Meta-Material Sensor-Based Internet of Things for Environmental Monitoring by Deep Learning: Design, Deployment, and Implementation

Abstract

Using widely deployed Internet of Things (IoT) sensors to perceive the environmental distribution is crucial in many IoT applications, such as intelligent healthcare and smart home. As using traditional sensors will lead to high costs and maintenance, it is important to design the next-generation IoT sensor to reduce the cost of ubiquitous deployment. For this purpose, we propose a novel IoT system based on low-cost and fully passive meta-material sensors. Specifically, the meta-material sensors can sense multiple environmental conditions such as temperature and humidity levels, and transmit back the information by signal reflection, simultaneously. With the information contained in the received signals, a wireless receiver can obtain detailed environmental distributions. However, it is not trivial to achieve high sensing accuracy in the meta-material sensor based IoT system because the structure of the meta-materials, the deployment positions of sensors, and the reconstruction function for environmental distributions need to be jointly optimized. To handle this challenge, we propose an algorithm to design the meta-material based IoT system with the help of a deep learning approach. Simulation results verify that the proposed algorithm effectively maximizes the sensing accuracy. Experimental evaluations also show that the proposed scheme can obtain humidity distribution with an accuracy of over 93%.