Capacity and Delay of Unmanned Aerial Vehicle Networks with Mobility

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is O(n/log n) times higher than that of the multi-hop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.Comment: 14 pages, 10 figures, IEEE Internet of Things Journa

Flying Drones Beyond Visual Line of Sight Using 4G LTE: Issues and Concerns

The purpose of this paper is to address the extent in which 4G LTE can be used for air traffic management of small Unmanned Air Vehicles (sUAVs) and the limitations and enhancements that may be necessary. We provide a brief overview of the communications aspects of the Unmanned Aerial System (UAS) Traffic Management Project followed by the evolving trends in air traffic management including beyond visual line of sight (BVLOS) operations concepts and current BVLOS operational systems. Issues and Concerns are addressed including the rapidly evolving global regulations and the resulting communications requirements as well LTE downlink and uplink interference at altitude and how that interference affects command and control reliability as well as application data capabilities and mobility performance

Developing 3D Virtual Safety Risk Terrain for UAS Operations in Complex Urban Environments

Unmanned Aerial Systems (UAS), an integral part of the Advanced Air Mobility (AAM) vision, are capable of performing a wide spectrum of tasks in urban environments. The societal integration of UAS is a pivotal challenge, as these systems must operate harmoniously within the constraints imposed by regulations and societal concerns. In complex urban environments, UAS safety has been a perennial obstacle to their large-scale deployment. To mitigate UAS safety risk and facilitate risk-aware UAS operations planning, we propose a novel concept called \textit{3D virtual risk terrain}. This concept converts public risk constraints in an urban environment into 3D exclusion zones that UAS operations should avoid to adequately reduce risk to Entities of Value (EoV). To implement the 3D virtual risk terrain, we develop a conditional probability framework that comprehensively integrates most existing basic models for UAS ground risk. To demonstrate the concept, we build risk terrains on a Chicago downtown model and observe their characteristics under different conditions. We believe that the 3D virtual risk terrain has the potential to become a new routine tool for risk-aware UAS operations planning, urban airspace management, and policy development. The same idea can also be extended to other forms of societal impacts, such as noise, privacy, and perceived risk.Comment: 33 pages, 19 figure

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.Comment: 23 page

Performance analysis of Unmanned Aerial Vehicles-enabled Wireless Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.As an indispensable part of mobile communication systems, Unmanned Aerial Vehicles (UAVs) can be leveraged to complement terrestrial networks by providing coverage to areas where infrastructures are scarce. Equipped with self-navigation and strong automation, UAVs have extensive applications to environmental monitoring, disaster recovery, search and rescue, owing to their excellent agility and autonomy. As a result, an increasing demand arises for ubiquitous connectivity and reliable communication for data exchange between UAVs, and between UAVs and ground stations. Since UAVs operate in three-dimensional (3D) space with strong manoeuvrability, random trajectories and wireless propagation environment can pose significant challenges to the study on coverage and capacity of UAV networks. On the other hand, UAVs are increasingly posing threats to information security. UAVs can be potentially used to eavesdrop and jam wireless transmissions between legitimate terrestrial transceivers. It is of practical interest to understand the robustness of terrestrial wireless communications under exposure to new threats from aerial adversaries. This thesis studies the coverage and capacity, including secure coverage and secrecy capacity, of UAV-enabled wireless networks with UAVs flying under 3D random trajectories based on stochastic geometry and measure convergence theory. The detailed contributions of this thesis are summarised as: • Capacity analysis of UAV networks under random trajectories. We geometrically derive probability distributions of UAV-to-UAV distances and closed-form bounds for the capacity can be obtained by exploiting the Jensen's inequality. We extrapolate the idea to dense UAV networks and analyse the impact of network densification and imperfect channel state information on the capacity. • Connectivity analysis of uncoordinated UAV swarms. New closed-form bounds are derived for the outage probability of individual UAVs, and broadcast connectivity of each UAV which evaluates the reliability of broadcast across the swarm. The qualifying conditions of the bounds on 3D coverage and impact of ground interference on the outage are identified. • Secure connectivity analysis in UAV networks. We propose a trust model based on UAVs’ behaviour and mobility pattern and characteristics of inter-UAV channels. We derive analytical expressions of both physical and secure connectivity probabilities with/without considering Doppler shift. • Secrecy capacity analysis against aerial eavesdroppers. We analyse ergodic and ϵ-outage secrecy capacities of ground link in the presence of cooperative aerial eavesdroppers. The “cut-off” density of eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of random trajectories from random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised

Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure