RESUMO
In this work, three-dimensional finite element analysis (3D FEA) of quasi-surface acoustic wave (QSAW) resonators with high accuracy is reported. The QSAW resonators consist of simple molybdenum (Mo) interdigitated transducers (IDT) on solidly mounted stacked layers of AlN/Mo/Si. Different to the SAW resonators operating in the piezoelectric substrates, the reported resonators are operating in the QSAW mode, since the IDT-excited Rayleigh waves not only propagate in the thin piezoelectric layer of AlN, but also penetrate the Si substrate. Compared with the commonly used two-dimensional (2D) FEA approach, the 3D FEA method reported in this work shows high accuracy, in terms of the resonant frequency, temperature coefficient of frequency (TCF), effective coupling coefficient (keff2) and frequency response. The fabricated QSAW resonator has demonstrated a keff2 of 0.291%, series resonant frequency of 422.50 MHz, and TCF of -23.418 ppm/°C in the temperature range between 30 °C and 150 °C, for the design of wavelength at 10.4 µm. The measurement results agree well with the simulations. Moreover, the QSAW resonators are more mechanically robust than lamb wave devices and can be integrated with silicon-based film bulk acoustic resonator (FBAR) devices to offer multi-frequency function in a single chip.
RESUMO
The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.
