  An important task in the Internet of Things (IoT) is field monitoring, where
multiple IoT nodes take measurements and communicate them to the base station
or the cloud for processing, inference, and analysis. This communication
becomes costly when the measurements are high-dimensional (e.g., videos or
time-series data). The IoT networks with limited bandwidth and low power
devices may not be able to support such frequent transmissions with high data
rates. To ensure communication efficiency, this article proposes to model the
measurement compression at IoT nodes and the inference at the base station or
cloud as a deep neural network (DNN). We propose a new framework where the data
to be transmitted from nodes are the intermediate outputs of a layer of the
DNN. We show how to learn the model parameters of the DNN and study the
trade-off between the communication rate and the inference accuracy. The
experimental results show that we can save approximately 96% transmissions with
only a degradation of 2.5% in inference accuracy. Our findings have the
potentiality to enable many new IoT data analysis applications generating large
amount of measurements.
