UAVs have a wide application space in WSNs, which cannot directly communicate with the data center. Ref. [16] discuss a UAV-powered WSN, where the UAV transmits energy to the ground sensor through the antenna, and the sensor will send the collected information to the UAV after receiving it. The author minimizes the time required for the UAV to collect information by jointly optimizing the height of the UAV and the antenna beamwidth. In [17], the authors proposed a task offloading mechanism learning algorithm, which can predict the queuing delay of all UAVs, reduce network overhead and increase user satisfaction. Ref. [18] considered a large-scale WSN where some GSN may not be able to upload information for a long time, resulting in insufficient storage capacity. The authors proposed a data collection strategy to minimize the data loss by jointly optimizing the sensor scheduling and the UAV’s trajectory. Refs. [19,20] investigated the energy consumption problem of the UAV-assisted WSN. Zhu et al. [19] proposed a novel optimization algorithm based on a deep reinforcement learning technique that can effectively reduce the UAV’s consumption. Beak et al. [20] model the UAV collecting ground sensor information as a non-convex problem, and optimize the trajectory by the Voronoi diagram to maximize the residual energy after the sensor transmits information.

Since UAVs are more vulnerable to eavesdropping by illegal parties, some recent studies have considered the physical layer security of UAV-assisted WSNs. Ref. [21] investigate a UAV-assisted WSN with multiple eavesdroppers, and considered a downlink secure transmission scheme based on power splitting, where the transmission power of the UAV is divided into information transmission and noise generation. The authors proposed an optimization algorithm to maximize the minimum average secrecy rate. In [22], the authors considered how to improve the quality of service (QoS) of the wireless networks, joint optimization of the video levels selection, power allocation, and a UAV trajectory algorithm is proposed to maximize the ratio of power consumption to video quality. Refs. [23,24] discussed secrecy capacity maximum problem in cache-enabled UAV communications. Ref. [23] investigate a UAV-enabled network with D2D communications, where the UAV and D2D transmitter are equipped with caches that the users can directly obtain high-frequency communication requirements without communicating with the base station. In [24], the caching-equipped UAV is used to replace the small cell to communicate with the user, and the replaced cell is used as the interference source to send interference signals to Eve to improve the security performance of the system.

To realize the goal of energy-efficient communication while ensuring communication, secrecy energy efficiency (SEE) has increasingly become hot research in UAV-assisted WSNs. Li et al. [25] discuss two main challenges in a UAV-enabled WSN: the UAV’s energy consumption and secure transmission. The authors proposed a low-complexity iterative algorithm to maximize the secrecy energy efficiency. In [26], the authors discussed a multi-carrier multi-UAV enabled WSN, where the UAVs use Cooperative Rate-Splitting (CRS) technique to protect the communication between UAVs and the ground sensors, and proposed a secure resource allocation alternating iterative algorithm to maximize the UAV’s SEE by jointly optimizing the resource allocation and the ground sensors’ association matrix. Refs. [27,28] both introduced the simultaneous wireless information and power transfer (SWIPT) technology when considering the maximization of Secrecy Energy Efficiency, among which Ref. [27] assumed that the users divide the received signal into two parts, which are used for energy collection and information decoding, respectively. Ref. [28] assumed that only known the channel distribution information (CDI) of Eves. In addition, the dual-layer PS receiver architecture is introduced to solve the problem of energy harvesting (EH) circuits’ performance limitation.