In this dissertation, we investigate optimal unmanned aerial vehicle (UAV) maneuvering design for relay and wireless sensor network (WSN) systems. Compared to conventional relay systems (CRSs) that utilize a ground relay node, UAV relay systems (URSs) can achieve better transmission capability by a strong line-of-sight (LOS) channel between UAV and ground nodes. In addition, compared to a ground data collector (e.g., mobile robot) whose mobility is limited by a number of obstacles, UAVs can be swiftly deployed and moved in three-dimensional (3D) free space to efficiently collect data in WSNs.
The first study focuses on URSs with non-orthogonal transmission scheme and optimizes resource allocation for a given UAV hovering location to provide wide coverage and reliable relay transmission. The main objective is to investigate which relay scheme is proper and how resource allocation affects the network performance. Although orthogonal relay transmission is widely adopted for CRSs, URSs requires non-orthogonal relay transmission to extend the operation range of UAV. Also, resource allocation needs to be optimized by adjusting transmission power and duration of each relay link especially when user equipments (UEs) are outside cell coverage or requires a high level of quality of service (QoS). Therefore, we compare non-orthogonal transmission scheme with orthogonal transmission scheme, and propose a resource allocation algorithm focusing on two transmit time slot to maximize throughput in the cell while guaranteeing seamless relay transmission.
The second study focuses on applications of UAV in WSNs and propose a UAV route determination algorithm for efficient data collection in sparsely deployed WSNs. In order to design a UAV route, UAV traveling distance and sensor energy status need to be considered simultaneously since UAV has limited on-board energy and the communication distance between UAV and each sensor affects sensor energy consumption in data transmission. An optimization problem constrained by data collection and UAV traveling distance is formulated, and solved by Voronoi diagram. In Voronoi diagram, UAV hovering location on either Voronoi vertex or Voronoi edge can serve multiple adjacent sensors, resulting in efficient data collection. Based on Voronoi diagram, the proposed algorithm finds a feasible UAV route (i.e., shortest UAV route that guarantees data collection at all sensors), then optimizes it by adjusting each UAV hovering location reflecting sensor energy status.
In the last study, for the sake of extending lifetime of WSNs, we consider a wireless charging sensor networks (WCSNs), where UAV is dispatched for data collection and wireless transfer power. Both energy consumption and harvesting at sensors are considered for UAV route design, where UAV hovering location and duration are optimized to maximize network lifetime while guaranteeing data collection at all sensors. Especially, compared to the second study that takes UAV traveling distance into account, the more practical UAV energy model is considered along with sensor energy status to optimize UAV hovering locations and duration. A proposed algorithm combines Lagrange multiplier method and geometry-based algorithms to find an optimal solution.