  Novel backscatter communication techniques enable battery-free sensor tags to
interoperate with unmodified standard IoT devices, extending a sensor network's
capabilities in a scalable manner. Without requiring additional dedicated
infrastructure, the battery-free tags harvest energy from the environment,
while the IoT devices provide them with the unmodulated carrier they need to
communicate. A schedule coordinates the provision of carriers for the
communications of battery-free devices with IoT nodes. Optimal carrier
scheduling is an NP-hard problem that limits the scalability of network
deployments. Thus, existing solutions waste energy and other valuable resources
by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning
scheduler that leverages graph neural networks to efficiently provide
near-optimal carrier scheduling. We train our scheduler with relatively small
optimal schedules obtained from a constraint optimization solver, achieving a
performance within 3% of the optimal scheduler. Without the need to retrain,
DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x
larger in the number of tags than those used for training, breaking the
scalability limitations of the optimal scheduler and reducing carrier
utilization by up to 50% compared to the state-of-the-art heuristic. Our
scheduler efficiently reduces energy and spectrum utilization in backscatter
networks.
