@article{Spyridis2021,

title = {Modelling and simulation of a new cooperative algorithm for UAV swarm coordination in mobile RF target tracking},

author = { Y. Spyridis, T. Lagkas, P. Sarigiannidis and J. Zhang},

url = {https://www.researchgate.net/publication/346563683_Modelling_and_simulation_of_a_new_cooperative_algorithm_for_UAV_swarm_coordination_in_mobile_RF_target_tracking},

doi = {10.1016/j.simpat.2020.102232},

year  = {2021},

date = {2021-02-01},

journal = {Simulation Modelling Practice and Theory},

volume = {107},

pages = {102232},

publisher = {Elsevier BV},

abstract = {Recent advancements in sensor technology have allowed unmanned aerial vehicles (UAVs) to function as sensing devices in cooperative aerial communication networks, offering novel solutions in applications of environment inspection, disaster detection and search and rescue operations. Towards this trend, the efficient deployment and coordination of UAV networks is of vital importance. Generating controlled experimental conditions to implement and evaluate different approaches in this context can be impractical and costly and thus the solution of modelling is often preferred. This paper introduces a tracking model in which multirotor UAVs, equipped with received signal strength indicator (RSSI) sensors, are organized in a swarm and cooperate to approximate and trail a moving target. The proposed algorithm is able to offer autonomous tracking in large scale environments, by utilising just the strength of the communication signal emitted by a radio frequency transmitter carried by the target. A model of the proposed algorithm is created, and its performance is thoroughly evaluated in a specialized simulator developed in the Processing IDE. Results demonstrate the increased tracking efficiency of the proposed solution compared to a trilateration method. © 2020 Elsevier Ltd},

keywords = {Mobile target tracking, Modelling, RSSI, Simulation, UAV swarm, unmanned aerial vehicles, wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}

@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}

}