UAV-to-Ground Multi-Hop Communication Using Backpressure and FlashLinQ-Based Algorithms

Katila, Charles Jumaa; Okolo, Benjamin; Buratti, Chiara; Verdone, Roberto; Caire, Giuseppe

doi:10.1109/pimrc.2018.8580911

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Both maintenance of the connectivity and reducing energy consumption allow providing a certain degree of reliability and a predefined threshold of QoS [118]. To design a fully cooperative UAV network, a set of rules is required to define how data should be exchanged between UAVs and ground BSs [119]. There are many architectures proposed across the literature, but in this section, we analyze the major architectures that are adopted in most applications.…”

Section: A Uav Networkmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: A Uav Networkmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…Recent advances in Unmanned Aerial Vehicle (UAV) technology have opened up the possibility of utilizing UAVs in many research and industrial areas. Initially, UAVs were developed for military purposes, but now UAVs can be used in a wide variety of civilian fields, such as video shooting, surveillance for crime investigations, logistics delivery, and communication infrastructure provision [ 1 ]. Meanwhile, UAVs have also become smaller, cheaper to use, and have advanced in terms of freedom and maneuverability.…”

Section: Introductionmentioning

confidence: 99%

“…Although some research has been conducted to control the transmission power of a network topology composed of numerous nodes [ 1 ], the presence of a centralized controller may limit the scalability of network control using multiple UAVs. To overcome these limitations, a control system that configures a network topology for multiple UAVs based on the Minimum Spanning Tree (MST) concept can be a desirable solution.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Topology Control for UAV Networks

Yoo

Lee

Yoo

et al. 2023

Sensors

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The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.

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“…Unmanned aerial vehicles (UAV) were originally developed and used for military purposes, but as drone technology gradually develops, drones are becoming more popular and are being used in various fields [1]. There are various advantages in utilizing multiple drones as a swarm [2].…”

Section: Introductionmentioning

confidence: 99%

Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning

Park

Lee

Joo

et al. 2023

Drones

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Since unmanned aerial vehicles (UAVs), such as drones, are used in various fields due to their high utilization and agile mobility, technologies to deal with multiple UAVs are becoming more important. There are many advantages to using multiple drones in a swarm, but, at the same time, each drone requires a strong connection to some or all of the other drones. This paper presents a superior approach for the UAV network’s routing system without wasting memory and computing power. We design a routing system called the geolocation ad hoc network (GLAN) using geolocation information, and we build an adaptive GLAN (AGLAN) system that applies reinforcement learning to adapt to the changing environment. Furthermore, we increase the learning speed by applying a pseudo-attention function to the existing reinforcement learning. We evaluate the proposed system against traditional routing algorithms.

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UAV-to-Ground Multi-Hop Communication Using Backpressure and FlashLinQ-Based Algorithms

Cited by 9 publications

References 10 publications

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Reinforcement Learning Based Topology Control for UAV Networks

Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning

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