We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. This dynamic selection is jointly learned with the task model in an end-to-end way, using the Gumbel-Softmax trick to allow the discrete decisions to be learned through standard backpropagation. We then show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit. We further improve performance by including a dynamic spatial filter that makes the task-DNN more robust against the fact that it now needs to be able to handle a multitude of possible node subsets. Finally, we explain how the selection of the optimal channels can be distributed across the different nodes in a WSN. We validate this method on a use case in the context of body-sensor networks, where we use real electroencephalography (EEG) sensor data to emulate an EEG sensor network. We analyze the resulting trade-offs between transmission load and task accuracy.

翻译：我们提出了一种面向深度神经网络（DNN）的动态传感器选择方法，该方法能够针对每个特定输入样本推导出最优传感器子集选择，而非为整个数据集采用固定选择。这种动态选择通过Gumbel-Softmax技巧以端到端方式与任务模型联合学习，使得离散决策能够通过标准反向传播进行学习。随后，我们展示了如何通过限制每个节点允许传输的频率，利用这种动态选择来延长无线传感器网络（WSN）的寿命。我们进一步通过引入动态空间滤波器来提升性能，该滤波器使任务型DNN能够更鲁棒地处理需要应对多种可能节点子集的情况。最后，我们阐述了如何在WSN的不同节点间分布式实现最优信道的选择。我们在体域网场景下验证了该方法，利用真实脑电图（EEG）传感器数据模拟EEG传感器网络，并分析了传输负载与任务准确率之间的权衡关系。