Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction

Abstract

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%.

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Cited by in corpus (9)

• 6G White Paper on Machine Learning in Wireless Communication Networks
• The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
• FALCON: An accurate real-time monitor for client-based mobile network data analytics
• Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks
• Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems
• Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
• Towards Data-driven Simulation of End-to-end Network Performance Indicators
• Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data
• Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network