@article{Spyridis2021b,

title = {Towards 6g iot: Tracing mobile sensor nodes with deep learning clustering in uav networks},

author = { Y. Spyridis and T. Lagkas and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and G. Eleftherakis and J. Zhang},

url = {https://www.researchgate.net/publication/352197709_Towards_6G_IoT_Tracing_Mobile_Sensor_Nodes_with_Deep_Learning_Clustering_in_UAV_Networks},

doi = {10.3390/s21113936},

year  = {2021},

date = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {11},

abstract = {Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {6G, Deep Learning, Graph convolutional network, IoT, RSSI, Sensor tracking, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}