In this paper, an energy-efficient full-duplex (FD) unmanned aerial vehicle (UAV) relaying network is proposed, where UAV acts as a mobile relay and assists information exchange between two transceivers. Specifically, the load-carry-and-delivery scheme is applied to positively take advantage of the time-varying channel gain in delay-tolerant networks; meanwhile, the FD communication policy is used to potentially further increase the energy efficiency (EE). In particular, the self-interference channel gains follow the complex Gaussian distribution instead of being constant. The EE is first rigorously derived and then, the optimum flight speed is determined under the information causality constraint to maximize the EE. Numerical results demonstrate that the proposed scheme outperforms the half-duplex as well as static schemes in terms of the EE. In addition, the impact of the self-interference cancellation factor on the EE is also demonstrated, which provides valuable insights for the system design of UAV-assisted relaying networks.INDEX TERMS Energy efficiency (EE), delay-tolerant networks, full-duplex relaying (FDR), load-carryand-delivery (LCAD), UAV.
Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.
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