# Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality
## 1 Introduction
The clustering routing protocol is another dynamic network protocol where variable clusters of sensor nodes only communicate with specifically selected head nodes [28]. Here, only the head nodes of the presently configured clusters directly send their data to the sink node in a passing UAV, and the changing position of the UAV is accommodated by reconfiguring the individual clusters of the sensor nodes in the WSN and reselecting the respective head nodes after each data transmission in order to avoid overtaxing the battery resources of the selected head nodes [29; 30; 31; 32; 33]. Research focused on the development of UAV-WSN systems based on the clustering routing protocol has featured a number of methods for configuring sensor node clusters, such as particle swarm optimization (POS) or the weighted K-means clustering algorithm [34; 35; 36; 37; 38], and the head nodes are selected by other methods, such as the low-energy adaptive clustering hierarchy (LEACH) protocol or the hybrid energy-efficient distributed (HEED) clustering approach [39; 40; 41]. Finally, the optimal flight route of the UAV over the head nodes is typically planned using a number of approaches, such as the traveling salesman problem (TSP) or simulated annealing (SA) [29; 30; 42; 43; 44; 45; 46]. This method has also been employed to good effect in a number of UAV-WSN applications. For example, [PERSON] et al. [37] deployed a UAV-WSN system based on the clustering routing protocol over a large area for the purpose of environmental monitoring. The weighted K-means algorithm was employed for sensor node clustering, the LEACH protocol was employed to select the head node of each cluster, and SA was employed to plan the flight route. According to its operational mode, the clustering routing protocol has a strong capability for balancing the energy consumption of sensor nodes in a WSN. However, like the flat routing protocol, the streamlined flight route of the UAV, flying over the head nodes only, reduces the coverage of UAV imaging. Moreover, the changing head node locations of the clustering routing protocol induces an additional disadvantage, in that the images collected during different flight missions cannot be readily compared due the corresponding changes in the flight route. Therefore, the clustering routing protocol is best suited for conditions where the monitored area and the density of sensor nodes are both large. The common disadvantage of the data transmission protocols discussed above for use in UAV-WSN system applications is that they do not provide sufficient flexibility to UAVs for the collection of remote sensing data due to the constraints associated with the collection of ground sensor data [47; 48; 49; 50]. Presently, this necessitates that the UAV must fly over a study area twice, where one flight path is planned for visiting each communication node in the WSN (i.e., the sensor nodes, route nodes, or head nodes, depending on the protocol in use) and another is planned for collecting high-resolution remote sensing data. However, the combined collection of both ground sensor and remote sensing data over a single-flight path by an integrated UAV-WSN system would greatly reduce the flight time and energy usage of UAVs during farmland quality monitoring operations, while also enabling the simultaneous collection of high-resolution remote sensing data. Therefore, the facile and flexible integration of ground sensor and high-resolution remote sensing data in a UAV-WSN system requires the development of a new data transmission protocol specifically designed for this purpose. As such, the designed protocol must accommodate the flexible routing of UAVs and adhere to the principles of aerial photogrammetry while enabling the efficient collection of ground sensor data. These issues are addressed in this work by developing an integrated UAV-WSN system based on a newly proposed data transmission optimization routing protocol (DTORP). In the following, Section 2 presents the design of the improved UAV-WSN system. Section 3 presents the DTORP.