## 7 Conclusions  
This paper examined critical components and important issues for an efficient design of AoI-minimal UAV-assisted data-gathering WSN/IoT applications. Our SLR study identified three critical components or primary design aspects from the selected articles, which include energy consumption, UAV flight, and trajectory control, and UAV and sensor node scheduling.  
One of the interesting findings from this study is that UAV trajectory has been considered the most important element in AoI optimization by the majority of researchers. In addition, minimizing the overall UAV flight distance by proper trajectory planning and scheduling could significantly reduce the energy consumption of the UAV. The speed of the UAV also needs to be controlled to ensure a balance between energy dissipation due to acceleration and the Age of Information.  
This paper also examines important issues related to each of the aforementioned components. For example, concerning UAV trajectory, the issues include UAV flight time, scheduling of ground nodes, QoS metrics, and service time allocation. In scheduling, the discussed issues include scheduling policies which involves scheduling packets, and scheduling IoTDs. In addition, UAV cooperation, energy level, flight time, velocity control, and charging optimization are some of the issues addressed in regard to energy consumption.  
Similarly, major contributions made by researchers include, the study of UAV trajectory while incorporating the waiting time of (update) packets, node visiting order, backscatter communication, UAV battery recharge, energy harvesting, full duplex communication, various channel conditions, UAV heterogeneity, IoTD device selection and joint data transmission, and packet allocation.  
In summary, planning for efficient UAV- aided data collection for WSN/IoT networks with minimal AoI must consider various factors, including adopting suitable optimization algorithms. This study also addressed the numerous challenges in AoI minimization and recommended a few future research directions. Finally, other topics, such as security, covert communication problems, and routing protocols, were considered out of this work's scope and thus shall be considered in future work.  
Conceptualization, O.A.A. and R.N.; investigation, O.A.A.; resources, O.A.A., C.J. and U.A.B.; writing--original draft preparation, O.A.A.; writing--review and editing, R.N., R.A.R.M.; methodology, O.A.A. and U.A.B.; visualization, O.A.A., U.A.B. and C.J.; supervision, R.N. and M.O.; project administration, O.A.A.; funding acquisition, R.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Kebangsaan Malaysia, under Dana Impak Perdana 2.0, (Ref: DIP-2022-020), Project Title: 'Air-to-Ground Wireless Communications with Multiple Objective Paths Planning for Swarm Aerial Drone Base Stations'.  
Not applicable.  
The authors would like to thank everyone who provided valuable suggestions and support to improve the content, quality, and presentation of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Ant colony \\ AoI & Age of Information \\ AP & Affinity Propagation \\ A2G & Air-to-ground \\ BS & Base station \\ CH & Cluster head \\ CP & Clustering point \\ CSI & Channel state information \\ DC & Data center \\ DDPG & Deep Deterministic Policy Gradient \\ DP & Dynamic programming \\ DQN & Deep Q network \\ DRL & Deep reinforcement learning \\ EH & Energy harvesting \\ ESA & Expected sum AoI \\ FANETs & Flying Ad hoc Networks \\ GA & Genetic algorithm \\ GTSP & Generalized Traveling Salesman Problem \\ \end{tabular}