  IoT sensors are crucial for visualizing multidimensional and multimodal
information and enabling future IT applications/services such as cyber-physical
space, digital twins, autonomous driving, smart cities, and virtual/augmented
reality (VR or AR). However, IoT sensors need to be battery-free to
realistically manage and maintain the growing number of available sensing
devices. Here, we provide a novel sensor design approach that employs
metasurfaces to enable multifunctional sensing without requiring an external
power source. Importantly, unlike existing metasurface-based sensors, our
metasurfaces can sense multiple physical parameters even at a fixed frequency
by breaking classic harmonic oscillations in the time domain, making the
proposed sensors viable for usage with limited frequency resources. Moreover,
we provide a method for predicting physical parameters using the machine
learning-based approach of random forest regression. The sensing performance
was confirmed by estimating temperature and light intensity, and excellent
determination coefficients larger than 0.96 were achieved. Our study affords
new opportunities for sensing multiple physical properties without relying on
an external power source or needing multiple frequencies, which markedly
simplifies and facilitates the design of next-generation wireless communication
systems.
