The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.
Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction
IEEE Transactions on Intelligent Transportation Systems ; 21 , 8 ; 3497-3512
2020-08-01
6370025 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Vehicle parking spot availability prediction based on vehicle-to-anything enabled machine learning
Europäisches Patentamt | 2021
|VEHICLE PARKING SPOT AVAILABILITY PREDICTION BASED ON VEHICLE-TO-ANYTHING ENABLED MACHINE LEARNING
Europäisches Patentamt | 2020
|Machine-learning-enabled plasma modeling and prediction
AIAA | 2024
|