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Wireless power transfer (WPT) has been widely used in IoT applications, such as mobile device charging, biomedical implants communication, and RFID field. Maximizing the power transfer efficiency (PTE) becomes one of the most crucial problems for designing the WPT systems. Magnetic induction (MI) beamforming has been proposed recently to maximize the PTE for the near field MIMO WPT systems. However, conventional magnetic beamforming in WPT systems usually requires accurate magnetic channel estimation, both amplitude and phase control of the charging source, which can not be achieved in an extreme environment. In this paper, we propose a novel magnetic induction beamforming scheme in MIMO WPT system using a reconfigurable metasurface. Instead of controlling the source currents or voltages, the reconfigurable metasurface can achieve near field beamforming only by varying the capacitor and resistance in specific coil array units. The beamforming is modeled as a discrete optimization problem and solved by using the Simulate Anneal (SA) method. Through the analytical and COMSOL simulation results, our proposed beamforming scheme can achieve approximately two times PTE of the conventional beamforming method in a 40 cm charging distance. 

Underwater motion recognition using acoustic wireless networks has a promisingly potential to be applied to the diver activity monitoring and aquatic animal recognition without the burden of expensive underwater cameras which have been used by the image-based underwater classification techniques. However, accurately extracting features that are independent of the complicated underwater environments such as inhomogeneous deep seawater is a serious challenge for underwater motion recognition. Velocities of target body (VTB) during the motion are excellent environment independent features for WiFi-based recognition techniques in the indoor environments, however, VTB features are hard to be extracted accurately in the underwater environments. The inaccurate VTB estimation is caused by the fact that the signal propagates along with a curve instead of a straight line as the signal propagates in the air. In this paper, we propose an underwater motion recognition mechanism in the inhomogeneous deep seawater using acoustic wireless networks. To accurately extract velocities of target body features, we first derive Doppler Frequency Shift (DFS) coefficients that can be utilized for VTB estimation when signals propagate deviously. Secondly, we propose a dynamic self-refining (DSR) optimization algorithm with acoustic wireless networks that consist of multiple transmitter-receiver links to estimate the VTB. Those VTB features can be utilized to train the convolutional neural networks (CNN). Through the simulation, estimated VTB features are evaluated and the testing recognition results validate that our proposed underwater motion recognition mechanism is able to achieve high classification accuracy. 

Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations. 

Water resource has become one of the most precious resources in recent decades. Agriculture accounts for about 80\% of the total water usage in US. There is a demanding need for efficient irrigation and water management systems built for sustainable water utilization in smart agriculture. Real time in-situ soil moisture sensing is a vital part for smart agriculture. Traditional electromagnetic (EM) based soil moisture sensing relies on EM based wireless sensor or ground penetrating radar (GPR) system. Based on the receiving signal strength and delay, tomographic techniques are used to derive the dielectric parameters of the soil, which are then into soil moisture distribution using empirical model. However, the EM signal attenuate sharply during underground propagation because of high operating frequency and lossy medium. In order to counter the disadvantage for underground sensing, we propose a Magnetic Induction (MI) based large range soil moisture sensing scheme in inhomogeneous environments. Here, we present the topology of the sensing system and analyze the channel model. The sensing process is based on transformed model, the conductivity and permittivity distribution are derived using SIRT algorithm. Through COMSOL simulation and analytical results, our proposed soil moisture sensing method achieves a root mean square error (RMSE) of 0.06 m^3/m^3 in 40 m 2D scale inhomogeneous environment range. 

The acoustic cooperative multi-input-multi-output (MIMO) systems equipped on the underwater robot swarms (URSs) can enable long-range and high-throughput communications. However, the acoustic communications cannot provide the real-time and accurate synchronization for the distributed transmitters of the cooperative MIMO due to the large delay of acoustic channels. In addition, the narrow bandwidth of the acoustic channel further enlarges the synchronization time and errors. In this paper, we propose the metamaterial magnetic induction (M2I)-assisted acoustic cooperative MIMO to address aforementioned challenges. The synchronization time can be reduced since the M2I has negligible signal propagation delays. To quantitatively analyze the improvement, we deduce the synchronization errors, signal-to-noise ratio (SNR), eective communication time, and the throughput of the system. Finally, the improvement of using M2I-assisted synchronization is validated by the numerical evaluation. 

Magnetic induction (MI) communication are widely used in applications in extreme environments, including environment surveillance, past disaster rescue, and resource detection since it does not su↵er from high material absorption in lossy media. However, existing MI systems rely on high transmitting power and large antenna to reach practical communication range. Recently, metamaterial enhanced MI (M2I) communication was proposed, which can increase the signal strength of the original MI system to 30 dB in theory. However the latest practical implementation of M2I system only achieves an 8 dB gain due to the metamaterial loss. In this paper, the active metamaterial unit is introduced to the current M2I communication system to close the performance gap between theoretical and practical results. The antenna system is optimized based on the rigorously model of circuit, coil array structure and channel. Through analytical deduction and COMSOL simulations, the proposed active M2I antenna system shows significant power gain and improvement in communication range compared with the passive M2I system and the original MI system. 

Many important applications in the extreme environment require wireless communications to connect smart devices. Metamaterial-enhanced magnetic induction (M2I) has been proposed as a promising solution thanks to its long communication range in the lossy medium. M$^2$I communication relies on magnetic coupling, which makes it intrinsically full-duplex without self-interference. Moreover, the engineered active metamaterial provides reconfigurability in communication range and interference. In this paper, the new networking paradigm based on the reconfigurable and full-duplex M2I communication technique is investigated. In particular, the theoretical analysis and electromagnetic simulation are first provided to prove the feasibility. Then, a medium access control protocol is proposed to avoid collisions. Finally, the capacity and delay of the full-duplex M2I network are derived to show the advantage of the new networking paradigm. The analysis in this paper indicates that in a full-duplex M2I network, the distance between the source and destination can be arbitrarily long and the end-to-end delay can be as short as a single hop delay. As a result, each node in such network can reach any other node by one hop, which can greatly enhance the network robustness and efficiency. It is important for timely transmission of emergent information or real-time control signals.