Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection

Abstract

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.