Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The spatiotemporal The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models.

Ambient computing creates a digital environment where a variety of technologies, including hardware, software, and even machine learning, are integrated into most useful and autonomous digital device that we want to use right here right now. The main obstacle for this computing is a barrier of vendor-specific details, programming interfaces, and access procedure even though all the things come to have network interfaces and interact with each other through the Internet. However, this configuration defies the expectation that ambient computing can reduce the demand for human attention. In this article, we propose Ambient Virtio, a device virtualization technology that virtualizes devices with the virtio framework. This allows devices in close proximity to users to be accessed without human intervention and integrated into metadevices that users want to create and access on the fly. We newly defined a virtio-based backend device named virtio-ambient and also constructed an ambient device architecture for virtual machines to read and write the remote device only with built-in system calls. We completely implemented the system and performed comparative evaluation with the existing application-level data networking system.

In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios.

Unmanned aerial vehicles (UAVs), require low-latency control for accurate mission execution and automation in vehicular or industrial applications. To facilitate clock synchronization in ensuring low-latency performance for UAVs, we propose the Mobile Edge Clock Server (MECS), a software-based timestamping mechanism tailored for UAVs, by modifying the poll process in Network Time Protocol (NTP) and merging GPS signal resources. Through the application of the Raw-offset filter, we also propose precise MECS (P-MECS), aimed at achieving high precision by considering the characteristics of GPS modules and conducting an in-depth analysis of signals. The MECS and P-MECS resulted in improving clock accuracy and reducing inter-UAV synchronization error to $\frac {1}{129}$ (99.2%) and $\frac {1}{19,402}$ (99.99%), respectively, compared to NTP on WLAN, and to $\frac {1}{32}$ (96.9%) and $\frac {1}{4,762}$ (99.98%), respectively, compared to Chrony on WLAN.