-to-cloud data transfer in 5G networks, significantly improving data rates and reducing power consumption.

Contribution

It introduces a novel combination of mobility prediction and channel quality estimation for context-aware scheduling in car-to-cloud communication.

Findings

Increased average data rate by up to 194%.

Reduced uplink power consumption by up to 54%.

Validated in a real-world 5G network scenario.

Abstract

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resource-efficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.