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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具有边缘计算功能的深度学习和区块链，可实现启用5G的无人机识别和飞行模式检测

如今，无人机不仅用于国防和军事设施，而且还广泛用于许多应用中，例如自然灾害监测，土壤和农作物分析，道路和交通监控以及消费产品交付。某些信息（例如无人机识别和飞行模式）可以传输到其他无人机。可以使用射频（RF）信号并通过5G网络在无人机之间共享此信息。最近，很少有人提出在RF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的信息，例如关闭，开启和连接，飞行，悬停和视频记录。但是，无人机和5G节点之间的RF信号传输必须安全且分散。此外，识别和检测的性能需要更准确。在本文中，我们介绍了一个框架，该框架将区块链与深度递归神经网络（DRNN）和边缘计算相结合，用于启用5G的无人机识别和飞行模式检测。在提出的框架中，在云服务器上遥感并收集了几种飞行模式下不同无人机的原始RF信号，以训练DRNN模型，然后将训练后的模型分布在边缘设备上以检测无人机及其飞行模式。提出的框架中使用了区块链，以确保数据完整性和保护数据传输。DRNN模型在称为DroneRF的公共数据集上进行评估。