Coordinated Coverage and Fault Tolerance using Fixed-wing Unmanned Aerial Vehicles

Shriwastav, Sachin; Song, Zhuoyuan

doi:10.1109/icuas48674.2020.9213833

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…For Shriwastav and Song [ 15 ], area coverage is considered in a different class of WVSN, the Unmanned Aerial Vehicle (UAV) networks. In this case, each UAV is equipped with a camera, being responsible to perform a persistent full coverage of the area.…”

Section: Related Workmentioning

confidence: 99%

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A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

Jesus

Portugal

Costa

et al. 2020

Sensors

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In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

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Section: Related Workmentioning

confidence: 99%

“…Although they consider QoM in area coverage, neither the works in References [ 12 , 13 , 14 , 15 ] actually compute a sensor node’s or network’s QoM. The work in Tao et al [ 6 ] overcomes this issue by defining a weighted sensing quality, which is calculated over a predefined deployment scheme.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

Jesus

Portugal

Costa

et al. 2020

Sensors

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“…However, they only consider a single fixed-wing UAV relay to amplify and forward (AF) received signals between source(s) and destination. In geographically-large rural areas with scattered concentrations of users, multiple fixed-wing UAVs may be deployed [4], [9], [10]. In [4], an iterative method was proposed to optimise the energy consumption of low-power ground devices served by multiple fixed-wing UAVs, by adjusting the UAV trajectories to allow for lowpower data transmission from the ground devices.…”

Section: Introductionmentioning

confidence: 99%

“…The authors demonstrate that increasing the radius of a UAV's circular orbit can reduce the energy consumption for that UAV. The work in [10] proposed deployments that maximise coverage while improving resilience against UAV failure, using circle-packing theory. Note that as [4], [9], [10] focus on basic network coverage scenarios, they do not consider data throughput in their system models, and their optimisation algorithms do not optimise the amount of data that can be delivered across the UAV-user access link.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points

Галкін¹,

Omoniwa²,

Dusparić³

2021

Preprint

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Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to supplement existing terrestrial networks. The majority of the existing research into the use of UAV access points for cellular coverage considers rotary-wing UAV designs (i.e. quadcopters). However, we expect fixed-wing UAVs to be more appropriate for connectivity purposes in scenarios where long flight times are necessary (such as for rural coverage), as fixed-wing UAVs rely on a more energy-efficient form of flight when compared to the rotary-wing design. As fixed-wing UAVs are typically incapable of hovering in place, their deployment optimisation involves optimising their individual flight trajectories in a way that allows them to deliver high quality service to the ground users in an energy-efficient manner. In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground. In our decentralized approach, each UAV is equipped with a Dueling Deep Q-Network (DDQN) agent which can adjust the 3D trajectory of the UAV over a series of timesteps. By coordinating with their neighbours, the UAVs adjust their individual flight trajectories in a manner that optimises the total system energy efficiency. We benchmark the performance of our approach against a series of heuristic trajectory planning strategies, and demonstrate that our method can improve the system energy efficiency by as much as 70%.

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“…Danoy et al [21] presented an online and distributed approach for bi-level flying ad-hoc networks, in which the higher-level fixed-wing fleet serves mainly as a communication bridge for the lower-level fleets that conduct precise information sensing. In [22], full coverage of a desired rectangular area was maintained using homogeneous fixed-wing aerial vehicles loitering on a given altitude, using both square and hexagon packing. In the case of multiple UAV failures, the algorithm restored full coverage by modifying the loitering locations and a new homogeneous radius of the remaining UAVs at the same altitude.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Fixed-wing Aerial Vehicles for Resilient Coverage of an Area

Shriwastav

Song

2020

Preprint

Self Cite

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This paper presents a distributed approach to provide persistent coverage of an arbitrarily shaped area using heterogeneous coverage of fixed-wing unmanned aerial vehicles (UAVs), and to recover from simultaneous failures of multiple UAVs. The proposed approach discusses levelhomogeneous deployment and maintenance of a homogeneous fleet of fixed-wing UAVs given the boundary information and the minimum loitering radius. The UAVs are deployed at different altitude levels to provide heterogeneous coverage and sensing. We use an efficient square packing method to deploy the UAVs, given the minimum loiter radius and the area boundary. The UAVs loiter over the circles inscribed over these packing squares in a synchronized motion to fulfill the full coverage objective. An top-down hierarchy of the square packing, where each outer square (super-square) is partitioned into four equal-sized inner squares (sub-square), is exploited to introduce resilience in the deployed UAV-network. For a failed sub-square UAV, a replacement neighbor is chosen considering the effective coverage and deployed to the corresponding super-square at a higher altitude to recover full coverage, trading-off with the quality of coverage of the sub-area. This is a distributed approach as all the decision making is done within close range of the loss region, and it can be scaled and adapted to various large scale area and UAV configurations. Simulation results have been presented to illustrate and verify the applicability of the approach.

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Coordinated Coverage and Fault Tolerance using Fixed-wing Unmanned Aerial Vehicles

Cited by 6 publications

References 19 publications

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points

Heterogeneous Fixed-wing Aerial Vehicles for Resilient Coverage of an Area

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