  For many applications envisioned for the Internet of Things (IoT), it is
expected that the sensors will have very low costs and zero power, which can be
satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their
constituent meta-materials can reflect wireless signals with
environment-sensitive reflection coefficients, meta-IoT sensors can achieve
simultaneous sensing and transmission without any active modulation. However,
to maximize the sensing accuracy, the structures of meta-IoT sensors need to be
optimized considering their joint influence on sensing and transmission, which
is challenging due to the high computational complexity in evaluating the
influence, especially given a large number of sensors. In this paper, we
propose a joint sensing and transmission design method for meta-IoT systems
with a large number of meta-IoT sensors, which can efficiently optimize the
sensing accuracy of the system. Specifically, a computationally efficient
received signal model is established to evaluate the joint influence of
meta-material structure on sensing and transmission. Then, a sensing algorithm
based on deep unsupervised learning is designed to obtain accurate sensing
results in a robust manner. Experiments with a prototype verify that the system
has a higher sensitivity and a longer transmission range compared to existing
designs, and can sense environmental anomalies correctly within 2 meters.
