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

TL;DR

This paper introduces BS-CB, a hybrid machine learning approach for vehicular sensor data transmission that significantly improves data rates and resource efficiency while managing buffering delays.

Contribution

It presents a novel client-based method combining supervised, unsupervised, and reinforcement learning for resource-efficient data transmission in vehicular networks.

Findings

Data rate improved by 125%-195%

Cell resource occupancy reduced by 84%-89%

Power consumption decreased by 53%-75%

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.