## 6 Conclusions  
In this paper, we proposed optimization schemes to solve the UAV data collection problems in remote areas without public networks. To reduce the UAV flight distance and time, the optimization schemes consist of three aspects. Specifically, the ILEACH algorithm was proposed, compared with the typical clustering algorithms (LEACH, TEEN, PEGASIS), it is aware of the distance from the cluster heads to UAV take-off point, while considering the factors of remaining energy, number of nodes covered, and average distance of neighbor nodes. Then, the TGA algorithm was presented to reduce the UAV planned path, which introduces the TLBO and local search optimization algorithms, and has a better convergence rate and path solution than those of GA. Finally, we proposed the DCMDR strategy, using LoRa 2.4 GHz technology with dual channels, and the data rate between UAV and cluster heads can be adaptively adjusted according to the distance and link quality. Compared with the current Sub-G LoRa technologies, it meets the requirement of high data rate while taking into account long-range communication.  
Furthermore, we tested the thresholds of SNR, RSSI, and Distance for each DR through field tests in the Lingang area of Shanghai. When using the DCMDR scheme, with a flight speed of 4 m/s, up to 100 Mbits data in cluster head can be transmitted without UAV hovering and reducing speed.  
Finally, we carried out simulations and experiments to verify the advantages of ILEACH and TGA-MOVING-DCMDR. In simulations with three classical datasets (R201, C201, and RC201), when the number of iterations was 200, the optimal solution and average solution of TGA for flight path were about two-thirds lower than those of GA. Compared with ILEACH-TGA, ILEACH-TGA can reduce the planned path distance by 30%. TGA-MOVING-DCMDR uses the minimum data gathering time regardless of the amount of data stored in the cluster heads. In small amount of data cases, it can start gathering data from a long distance with the lower DRs. In large amount of data cases, it can adjust the flight speed to complete the data gathering and reduce unnecessary flight distance. The experiments further verified the better performance of the optimization schemes, which can be applied to the UAV data collection problem well in remote areas without infrastructure. When using our schemes, with a flight speed of 12 m/s, up to 38 Mbits data in cluster head can be transmitted without UAV hovering and reducing speed.  
For future work, the multi-UAV system will be considered for large area data collection. We will conduct more experiments with more end-nodes, algorithm complexity and energy efficiency will be further analyzed, to further evaluate the performance of the optimization scheme. The fixed wing drone, which has a much longer endurance and faster speed, will be studied to cover much larger areas.  
Conceptualization, Z.Z. and S.C.; methodology, Z.Z. and C.Z.; software, C.Z.; validation, Z.Z., C.Z. and L.S.; formal analysis, C.Z.; investigation, L.S.; resources, S.C.; data curation, L.S.; writing--original draft preparation, C.Z.; writing--review and editing, Z.Z.; visualization, L.S.; supervision, Z.Z.; project administration, S.C.; funding acquisition, S.C. and L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported the program for Shanghai Collaborative Innovation Center for Cultivating Elite Breeds and Green-culture of Aquaculture animals (Project No. 2021-KJ-02-12), Science and Technology on Near-Surface Detection Laboratory (Project No. 6142414200410).  
**Acknowledgments:** We thank the anonymous reviewers for their valuable comments and suggestions for improving our manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.