A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks

TL;DR

This paper introduces a neural network-based method for dynamic sensor selection in wireless sensor networks, optimizing for energy efficiency and robustness while maintaining high task accuracy, demonstrated on EEG data.

Contribution

It presents a novel end-to-end trainable approach for input-dependent sensor selection using Gumbel-Softmax, enhancing network lifetime and robustness in body-sensor applications.

Findings

Improved sensor network lifetime through dynamic selection constraints.

Enhanced robustness with a spatial filtering mechanism.

Effective trade-off management between transmission load and accuracy.

Abstract

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.