# Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

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## Abstract

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## 1. Introduction

- We introduced a graph representation of the UAV network that enabled the utilisation of graph convolutional network (GCN) architectures;
- The clusterisation was performed in the network at regular intervals based on the RSSI and the current locations of the UAVs, with a dynamic number of clusters at each phase;
- We utilised the optimisation of an RSSI deep learning loss function to determine the final clusters;
- The introduced method allowed the UAVs to trace the target sensor node without explicit knowledge of its location or the use of distance estimates.

## 2. Related Work

## 3. Methodology

#### 3.1. Tracing Algorithm

#### 3.2. Deep Learning Clustering for the Determination of UAV Groups

#### 3.2.1. Graph Representation of the Network

**I**is the identity matrix and

**D**and

**A**are the degree and adjacency matrices, respectively. In addition, a new matrix was created, holding information about each UAV pertaining to the location coordinates and the RSSI from the target. Using the above matrices, the final GCN model was constructed and trained to provide the optimal clusters.

#### 3.2.2. Deep Learning Loss Formulation

**Y**is the output matrix from the neural network and

**r**is a vector containing the RSSI values. By optimising this loss function, the deep learning model determined the partitions and where each node belonged. To ensure that the number of UAVs was balanced in each cluster, an additional loss function was used:

#### 3.2.3. Determining Optimal Number of Clusters

## 4. Simulation Setup

- In the first scenario, the speed of the UAVs was set to 40 km/h, and the target sensor was placed in a random position, 2 km away from the UAV deployment location;
- In the second scenario, the target was positioned 3 km away from the deployment location, and the UAVs’ velocity was set to 50 km/h.

## 5. Evaluation

#### 5.1. Results

#### 5.2. Ablation Study

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Structure of the deep learning network. The input layer’s dimensions are n × 3, where n is the number of UAVs in the UAV network. The output layer has k × 1 dimensions, with k being the number of clusters.

**Figure 3.**Example of a plot demonstrating the elbow method. The distortion score is computed as the sum of squared distances from each point to its assigned centre. In this case, the number of selected clusters should be 4.

**Figure 4.**Simulator instances at different timestamps of the simulation. The red square represents the target sensor, and the dots indicate the UAVs. The left subfigure depicts the swarm 90 s after initiating the algorithm. The middle subfigure corresponds to 120 s, while the last subfigure demonstrates the tracing process after the threshold for terminating the DL procedure has been reached.

**Figure 5.**Time required for the first UAV to reach the target sensor when placed at a distance of 2 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 6.**Time required for the first UAV to reach the target sensor when placed at a distance of 3 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 7.**Average distance covered per UAV when the target sensor is placed at a distance of 2 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 8.**Total distance covered by all UAVs when the target sensor is placed at a distance of 2 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 9.**Average distance covered per UAV when the target sensor is placed at a distance of 3 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 10.**Total distance covered by all UAVs when the target sensor is placed at a distance of 3 km, versus the standard deviation $\sigma $ of the additive noise. Comparison of the proposed method at clustering termination thresholds of 20, 15 and 10 dB and the previous approach.

**Figure 11.**Time required for the first UAV to reach the target sensor when placed at a distance of 2 km, versus the standard deviation $\sigma $ of the additive noise. Results at clustering termination thresholds of 20 dB, with and without using the elbow method to determine the number of clusters.

**Table 1.**Parameters of the simulation for each examined scenario. The two scenarios are differentiated in terms of the UAVs’ velocity and the target’s initial distance.

Parameter | Scenario 1 | Scenario 2 |
---|---|---|

Number of UAVs | 45 | 45 |

Cluster update interval | 30 s | 30 s |

UAV altitude | 100 m | 100 m |

UAV velocity | 40 km/h | 50 km/h |

Target velocity | 5 km/h | 5 km/h |

Target distance | 2 km | 3 km |

Target TX power | 10 mW | 10 mW |

Target TX antenna gain | 2 dBi | 2 dBi |

UAV RX antenna gain | 2 dBi | 2 dBi |

Signal frequency | 2400 MHz | 2400 MHz |

Total duration | 1000 s | 1000 s |

**Table 2.**Average results for all values of the standard deviation $\sigma $ of the additive noise for the DL method and the previous approach, at different thresholds. The target is placed at a 2 km distance. Time is measured in seconds and distance in meters.

Threshold | Proposed | Spyridis et al. [17] | ||||
---|---|---|---|---|---|---|

Time | Average Distance | Total Distance | Time | Average Distance | Total Distance | |

10 | 260 | 8661 | 249,032 | 332 | 9201 | 414,214 |

15 | 268 | 8351 | 126,716 | 295 | 9270 | 417,322 |

20 | 251 | 8222 | 99,191 | 268 | 9345 | 420,705 |

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**MDPI and ACS Style**

Spyridis, Y.; Lagkas, T.; Sarigiannidis, P.; Argyriou, V.; Sarigiannidis, A.; Eleftherakis, G.; Zhang, J. Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks. *Sensors* **2021**, *21*, 3936.
https://doi.org/10.3390/s21113936

**AMA Style**

Spyridis Y, Lagkas T, Sarigiannidis P, Argyriou V, Sarigiannidis A, Eleftherakis G, Zhang J. Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks. *Sensors*. 2021; 21(11):3936.
https://doi.org/10.3390/s21113936

**Chicago/Turabian Style**

Spyridis, Yannis, Thomas Lagkas, Panagiotis Sarigiannidis, Vasileios Argyriou, Antonios Sarigiannidis, George Eleftherakis, and Jie Zhang. 2021. "Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks" *Sensors* 21, no. 11: 3936.
https://doi.org/10.3390/s21113936