A Secure Real-time Multimedia Streaming through Robust and Lightweight AES Encryption in UAV Networks for Operational Scenarios in Military Domain

multimodal data encryption and decryption for security applications in protected environments like espionage, situational awareness, monitoring, and counter-UAV. Data is captured from drones equipped with microphone arrays and cameras. This is performed by exploiting acoustic event analysis, video tracking, and recognition, performed on a ground station. All the communications are delivered in a secure data channel. Integrity and secrecy of the sensitive data acquired by drones must be guaranteed until the data is delivered in real-time from UAVs to the destination node. A possible data exploit may cause critical problems if the data is intercepted by malicious attackers. Being the drones equipped with low energy consuming devices with low computational power, like single-board-computers, a real-time lightweight application-level AES encryption, in addition to the MAC encryption of the wireless communication channel, has been considered. In the experiment, the encryption and decryption process has been optimized, even under adverse transmission conditions ensuring continuous data encryption even if some packets are lost or the connection is repeatedly dropped and reestablished

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ