Robust Generalization of Graph Neural Networks for Scheduling Backscatter Communications at Scale

Novel backscatter communication techniques allow <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">battery-free sensor tags</i> to operate with standard IoT devices, thereby augmenting a network’s sensing capabilities. For communicating, sensor tags rely on an unmodulated carrier provided by neighboring IoT devices, with a schedule coordinating this provisioning across the network. Computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency—i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">carrier scheduling</i>—is an NP-hard problem. While recent work introduces learning-based systems for carrier scheduling, we find that their advantage over traditional heuristics progressively decreases for networks with hundreds of IoT nodes. Moreover, we find that their generalization is not consistent: it greatly varies across identically trained models while fixing the dataset, hyperparameters and random seeds used. We present RobustGANTT, a Graph Neural Network scheduler for backscatter networks that learns from optimal schedules of small networks (up to 10 nodes). Our scheduler generalizes, without the need for retraining, to networks of up to hundreds of nodes (100× training topology sizes), and exhibits consistent generalization across independent training rounds. We evaluate our system on both simulated topologies of up to 1000 nodes and real-life IoT network topologies of up to 300 IoT devices. RobustGANTT not only exhibits better generalization than existing systems, it also computes schedules achieving up to 2× less energy and spectrum utilization. Additionally, its polynomial runtime complexity allows it to react fast to changing network conditions. Our work facilitates the operation of large-scale IoT networks, and our machine learning findings further advance the capabilities of learning-based network scheduling. We release our code, datasets and pre-trained models.