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

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Document pages: 6 pages

Abstract: as a novel client-based method for resource-efficient opportunistictransmission of delay-tolerant vehicular sensor data. BS-CB applies a hybridapproach which brings together all major machine learning disciplines -supervised, unsupervised, and reinforcement learning - in order to autonomouslyschedule vehicular sensor data transmissions with respect to the expectedresource efficiency. Within a comprehensive real world performance evaluationin the public cellular networks of three Mobile Network Operators (MNOs), it isfound that 1) The average uplink data rate is improved by 125 -195 2) Theapparently selfish goal of data rate optimization reduces the amount ofoccupied cell resources by 84 -89 3) The average transmission-related powerconsumption can be reduced by 53 -75 4) The price to pay is an additionalbuffering delay due to the opportunistic medium access strategy.

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