Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data

Abstract

as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised, unsupervised, and reinforcement learning - in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.

References in corpus (8)

• A Contextual-Bandit Approach to Personalized News Article Recommendation
• 6G White Paper on Machine Learning in Wireless Communication Networks
• Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
• 5G and Beyond: Smart Devices as part of the Network Fabric
• Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators
• Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G
• 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