In wireless Internet of things (IoT), the sensors usually have limited
bandwidth and power resources. Therefore, in a distributed setup, each sensor
should compress and quantize the sensed observations before transmitting them
to a fusion center (FC) where a global decision is inferred. Most of the
existing compression techniques and entropy quantizers consider only the
reconstruction fidelity as a metric, which means they decouple the compression
from the sensing goal. In this work, we argue that data compression mechanisms
and entropy quantizers should be co-designed with the sensing goal,
specifically for machine-consumed data. To this end, we propose a novel deep
learning-based framework for compressing and quantizing the observations of
correlated sensors. Instead of maximizing the reconstruction fidelity, our
objective is to compress the sensor observations in a way that maximizes the
accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior
work, we do not impose any assumptions about the observations distribution
which emphasizes the wide applicability of our framework. We also propose a
novel loss function that keeps the model focused on learning complementary
features at each sensor. The results show the superior performance of our
framework compared to other benchmark models.

本文提出一种基于深度学习的机器消耗数据压缩和量化框架，以最大化在融合中心推导出的全局决策的准确性为目标，并提出以感测目标为设计基础的数据压缩机制和熵量化器方法，相较于其他基准模型在性能上有较大提升。