# Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality
## 3 Data Transmission Optimization Routing Protocol
### Data Transmission Scheduling
These results demonstrate that the energy efficiency value of data transmission obtained using the DTORP was much smaller than that obtained using the LEACH protocol. Here, the DTORP consumed less than one-fifth of the energy consumed by the conventional LEACH protocol when the pixel size of the image was not more than \(3.5\times 3.5\) cm. Moreover, the energy consumption of data transmission obtained by the DTORP for a spatial image resolution of \(4\times 4\) cm remains two-fifths of that consumed by the LEACH protocol. The benefits of the three-step data scheduling process employed by the DTORP for reducing energy consumption during data transmission are particularly evident as the spatial image resolution becomes increasingly coarse and the data transmission load increases. Here, a coarse spatial image resolution is obtained when the UAV is at a high altitude, and a relatively large number of sensor nodes must transmit their data to other sensor nodes to accommodate the relatively

Figure 2: Total volume of data transmitted by the 10 sensor nodes in the wireless sensor network (WSN) clusters, with respect to the spatial image resolution of the unmanned aerial vehicle (UAV). LEACH: Low-energy adaptive clustering hierarchy. DTORP: Data transmission optimization routing protocol. small effective communication ranges involved. Accordingly, all the steps of the DTORP can be fully applied to optimize the data transmission process and reduce the corresponding energy consumption. The relationship between the maximum revisit period of the UAV-WSN systems and the spatial resolution of UAV images is given in Figure 4. We note that the maximum revisit period of the proposed UAV-WSN system was, in fact, greater than one year for pixel sizes less than \(2.5\times 2.5\) cm, and it decreased continuously as the spatial resolution became increasingly coarse. Here, in the Figure 4, the max value of the y-axis was limited to 365 days to clearly illustrate this decreasing trend. The conventional UAV-WSN system exhibited a similar decreasing trend for the maximum revisit period, except with much smaller values than the proposed system, which is mainly the result of the much smaller total volume of transmitted data (Figure 2). This indicates that the conventional UAV-WSN system requires a much higher frequency of revisits to collect the data from the sensor nodes than the proposed system for a given study area and spatial image resolution. Overall, the one-year maximum revisit period obtained by the improved system when the spatial image resolution is finer than \(2\times 2\) cm indicates that this consideration is not a significant issue when revisiting is scheduled for the improved UAV-WSN system. Figure 4: Maximum revisit period of the UAV-WSN systems with respect to the spatial image resolution. Figure 3: Energy efficiency of the UAV-WSN systems with respect to the spatial image resolution. The above results obtained for the three performance indicators demonstrate that the DTORP can greatly improve the data transmission efficiency and reduce the energy consumption of the improved UAV-WSN system relative to those of a conventional UAV-WSN system employing the widely used LEACH protocol. These results, when taken together, demonstrate that the improved UAV-WSN system can be expected have a longer lifetime and be adaptive for long-term monitoring applications. ### Discussion

The improved UAV-WSN system employing the DTORP proposed in this study has demonstrated its potential for using sensor nodes on the ground to monitor changes in the environment directly at sampled locations while simultaneously using drones to monitor the entire region. As such, in addition to farmland quality monitoring, the proposed system is equally applicable to many different activities that seek to utilize UAV-based remote sensing images and WSN-based ground sensing for monitoring data simultaneously, such as environmental monitoring, pollution control, flood condition monitoring, and emergency response.