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Energy remains a major hurdle in running computation-intensive tasks on wireless sensors. Recent efforts have been made to employ a Mobile Charger (MC) to deliver wireless power to sensors, which provides a promising solution to the energy problem. Most of previous works in this area aim at maintaining perpetual network operation at the expense of high operating cost of MC. In the meanwhile, it is observed that due to the low cost of wireless sensors, they are usually deployed at high density so there is abundant redundancy in their coverage in the network. For such networks, it is possible to take advantage of the redundancy to reduce the energy cost. In this paper, we relax the strictness of perpetual operation by allowing some sensors to temporarily run out of energy while still maintaining target $k$k-coverage in the network at lower cost of MC. We first establish a theoretical model to analyze the performance improvements under this new strategy. Then, we organize sensors into load-balanced clusters for target monitoring by a distributed algorithm. Next, we propose a charging algorithm named $\lambda$λ-GTSP Charging Algorithm to determine the optimal number of sensors to be charged in each cluster to maintain $k$k-coverage in the network and derive the route for MC to charge them. We further generalize the algorithm to encompass mobile targets as well. Our extensive simulation results demonstrate significant improvements of network scalability and cost saving that MC can extend charging capability over 2-3 times with a reduction of 40 percent of moving cost without sacrificing the network performance. [ABSTRACT FROM AUTHOR]

The issue of the aerodynamic noises from wind turbine blades affecting the surrounding residents life begins to attract researcher's attention. Most of the existing researches are based on CFD software or experimental data fitting method to analyze the aerodynamic noises, so it is difficult to adapt the demand to dynamic analysis of the aerodynamic noises from wind speed variation. In this paper, the operation parameters, the inflow wind speed and the receiver location are considered, and a modified model to calculate aerodynamic noises from wind turbine blades which is based on traditional acoustic formulas is established. The program to calculate the aerodynamic noises from the 2 MW wind turbine blades is compiled using a time-domain analysis method based on the Simulink modular in Matlab software. And the pressure time sequence diagrams of the aerodynamic noises from wind turbine blades are drawn. It has provided a theoretical foundation to develop low noise wind turbine blades. [ABSTRACT FROM AUTHOR]

The emergence of 5G technology has enabled the development of Metaverse applications that provide users with immersive experiences through augmented reality (AR) devices, and the integration of federated learning (FL) with the Metaverse AR (MAR) systems can enable many edge intelligence services in 5G. However, the presence of nonindependent and identically distributed (Non-IID) data across all AR users' devices, coupled with limited edge communication resources, makes it challenging to achieve human-centric Metaverse-related applications such as target detection or image classification that combine virtual content with real-world. To address these challenges, we propose a novel adaptive resource-efficient Metaverse-based FL (AMFL) algorithm for AR applications that mitigates the negative effect of Non-IID data and reduces resource costs as well as improves the quality of experience (QoE). We first analyze the impact of wireless communication factors such as CPU frequency, bandwidth, and transmission power on FL training performance by a toy example in the MAR systems. Based on this analysis, furthermore, we establish a Non-IID degree, model accuracy, and resource consumption-related QoE maximization problem under given resource budgets, which is a stochastic optimization problem with strongly coupled variables, including bandwidth, CPU frequency, and transmission power. Guided by the theoretical analysis, to solve this issue, AMFL employs a deep reinforcement learning (DRL)-based method to adaptively allocate resources. Numerical results demonstrate that AMFL can significantly improve the QoE by up to 30.28 % , and reduce communication round and energy costs by up to 81.08 % and 72.20 % , respectively, even under the worst Non-IID case, compared to benchmarks.