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.

中文翻译：

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基于超材料传感器的物联网：设计、优化和实现

对于物联网（IoT）设想的许多应用，预计传感器将具有非常低的成本和零功耗，这可以通过基于超材料传感器的物联网（即元物联网）来满足。由于其构成的超材料可以反射具有环境敏感反射系数的无线信号，因此元物联网传感器可以在没有任何主动调制的情况下实现同时传感和传输。然而，为了最大限度地提高传感精度，需要优化元物联网传感器的结构，考虑到它们对传感和传输的联合影响，由于评估影响的计算复杂性很高，尤其是在传感器数量众多的情况下，这具有挑战性。在本文中，我们提出了一种针对具有大量元物联网传感器的元物联网系统的联合传感和传输设计方法，可以有效优化系统的传感精度。具体而言，建立了一个计算有效的接收信号模型，以评估超材料结构对传感和传输的联合影响。然后，设计了一种基于深度无监督学习的感知算法，以稳健的方式获得准确的感知结果。通过样机实验验证，该系统与现有设计相比具有更高的灵敏度和更长的传输距离，并且能够正确感知2米范围内的环境异常。设计了一种基于深度无监督学习的传感算法，以稳健的方式获得准确的传感结果。通过样机实验验证，该系统与现有设计相比具有更高的灵敏度和更长的传输范围，并且可以在2米范围内正确感知环境异常。设计了一种基于深度无监督学习的传感算法，以稳健的方式获得准确的传感结果。通过样机实验验证，该系统与现有设计相比具有更高的灵敏度和更长的传输距离，并且能够正确感知2米范围内的环境异常。