Abstarct: In this thesis, we have considered an IoT network in which a rotary-wing UAV is assigned as a mobile relay to transmit constantly updated IoT device information through direct line-of-sight and indirect line-of-sight communication lixnks by using the non-orthogonal multiple access technique available between Internet of Things devices to send information to the baxse station for processing. Most of the UAV-assisted data collection projects have neglected the time value of data collected. Therefore, we use the average peak age of information as a measure to quantify the temporal correlation between information that has been sampled sequentially by IoT devices. Also, due to having a battery with a limited capacity, the UAV does not have the ability to fly non-stop for a long period of time. Therefore, we charge the UAV battery from a distributed laser charging source that transmits power through the wireless power transmission protocol so that the UAV does not need to stop to change the battery or return to the ground charging station. The energy consumption of UAV and Internet of Things devices affects the efficiency of the system in terms of energy. Therefore, to check the energy consumption performance of UAV and Internet of Things devices in this text, we have optimized the energy efficiency of the system at the same time by optimizing the average value of the information peak. In simulations baxsed on deep reinforcement learning (DRL) algorithms, we observe that algorithms such as deep deterministic delay twin algorithm (TD3) compared to deep Q network (DQN) and actor-critic baxsed algorithms such as deep deterministic policy gradient (DDPG) in terms of the average peak age of achievable (PAoI) information and energy efficiency (EE) performs better.