In this work, we investigate the application of an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system in large-scale wireless sensor networks (WSNs). The specific research described in this paper can be divided into three parts. Firstly, it is well known that the energy consumption of WSNs and the limited-capacity battery of nodes lead to the limited lifetime of WSNs. To improve the lifetime of the WSNs, the UAV's optimal position and the optimal clustering scheme are determined using an improved differential evolution (DE) algorithm in multicluster scenarios. The corresponding energy consumption is determined by comparing different clustering schemes. Meanwhile, the shortest time for the UAV to charge nodes is determined by utilizing the proposed DE algorithm to search for the optimal charging location. Since the UAV usually acts as both a base station and an energy transmitter, the algorithm requires joint optimization of the clustering scheme and the UAV's charging location. Secondly, as the scale of WSNs increases, some areas will be in the "dead" state within the WSNs, which will greatly affect the performance. On the basis of the above studies, an improved local permutation algorithm is applied to plan an optimal path subject to the dormancy rate constraint in all regions. With the algorithm, the UAV needs to "rescue" the target region and charge regions along the path as much as possible. Finally, numerical results show that the optimized UAV trajectory can significantly improve the energy transmission efficiency, and the proposed optimal path planning improves the lifetime of WSNs.

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In the paper, we demonstrate an application of the wireless earbud -an acoustic ruler. Approaches are proposed to improve the robustness of the design in the low signal-to-noise ratio environment. We also share our solution to several engineering challenges, which aims at facilitating the transformation of earbuds to into acoustic sensing research platforms without any hardware modification. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.