## 3 UAV Deployment and Trajectory
### A Mobile UAV  
#### 3.2.1 UAV Aggregator  
A mobile UAV has attracted attention on IoT applications and wireless sensor networks to collect a data from or to disseminate an information to devices or sensors as a moving aggregator [48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. To efficiently utilize UAVs, optimal deployment or trajectory and energy efficiency of UAVs should be considered, and these are the main challenges of the UAV aggregator to prolong the lifetime of networks and maintain stable systems. Ref. [48] investigates the optimal trajectory and deployment of multiple UAVs to enable reliable uplink communications for data collection from IoT devices with a minimum energy consumption by properly clustering IoT devices and serving each cluster with one UAV. This study is extended in [49] to jointly optimize the 3D placement and the mobility of the UAVs, UAV-device association, and uplink power control. The proposed approach achieves the reduction of total transmit power of IoT devices by 45% and the improvement of system reliability by 28% compared with static UAV BSs. Ref. [50] minimizes the mission completion time to find a target (e.g., set a coverage area) and to setup a communication link (e.g., network connectivity) by prioritizing coverage or connectivity depending on the mission demands. Ref. [51] addresses the capacity and delay of UAV wireless networks with multiple UAVs to monitor the 3D environment, and derives the capacity and delay scaling laws of UAV wireless networks with a mobility pattern information. UAVs deliver a delay-tolerant data with a store-carry-and-forward (SCF) method when UAVs return to control station to charge the battery. The capacity of the networks increases and the delay decreases as more UAVs are deployed. Ref. [52] applies a Markov chain to model the irregular mobility of a single UAV for data collection from sensors. It also investigates the success probability of information transmission depending on the distance between UAV and sensors. Ref. [53] jointly optimizes the sensors' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption at sensors to prolong the network lifetime. Ref. [54] explores a UAV as a SCF node to enhance the availability of delivery path and to reduce the end-to-end packet delivery delay for VANET, which is one of the major vertical applications in 5G networks. In [55], wireless sensor network (WSN) is used to monitor animals. To determine the best monitoring scheme under different environments, it investigates the average energy consumption and the buffer capability of two proposed data collection systems, which determines the UAV trajectory. Ref. [56] formulates the data collection utility maximisation problem (UMP) to improve the data collection efficiency and considers it jointly on the determination of UAV trajectory. The cooperative trajectory planning algorithm in large-scale WSN is proposed in [57] to minimize the total mission time to collect data. It first divides the overall mission area into subregions where UAV can stay for data collection. In this study, a battery-carrying truck is considered as a mobile recharging station, and follows UAV. The proposed algorithm also takes the position of the truck into account for the determination of UAV trajectory because the distance between UAV and truck affects the mission completion time when UAV is out of battery. From the literature review, it is obvious that the UAV trajectory in IoT and wireless sensor networks targets to minimize the energy consumption and mission time to prolong the network lifetime.