Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

Shan, Lin; Miura, Ryu; Matsuda, Takashi; Koshikawa, Miho; Li, Huan-Bang; Matsumura, Takeshi

doi:10.3390/drones7110669

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Each CV adopts the same transmission power . The Base Station (BS) or UAV can detect the status information of CVs and RVs to assist V2V communication 22 . Due to environmental factors, the signal transmission between the transmitter and receiver will be affected by varying degrees of fading.…”

Section: System Model Of Cache-enabled Uav-assisted Iovmentioning

confidence: 99%

Joint cooperative caching and power control for UAV-assisted internet of vehicles

Wang,

Liu,

Dai

et al. 2024

Sci Rep

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In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE) . In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method.

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Section: System Model Of Cache-enabled Uav-assisted Iovmentioning

confidence: 99%

Joint cooperative caching and power control for UAV-assisted internet of vehicles

Wang,

Liu,

Dai

et al. 2024

Sci Rep

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“…(2) UAV traffic management With the development of 5G/B5G communication technology, UAV traffic management (UTM) has recently garnered significant attention from research scientists [69][70][71]. This is due to the dramatic evolution of UAV applications such as parcel delivery, precise agriculture, and survey mapping, which usually operate beyond the visual line of sight mode.…”

Section: Unmanned Aerial Vehicles Application In Ntnsmentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

Information

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

Fernando,

Gupta

2024

Drones

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The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

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Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

Cited by 3 publications

References 27 publications

Joint cooperative caching and power control for UAV-assisted internet of vehicles

Joint cooperative caching and power control for UAV-assisted internet of vehicles

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

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