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Meta-Material Sensor Based Internet of Things: Design, Optimization, and Implementation
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DOI：10.1109/TCOMM.2022.3187150.png)
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摘要
En 中文
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.
Keyword:
Sensors
Wireless sensor networks
Wireless communication
Signal processing algorithms
Computational modeling
Antenna arrays
6G mobile communication
Meta-materials
Internet of Things
passive sensors
unsupervised learning
期刊
IF:
8.3
论文数:
1.2W
被引数:
3.6W
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Meta-IoT: Simultaneous Sensing and Transmission by Meta-Material Sensor-Based Internet of Things
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