Meta-IoT: Simultaneous Sensing and Transmission by Meta-Material Sensor-Based Internet of Things

Hu, Jingzhi; Zhang, Hongliang; Di, Boya; Bian, Kaigui; Song, Lingyang

doi:10.1109/twc.2022.3145842

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…In [18], the authors proposed to sense temperature with an array of meta-material-based wireless sensors, and demonstrated the feasibility of wireless temperature sensing up to 200 • C. In [19], the authors designed a meta-material-based sensor to sense humidity and methanol-deionized water solutions inside a waveguide testbed. In [20][21],…”

Section: B Meta-meterial Sensor Based Systemsmentioning

confidence: 99%

“…In (2a), it can be observed that the environmental conditions determine the resistance of meta-IoT units, which influences the impedance and the reflection coefficients of the meta-IoT by DA as in [21]. Specifically, in this paper, we focus on the performance of meta-IoT sensors under the N C preselected environmental conditions, which are denoted by set C. Then, for each c ∈ C, the interpolation is performed in terms of d, and the resulting function is denoted by…”

Section: B Reflection Coefficient Modelmentioning

confidence: 99%

“…Then, as the objective function in (P1) contains the non-convex function ξ n (d, c) in ( 20), (P1) is non-convex, which is hard to solve. To solve (P1) efficiently, we can adopt efficient algorithms such as the pattern search algorithm [35] or the surrogate optimization algorithm as in [21], which can handle finitely bounded non-convex optimization problems and has a high probability of finding global optimum. The proposed algorithm for solving (P1) is summarized as Algorithm 1.…”

Section: B Algorithm Designmentioning

confidence: 99%

See 2 more Smart Citations

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

Hu¹,

Zhang²,

Di³

et al. 2022

IEEE Trans. Commun.

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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.

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