A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs

Fernandez, Stephanie Alvarez; Carvalho, Marcelo M.; Silva, Daniel G.

doi:10.3390/a13120323

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…UAVs are equipped with a specialized sensor that sends data to farmers in real time. UAV deployment was addressed in [17] to set up a wireless network for usage in situations such as disaster relief, search and rescue, and user connectivity in hostile or remote locations. To account for both ground-to-air and air-to-air propagation, the authors suggested a mathematical approach.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Optimization Solution for UAV Network Routing

Kout

Bouaita

Beghriche

et al. 2023

Eng. Technol. Appl. Sci. Res.

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An Unmanned Aerial Vehicle (UAV) network specifies a novel type of Mobile Ad hoc Network (MANET) in which drones serve as nodes and facilitate the retransmission of messages to their final destinations. Aside from its military application, it has recently begun to seep into the civilian sector. Similar to MANET and vehicular ad hoc networks, Flying Ad hoc Networks (FANET) are a subset of ad hoc networks. An FANET is different because it is founded on UAVs. Due to the characteristics of this sort of network, which is defined by a highly changing topology in a 3D environment, we must employ an adjusted configuration method to ensure good routing performance. Therefore, to deal with this problem, a technique that responds to any change in topology by always finding the best route is required. In this work, we propose a new protocol based on the hybrid optimization of the 2-opt heuristic and Honey Badger Algorithm (HBA), called HB-AODV. In order to locate its prey, a badger must move slowly and continuously while using scent markers and mouse-digging skills to catch it. In other words, the most efficient routes in terms of the number of hops are identified. Several simulations were conducted via the 3D version of Network Simulator (NS-2) on different deployment strategies. In comparison to AODV, DSDV, and AntHocNet, the obtained results demonstrated the proposed scheme’s good performance in terms of quality of service metrics.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Optimization Solution for UAV Network Routing

Kout

Bouaita

Beghriche

et al. 2023

Eng. Technol. Appl. Sci. Res.

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“…In this report, Euclidean distance 𝐸 𝐷 𝑗 is used to determine the absolute distance between SGCS and the drones during deployment. Euclidean distance is given as (5) [28]:…”

Section: Lunch Path To Initial Surveillance Regionmentioning

confidence: 99%

Drone’s node placement algorithm with routing protocols to enhance surveillance

Akut

Usman

Abubilal

et al. 2023

IJECE

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Flying ad-hoc network (FANET) is characterized by key component features such as communication scheme, energy awareness, and task distribution. In this research, a surveillance space considering standard petroleum pipe was created with three drones viz: drone 1 (D1), master drone (DM), and drone 2 (D2) to survey as FANET. DM aggregate packets from D1, D2 and communicate with the static ground control station (SGCS). The starting point of the three drones and their trajectories during deployment were calculated and simulated. Selection of DM, D1, and D2 was done using battery level before take-off. Simulation results show take-off time difference which depends on the distance of each drone to the SGCS during deployment. D1 take-off first, while DM and D2 followed after 0.0704 and 0.1314 ms respectively. The position-oriented routing protocols results indicated variation of information flow within time notch due to variation in the density of the transmitted packets. Packets delivery periods are 0.00136×103 sec, 0.00110×103 sec, and 0.00246×103 sec for time notch 1, 2, and aggregating time notch respectively. From the results obtained, two algorithms were used successfully in deploying the drones

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“…The truck and drone routes are defined in advance, where intermediate stops to recharge the drone battery are included in the routing problem. Alvarez et al [18] tackle the problem of determining the location of drone facilities. The problem is approached as a p-median, where one wants to maximize the coverage of the stations by locating stations that can quickly respond to the needs of UAVs.…”

Section: Fuel Management Strategies Of Uavsmentioning

confidence: 99%

An Online Algorithm for Routing an Unmanned Aerial Vehicle for Road Network Exploration Operations after Disasters under Different Refueling Strategies

Reyes-Rubiano

Voegl²,

Hirsch³

2022

Algorithms

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This paper is dedicated to studying on-line routing decisions for exploring a disrupted road network in the context of humanitarian logistics using an unmanned aerial vehicle (UAV) with flying range limitations. The exploration aims to extract accurate information for assessing damage to infrastructure and road accessibility of victim locations in the aftermath of a disaster. We propose an algorithm to conduct routing decisions involving the aerial and road network simultaneously, assuming that no information about the state of the road network is available in the beginning. Our solution approach uses different strategies to deal with the detected disruptions and refueling decisions during the exploration process. The strategies differ mainly regarding where and when the UAV is refueled. We analyze the interplay of the type and level of disruption of the network with the number of possible refueling stations and the refueling strategy chosen. The aim is to find the best combination of the number of refueling stations and refueling strategy for different settings of the network type and disruption level.

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A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs

Cited by 7 publications

References 38 publications

A Hybrid Optimization Solution for UAV Network Routing

A Hybrid Optimization Solution for UAV Network Routing

Drone’s node placement algorithm with routing protocols to enhance surveillance

An Online Algorithm for Routing an Unmanned Aerial Vehicle for Road Network Exploration Operations after Disasters under Different Refueling Strategies

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