A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks
This addresses energy efficiency and accuracy in wireless sensor networks for applications like healthcare monitoring, but it is incremental as it builds on existing dynamic selection and Gumbel-Softmax methods.
The paper tackles the problem of optimizing sensor selection in bandwidth-constrained body-sensor networks by proposing a dynamic approach that selects optimal sensor subsets per input sample, jointly learned with the task model, and validates it on real EEG data, showing trade-offs between transmission load and task accuracy.
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.