Meta-material Sensor Based Internet of Things: Design, Optimization, and Implementation

TL;DR

This paper introduces a novel meta-material sensor-based IoT system that optimizes sensing and transmission simultaneously, employing deep learning for robust sensing, and demonstrates improved sensitivity and range through prototype experiments.

Contribution

It presents a joint sensing and transmission optimization method for large-scale meta-IoT systems, combining efficient modeling and deep learning for enhanced performance.

Findings

Higher sensitivity compared to existing designs

Longer transmission range achieved

Accurate environmental anomaly sensing within 2 meters

Abstract

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.