Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalable way. For communicating, a sensor tag relies on an unmodulated carrier provided by a neighboring IoT device, with a schedule coordinating this provisioning across the network. Carrier scheduling--computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency--is an NP-Hard optimization problem. Recent work introduces learning-based schedulers that achieve resource savings over a carefully-crafted heuristic, generalizing to networks of up to 60 nodes. However, we find that their advantage diminishes in networks with hundreds of nodes, and degrades further in larger setups. This paper introduces RobustGANTT, a GNN-based scheduler that improves generalization (without re-training) to networks up to 1000 nodes (100x training topology sizes). RobustGANTT not only achieves better and more consistent generalization, but also computes schedules requiring up to 2x less resources than existing systems. Our scheduler exhibits average runtimes of hundreds of milliseconds, allowing it to react fast to changing network conditions. Our work not only improves resource utilization in large-scale backscatter networks, but also offers valuable insights in learning-based scheduling.

翻译：无电池传感器标签是利用反向散射技术与标准物联网设备通信的设备，从而以可扩展的方式增强网络的感知能力。为实现通信，传感器标签依赖于邻近物联网设备提供的未调制载波，并通过网络范围内的调度协议来协调这种供应。载波调度——即计算调度方案以轮询所有传感器标签，同时最小化能耗、频谱利用率和延迟——是一个NP难优化问题。近期研究引入了基于学习的调度器，其在精心设计的启发式算法基础上实现了资源节约，并能泛化至最多60个节点的网络。然而，我们发现其优势在具有数百个节点的网络中减弱，并在更大规模部署中进一步退化。本文提出RobustGANTT，一种基于图神经网络的调度器，其无需重新训练即可将泛化能力提升至1000个节点的网络（训练拓扑规模的100倍）。RobustGANTT不仅实现了更好且更稳定的泛化性能，其生成的调度方案所需资源比现有系统减少高达2倍。该调度器平均运行时间为数百毫秒，使其能够快速响应变化的网络条件。我们的工作不仅提升了大规模反向散射网络的资源利用率，也为基于学习的调度研究提供了重要见解。