Spyridis Yannis, Lagkas Thomas, Sarigiannidis Panagiotis, Argyriou Vasileios, Sarigiannidis Antonios, Eleftherakis George, Zhang Jie
Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S1 3JD, UK.
Department of Computer Science, International Hellenic University, 654 04 Kavala Campus, Greece.
Sensors (Basel). 2021 Jun 7;21(11):3936. doi: 10.3390/s21113936.
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.
有人提出将充当飞行锚节点的无人机用于协助地面物联网（IoT）传感器的定位，并在即将到来的6G网络环境中提供中继服务。本文考虑了使用一组配备接收信号强度指示器（RSSI）传感器的无人机来追踪位置未知的移动物联网设备这一目标。无人机利用目标射频（RF）信号功率的测量值尽可能快地接近目标。一个深度学习模型基于图卷积网络（GCN）架构，定期在无人机网络中进行聚类，该架构利用了RSSI和无人机位置的信息。使用启发式方法在每个时刻动态确定聚类数量，并通过优化RSSI损失函数来确定分区。所提出的算法保留了更有效地接近RF源的聚类，移除其余返回基地的无人机。仿真实验表明，与先前的确定性方法相比，该方法在到达目标所需时间和无人机覆盖的总距离方面有所改进。
