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Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.

Flexible piezoelectric zinc oxide (ZnO)-based nanogenerators (NGs) using an aluminum nitride (AlN) interlayer are proposed for high-efficiency energy harvesting applications. The effects of the AlN interlayer on device performance are studied. Use of the AlN interlayer in ZnO-based vertically integrated NGs (VINGs) results in a significant improvement in terms of the magnitude of the output voltages of up to 200 times when compared with a ZnO-based VING without any AlN interlayer. The improved device energy conversion efficiency is mainly attributed to a high contact potential barrier that the AlN interlayer provides in VINGs, along with the relatively high dielectric constant and large Young's modulus of the AlN material. In addition, the effects of AlN thickness on the electric potential and device performance of the VINGs are investigated through observation of the output voltages of ZnO-based VINGs with thickness/position-controlled AlN interlayers. Our findings in this work are expected to provide effective and useful approaches for realizing highly energy-efficient ZnO-based NGs and their extended applications, including self-power sources and sensor devices.

The free-carrier-modulated ZnO:N thin film-based flexible nanogenerators (NZTF-FNGs) are proposed and experimentally demonstrated. The suggested flexible nanogenerators (FNGs) are fabricated using N-doped ZnO thin films (NZTFs) as their piezoelectric active elements, which are deposited by a radio frequency magnetron sputtering technique with an N2O reactive gas as an in situ dopant source. Considerable numbers of N atoms are uniformly incorporated into NZTFs overall during their growth, which would enable them to significantly compensate the unintentional background free electron carriers both in the bulk and at the surface of ZnO thin films (ZTFs). This N-doping approach is found to remarkably enhance the performance of NZTF-FNGs, which shows output voltages that are almost two orders of magnitude higher than those of the conventionally grown ZnO thin film-based FNGs. This is believed to be a result of both substantial screening effect suppression in the ZTF bulk and more reliable Schottky barrier formation at the ZTF interfaces, which is all mainly caused by the N-compensatory doping process. Furthermore, the NZTF-FNGs fabricated are verified via charging tests to be suitable for micro-energy harvesting devices.