Recently, an unmanned aerial vehicle (UAV) has been widely adopted to make efficient use of network resources in such areas as internet of things (IoT), sensor networks and three dimensional (3D) wireless networks. Especially, in wireless sensor networks (WSNs) where energy consumption of sensors in data transmission is the most conspicuous feature, data collection by UAV provides a promising solution. To address this issue, we consider a UAV-enabled WSN, where a UAV is dispatched to collect data from sensors distributed in networks. We formulate an optimization problem to maximize the minimum residual energy of sensors after data transmission for energy-efficient UAV routing subject to data collection and UAV traveling distance constraints. To solve the non-convex optimization problem, we first derive a feasible solution, i.e., the shortest UAV route that guarantees data collection at all the sensors, where a Voronoi diagram is modified to find a set of UAV hovering locations. The proposed algorithm preferentially determines each UAV hovering location at Voronoi vertex so that UAV can collect data from as many adjacent sensors as possible. Then with an initial shortest UAV route, a UAV route is proposed by adjusting each UAV hovering location sequentially based on sensor energy status, which is easily accomplished by the properties of Voronoi diagram. Lastly, to find the proposed solution more quickly, we propose a sensor-energy based initial UAV route determination method. Simulation results are provided to validate the performance of our proposed algorithm, and to compare with other UAV route determination schemes.

An unmanned aerial vehicle (UAV) can be utilized as a flying data collector and wireless power source in wireless charging sensor networks (WCSNs). Different from conventional studies that focused to maximize the efficiency of wireless power transfer (WPT) for UAV route design, in this paper we maximize the lifetime of WCSNs by considering sensor energy consumption and energy harvesting simultaneously. We consider simultaneous wireless information and power transfer (SWIFT), where data collection capability guarantees power leftover for UAV to complete its round-trip flight. Our objective is to jointly optimize the UAV hovering location and duration to maximize the minimum energy of sensors after data transmission and energy harvesting under data collection and UAV energy consumption constraints. To tackle this non-convex optimization problem, we first assume that UAV hovering location for each sensor is fixed and optimize UAV hovering duration by Lagrange multiplier method. Next, for each UAV hovering location, we propose a geometry-based update algorithm, which can be used to find initial feasible UAV routes to the problem. Lastly, a near-optimal UAV route is determined by adjusting the initial feasible UAV route iteratively, where UAV hovering locations and duration are updated at each iteration. Numerical results are provided to validate the performance of our proposed algorithm.

A novel resource allocation (RA) algorithm for non-orthogonal transmission is proposed to maximize overall throughput of user equipments (UEs) in unmanned aerial vehicle (UAV) relay systems (URSs), which jointly optimizes transmit duration and power of signals at base station (BS) and UAV. Also, the proposed RA algorithm is shown to maximize the overall throughput of UEs while guaranteeing a seamless UAV relay transmission through the analysis of outage probability and power control strategy. Simulation results reveal that the proposed RA algorithm for non-orthogonal transmission in URSs achieves the maximum throughput of UEs within the actual operation range of UAV.

UAV equipped three-dimensional (3D) wireless networks can provide a solution for the requirements of 5G communications, such as enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). Especially, the introduction of an unmanned aerial vehicle (UAV) as a relay node can improve the connectivity, extend the terrestrial base station (BS) coverage and enhance the throughput by taking advantage of a strong air-to-ground line of sight (LOS) channel. In this paper, we consider the deployment and resource allocation of UAV relay network (URN) to maximize the throughput of user equipment (UE) within a cell, while guaranteeing a reliable transmission to UE outside the coverage of BS. To this end, we formulate joint UAV deployment and resource allocation problems, whose analytical solutions can be hardly obtained, in general. We propose a fast and practical algorithm to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. The transmit power at BS and UAV is determined in advance based on the availability of channel state information (CSI). Simulation results demonstrate that the proposed algorithms can significantly reduce the computational effort and complexity to determine the optimal UAV location and transmit time slots over an exhaustive search.

In multiple target tracking, one of the crucial techniques is how to estimate unknown initiation points of tracks by incorporating a set of measurements under noisy and cluttered environment. Due to unknown data association and false alarms, all possible track initiation points need to be evaluated, which suffers from computational complexity. In this paper, we propose a computationally efficient track initiation method for multi-static multi-frequency passive coherent localization systems, where bistatic measurements from different illuminators are incorporated at a receiver to find the most probable track initiation points. By the proposed likelihood utility function, our method resolves unknown data association between measurements and targets to reduce computational complexity. In addition, the proposed method can be extended to process a set of multi-scan measurements in association-free manner, which results in improved estimation accuracy. Simulation results are provided to validate the performance of our proposed method.