Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using radio frequency (RF) signals and through 5G networks. Recently, few studies have been proposed to use deep neural networks (DNNs) on RF signals for identifying drones and detecting their flight modes, such as off, on and connected, flying, hovering, and video recording. However, transmitting RF signals between drones and 5G nodes needs to be secure and decentralized; in addition, the performance of identification and detection needs to be more accurate. In this article, we introduce a framework that combines a blockchain with a deep recurrent neural network (DRNN) and edge computing for 5G-enabled drone identification and flight mode detection. In the proposed framework, raw RF signals of different drones under several flight modes are remotely sensed and collected on a cloud server to train a DRNN model and then distribute the trained model on edge devices for detecting drones and their flight modes. Blockchain is used in the proposed framework for data integrity and securing data transmission. The DRNN model is evaluated on a public dataset, called DroneRF. Experimental evaluation results show that the DRNN model of the proposed framework can detect drones and their flight modes from real RF signals with high accuracy as compared to recent related work.

中文翻译：

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具有边缘计算的深度学习和区块链，用于支持 5G 的无人机识别和飞行模式检测


如今，无人机不仅被部署用于国防和军事设施，而且还广泛应用于自然灾害监测、土壤和作物分析、道路和交通监控以及消费品交付等许多应用中。一些信息，例如无人机识别和飞行模式，可以传输给其他无人机。这些信息可以通过使用射频 (RF) 信号和 5G 网络在无人机之间共享。最近，很少有研究提出在射频信号上使用深度神经网络（DNN）来识别无人机并检测其飞行模式，例如关闭、开启和连接、飞行、悬停和视频录制。然而，无人机和 5G 节点之间传输射频信号需要安全且去中心化；此外，识别和检测的性能需要更加准确。在本文中，我们介绍了一个将区块链与深度循环神经网络 (DRNN) 和边缘计算相结合的框架，用于支持 5G 的无人机识别和飞行模式检测。在所提出的框架中，在云服务器上遥感并收集不同无人机在多种飞行模式下的原始射频信号，以训练 DRNN 模型，然后将训练后的模型分发到边缘设备上以检测无人机及其飞行模式。所提出的框架中使用区块链来确保数据完整性和保护数据传输。 DRNN 模型在名为 DroneRF 的公共数据集上进行评估。实验评估结果表明，与最近的相关工作相比，所提出框架的 DRNN 模型可以从真实射频信号中高精度地检测无人机及其飞行模式。