Self-organized UAV-based Supervision and Connectivity: Challenges and Opportunities

Busnel, Yann; Caillouet, Christelle; Coudert, David

doi:10.1109/nca.2019.8935060

Cited by 7 publications

(5 citation statements)

References 46 publications

(48 reference statements)

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“…The authors concluded their work with the challenges in both DL and UAV autonomy. The short review paper in [36] tried to reiterate the importance of self-organization (for a detailed discussion on self-organization in cellular networks, the readers are referred to [37]) in UAV-assisted connectivity. The scalability issue in UAV-assisted networking was raised, and the role of distributed algorithms in mitigating such issues was also discussed.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Related Workmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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show abstract

“…A great number of recent works have been published in this field in the research literature [ 8 , 9 ]. In [ 10 ], the authors highlight the challenges in terms of connectivity in self-organized UAV deployments involving many UAVs. Attending to the contribution of the paper in the area of sensor interconnection using UAVs, in the following we focus on the application of these vehicles in IoT scenarios [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Extensive and Remote LoRa Deployments through MEC-Powered Drone Gateways

Gallego-Madrid

Zarca

Sánchez-Iborra

et al. 2020

Sensors

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The distribution of Internet of Things (IoT) devices in remote areas and the need for network resilience in such deployments is increasingly important in smart spaces covering scenarios, such as agriculture, forest, coast preservation, and connectivity survival against disasters. Although Low-Power Wide Area Network (LPWAN) technologies, like LoRa, support high connectivity ranges, communication paths can suffer from obstruction due to orography or buildings, and large areas are still difficult to cover with wired gateways, due to the lack of network or power infrastructure. The proposal presented herein proposes to mount LPWAN gateways in drones in order to generate airborne network segments providing enhanced connectivity to sensor nodes wherever needed. Our LoRa-drone gateways can be used either to collect data and then report them to the back-office directly, or store-carry-and-forward data until a proper communication link with the infrastructure network is available. The proposed architecture relies on Multi-Access Edge Computing (MEC) capabilities to host a virtualization platform on-board the drone, aiming at providing an intermediate processing layer that runs Virtualized Networking Functions (VNF). This way, both preprocessing or intelligent analytics can be locally performed, saving communications and memory resources. The contribution includes a system architecture that has been successfully validated through experimentation with a real test-bed and comprehensively evaluated through computer simulation. The results show significant communication improvements employing LoRa-drone gateways when compared to traditional fixed LoRa deployments in terms of link availability and covered areas, especially in vast monitored extensions, or at points with difficult access, such as rugged zones.

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“…[8]. Flying Ad-hoc Networks (FANET) [1] can fill the lack of networking infrastructure by allowing the aerial devices to cooperate efficiently to collect data from observations or communications with ground sensors, and gather information to a central processing unit [2,14].…”

Section: Introductionmentioning

confidence: 99%

Optimizing FANET deployment for mobile sensor tracking in disaster management scenario

Silva

Caillouet

Coudert

2021

2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

Self Cite

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This paper addresses the data collection problem using the minimum number of unmanned aerial vehicles (UAVs) in a disaster management scenario where mobile sensors are investigating the devastated area. Critical information needs to be quickly gathered for processing by the rescue team, so the use of UAVs in this situation is of great interest. We propose an optimal model for computing the trajectories of the UAVs while guaranteeing the total coverage of the ground mobile sensors and connectivity among the UAVs with a central base station dedicated to data processing. Our model is based on a decomposition model and is solved effectively using column generation. We show that we can provide a plan for deploying the UAVs minimizing the total traveled distance.

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Self-organized UAV-based Supervision and Connectivity: Challenges and Opportunities

Cited by 7 publications

References 46 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Enhancing Extensive and Remote LoRa Deployments through MEC-Powered Drone Gateways

Optimizing FANET deployment for mobile sensor tracking in disaster management scenario

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