# Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality

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## Abstract

**:**

## 1. Introduction

## 2. Improved UAV-WSN System Design

## 3. Data Transmission Optimization Routing Protocol

#### 3.1. Efficient Communication Length Computation

_{h}, which is based on the known transmission power of the node and the altitude h of the UAV, which can be determined by the spatial resolution of the remote sensing image acquired according to the camera parameters of the UAV. However, the length of the UAV flight path through the efficient communication range of each sensor node will be different because the planned flight path of the UAV is independent of the sensor node locations. This length will determine the time period allowed for efficient communication between the UAV and a sensor node according to the known speed, v, of the UAV, and therefore determines the maximum quantity of data that can be efficiently transmitted from a sensor node to the UAV. Therefore, the selection of the communication node within a cluster must be conducted to maximize the quantity of data that can be efficiently transmitted. Accordingly, an efficient communication length algorithm was developed to compute the maximum amount of data that can be transmitted to the UAV by each sensor node in a cluster, based on the efficient communication length and v.

_{i}

_{,j}, where i = 1, 2, …, m and j = 1, 2, …, n. It is easily determined that a line segment, I, cannot pass through the efficient communication area of sensor node j unless d

_{i}

_{,j}< r

_{h}. Therefore, only line segments for which d

_{i}

_{,j}< r

_{h}are considered for further analysis.

_{h}and the distances from sensor node j to the first and second turning points of line segment i, which are herein denoted as L

_{i}

_{−1,j}and L

_{i}

_{,j}, respectively. Here, the six cases are divided according to whether the two turning points of a line segment are on different sides of the sensor node or on the same side and according to the values of L

_{i}

_{–1,j}and L

_{i}

_{,j}, relative to r

_{h}. We also must note that the line segments in Figure 1 have all been rendered with horizontal orientations for simplicity, although these line segments can travel in any arbitrary direction. Defining the length of line segment i in the efficient communication range of sensor node j as s

_{i}

_{,j}, its value can be determined for the different and same side cases as follows: If both L

_{i}

_{−1,j}and L

_{i}

_{,j}are greater than r

_{h}, then s

_{i}

_{,j}is defined as follows:

_{i}

_{−1,j}or L

_{i}

_{,j}is greater than r

_{h}and the other is less than r

_{h}, then s

_{i}

_{,j}is defined as follows:

_{i}

_{−1,j}and L

_{i}

_{,j}are less than r

_{h}, then s

_{i}

_{,j}is defined as follows:

_{j}, that can be efficiently transmitted to the UAV by sensor node j can be calculated based on the data transmission rate, T, of the node as follows:

#### 3.2. Data Transmission Scheduling

#### 3.2.1. Short Distance Scheduling: Unit Data Polling Scheduling and the Maximum Remaining Energy Routing Algorithm

Algorithm 1. Unit Data Polling Scheduling and the Maximum Remaining Energy Routing Algorithm. | |

1: | Initialize: The complete list N of sensor nodes and the number of sensor nodes n, list D of demand nodes and the number of demand nodes n_{dn}, list S of support nodes and the number of support nodes n_{sn}, and the list of data sending DS and data receiving DR for all sensor nodes. |

2: | While n_{dn} > 0, n_{sn} > 0 and support nodes exist within a one- or two-jump distance from the demand nodes, do |

3: | For each D_{i}, i ∈ n_{dn} and S_{j}, j ∈ n_{sn}, do |

4: | If D_{i} and S_{j} lie within a single jump distance, then |

5: | Record the route and accumulate DS_{i} and DR_{j} |

6: | Update the quantity of data transmitted for D_{i} and S_{j} |

7: | End if |

8: | If D_{i} and S_{j} lie within a two-jump distance, then |

9: | Find all routes from D_{i} to S_{j}, and select the route N_{k} that has the greatest remaining energy |

10: | Record the route and accumulate DS_{i}, DR_{j}, DS_{k}, and DR_{k} |

11: | Update the quantity of data transmitted for D_{i} and S_{j} |

12: | End if |

13: | End for |

14: | End while |

15: | Compute the energy consumption using DS and DR. |

16: | Update the remaining energy for all nodes. |

#### 3.2.2. Medium Distance Scheduling: Maximum Data Greedy Scheduling and the Maximum Remaining Energy Routing Algorithm

Algorithm 2. Maximum Data Greedy Scheduling and the Maximum Remaining Energy Routing Algorithm. | |

1: | Initialize: The complete list N of sensor nodes and the number of sensor nodes n, the list D of demand nodes, the quantity of data DD that must be transmitted, and the number of demand nodes n_{dn}, list S of support nodes, the quantity of data SD available for support, and the number of support nodes n_{sn}. |

2: | Sort D according to DD and S according to SD in descending order |

3: | If n_{dn} > 0, n_{sn} > 0 and support nodes exist within a three- or four-jump distance, then |

4: | For each D_{i}, I ∈ n_{dn} and S_{j}, j ∈ n_{sn}, do |

5: | If D_{i} and S_{j} lie within three or four jumps distance, then |

6: | If DD_{i} ≥ SD_{j} then |

7: | DD_{i} = DD_{i} − SD_{j} |

8: | SD_{j} = 0 |

9: | Else |

10: | DD_{i} = 0 |

11: | SD_{j} = SD_{j} − DD_{i} |

12: | End if |

13: | Find all routes from D_{i} to S_{j} |

14: | Select the route having the greatest remaining energy |

15: | Record the route and the amount of data transmitted |

16: | End if |

17: | End for |

18: | End if |

#### 3.2.3. Long-Distance Scheduling: Maximum Data Greedy Scheduling and the Diffusion Route Finding Algorithm

Algorithm 3. Maximum Data Greedy Scheduling and the Diffusion Route Finding Algorithm. | |

1: | Initialize: The complete list N of sensor nodes and the number of sensor nodes n, the list D of demand nodes, the quantity of data DD that must be transmitted, and the number of demand nodes n_{dn}, the list S of support nodes, the quantity of data transmission SD available for support, and the number of support nodes n_{sn}. |

2: | While n_{dn} > 0 and n_{sn} > 0, do |

3: | Sort D according to DD and S according to SD in descending order |

4: | If DD_{l} ≥ SD_{l}, then |

5: | DD_{l} = DD_{l} − SD_{l} |

6: | SD_{l} = 0 |

7: | n_{sn} = n_{sn} − 1 |

8: | Else |

9: | DD_{l} = 0 |

10: | SD_{l} = SD_{l} − DD_{l} |

11: | n_{dn} = n_{dn} − 1 |

12: | End if |

13: | While S_{l} has not been found, then |

14: | For all nodes lying one more jump away from D_{l}, do |

15: | If S_{l} is found in these nodes, then |

16: | Record the route |

17: | Break |

18: | End if |

19: | End for |

20: | End while |

21: | End while |

## 4. Simulations

#### 4.1. Simulation Setup

#### 4.2. Performance of the Improved UAV-WSN System

#### 4.3. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Six conditions where flight line segment i may pass through the efficient communication range of sensor node j, which depend on the efficient communication radius r

_{h}and the distances from the sensor node to the first and second turning points of the line segment (L

_{i}

_{−1,j}and L

_{i}

_{,j}).

**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.

**Figure 4.**Maximum revisit period of the UAV-WSN systems with respect to the spatial image resolution.

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**MDPI and ACS Style**

Zhang, F.; Wang, G.; Hu, Y.; Chen, L.; Zhu, A.-x.
Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality. *Sensors* **2020**, *20*, 336.
https://doi.org/10.3390/s20020336

**AMA Style**

Zhang F, Wang G, Hu Y, Chen L, Zhu A-x.
Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality. *Sensors*. 2020; 20(2):336.
https://doi.org/10.3390/s20020336

**Chicago/Turabian Style**

Zhang, Feiyang, Guangxing Wang, Yueming Hu, Liancheng Chen, and A-xing Zhu.
2020. "Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality" *Sensors* 20, no. 2: 336.
https://doi.org/10.3390/s20020336