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 upto-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 resourceefficient 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%.
In REF , connectivity maps are jointly used with mobility prediction in order to schedule the time of vehicular sensor data transmissions with respect to the expected network quality on the future route.
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
Computer Science