## 5 Conclusions  
In this paper, an aerial-based data collection system has been developed, which was based on the integration of the IoT, LoRaWAN(r), and UAVs. The developed system consists of three main parts: (i) sensing nodes which are distributed throughout the farm which can be any kind of sensor node (beacon and/or unknown), (ii) a LoRaWAN(r)-based communication network, which collects data from sensors and conveys them to the cloud, and (iii) a path planning optimization technique, which optimizes the drone path for effectively sweeping the farm and collecting data from all established sensors. Based on the results, LoRaWAN(r) is a reliable system capable of establishing a WSN over a large-scale farm. Furthermore, by integrating LoRaWAN(r) into a drone, greater coverage can be achieved and it has robust performance with a maximum drone speed of 95 km/h. It was also shown that by increasing the flight altitude (e.g., 100 m-150 m), better LoRa coverage can be provided. Finally, the utilization of drones for aerial data collection not only can improve the availability of data, but can also accelerate farm monitoring procedures and enhance the productivity of farms, for example, by increasing production and optimizing the use of resources. In this regard, we believe that the developed system is a successful development in drone-based aerial data collection systems that provides a solution to tackle critical problems in farm management, aerial livestock monitoring, and collecting data from various IoT sensors.  
Conceptualization, M.B., A.B.M.N., H.A.H.A., and R.N.; methodology, M.B., A.B.M.N., H.A.H.A., and R.N.; software, M.B., A.B.M.N., and H.A.H.A.; validation, M.B., A.B.M.N., and R.N.; formal analysis, M.B. and A.B.M.N.; investigation, M.B., A.B.M.N., and R.N.; data curation, A.B.M.N. and M.A.Z.; writing--original draft preparation, M.B. and A.B.M.N.; writing--review and editing, M.B., H.A.H.A., R.N., and N.F.A.; visualization, M.B. and M.A.Z.; supervision, R.N. and N.F.A.; project administration, R.N. and N.F.A.; funding acquisition, R.N. and N.F.A. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flight Mission Mode** & **Total Path** & **No. of Stops/Battery** & **Continuous** \\  & **Length (km)** & **Replacements** & **Operation Time** \\ \hline Square & 212 & 2 & 02:15 \\ \hline Circular & 175 & 1 & 01:50 \\ \hline PSO-based & 79 & 0 & 00:50 \\ \hline ESPO-based & 49 & 0 & 00:40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of total traveling distance, operating hours, and the number of charging stops needed for different types of flight mission planning on the considered livestock farm.  
**Funding:** We acknowledge the financial support from CRIM, Universiti Kebangsaan Malaysia, under the Dana Pecutan Penerbitan, and Air Force Office of Scientific Research (AFOSR), under the grant ref number: FA2386-20-1-4045 for the open access fee payment.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank the Aerodyne Group members, especially [PERSON] and [PERSON], and the rest of the technical and management team for their support, technical assistance, guidance, and drone operations for performing the measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.