Three-dimensional sound-speed distribution is essential for large-scale underwater acoustic applications due to its influence on the signal propagation trajectory. However, it is labor, energy, and time consuming to measure sound speed by traditional methods because of the weak system maneuverability. In this article, an autonomous-underwater-vehicle-assisted underwater sound-speed inversion framework that collaborates ray tracing and artificial intelligence model is proposed to quickly obtain 3-D sound-speed distribution through multicoordinate inversions. An autoencoding-translation neural network is proposed to establish the nonlinear relationship from signal propagation time to the sound-speed profile (SSP), and the inversion time can be shortened with once forward propagation through the model. Robustness could be improved by inverting error-resistant implicit features into the SSP through the proposed translating neural network, whereas the implicit features are extracted from the autoencoder by denoising reconstruction of the input time information. To solve the overfitting problem and extend the training data set, virtual SSPs based on sparse feature points of real SSPs are generated. Simulation results show that our approach can provide a reliable and instantaneous monitoring of 3-D sound-speed distribution.

We design an ocean surface drifting buoy system based on an unmanned aerial vehicle (UAV)-enabled wireless powered relay network in which the UAV acts as mobile hybrid access point that broadcasts energy to all buoys in the downlink and forwards information from the buoys to a ship signal tower (ST) in the uplink. In order to maximize the resource allocation efficiency of the system, due to the different initial energy reserve of the buoys, a novel communication mode selection strategy is proposed. In the direct transmission mode (DT mode), an energy-sufficient buoy transmits information directly to the ST, and in the relay transmission mode (RT mode), an energy-insufficient buoy relays information to the ST through the UAV. By applying the block coordinate descent and successive convex optimization, a joint UAV trajectory and resource allocation algorithm is proposed to maximize the minimum throughput of the buoys to work in the RT mode. Simulation results show that the proposed algorithm can significantly improve the minimum throughput of the ocean surface drifting buoys.

This paper investigates mobile relaying in wireless powered communication networks (WPCN), where an unmanned aerial vehicle (UAV) is employed to help information delivery from multiple sources to destination with communication channels severely blocked. The sources are low-power without energy supply. To support information transmission, the UAV acts as a hybrid access point (AP) to provide wireless power transfer (WPT) and information reception for sources. We set the issue of system throughput maximization as the optimization problem. On the one hand, the system is subject to the information causality constraint due to the dependent processes of information reception and transmission for the UAV. On the other hand, the sources are constrained by a so-called neutrality constraints due to the dependent processes of energy harvesting and energy consumption. In addition, we take account of the access delay issue of all ground nodes. Specifically, two paradigms of delay-tolerant case and delay-sensitive case are presented. However, the formulated problem including optimizations for time slot scheduling, power allocation and UAV trajectory is non-convex and thus is difficult to obtain its optimal solution. To tackle this problem, we apply the successive convex approximation (SCA) technique and propose an iterative algorithm by which a suboptimal solution can be achieved. Simulation results validate our proposed design, and show that the obtained suboptimal solution is high-quality, as compared to benchmark scheme.

Sensor data search is an imminent component for the booming Internet of Things (IoT). Current proposals would refer to either keyword or spatial location, then the matched sensor data should be manually selected by requesters. However, the selection is arduous as there are massive connected devices. To this end, we propose a Combinatorial-Oriented Feedback (COF) mechanism to guarantee the reliability and accessibility of feedback results via enabling an intuitive exhibition of aggregated sensor data. Two critical issues are addressed in this paper: Sensor data combination and aggregated results ranking. To facilitate the combination process, we first propose two decisive factors for aggregated result evaluation. The Multiple Sensor-data Combinatorial (MSC) problem is converted into a multi-objective optimization model. To balance the trade-off between competing quality metrics, we introduce Pareto Sensor Set (PSS) as an optimal solution for MSC problem, and devise the Elitist Directive Breeding (EDB) method to get PSS solutions. In order to speed up the search efficiency and improve the feedback recall, we then develop the Fast Elitist Directive Breeding (FEDB) algorithm, which is able to return top-k ranked results according to different searching requirements. To demonstrate the effectiveness of COF mechanism, we conduct extensive simulations based on a virtual mobile sensing scenario. The results show that our proposed COF mechanism outperforms the state-of-the-art solutions significantly in terms of search efficiency and feedback quality, and specially, FEDB responds quite faster than EDB under the command of top-k ranking.