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Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption

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dc.citedby7
dc.contributor.authorAlsammak I.L.H.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAlkilabi M.en_US
dc.contributor.authorid57220190775en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57191252149en_US
dc.date.accessioned2024-10-14T03:21:04Z
dc.date.available2024-10-14T03:21:04Z
dc.date.issued2023
dc.description.abstractWildfires are among the biggest problems faced worldwide. They are increasing in severity and frequency, causing economic losses, human death, and significant environmental damage. Environmental factors, such as wind and large forest areas, contribute to the fire spreading over multiple fire spots, all of which grow continuously, making fire suppression extremely difficult. Therefore, fire spots should be coverage simultaneously to contain the spread and prevent coalescence. Therefore, this study presents a new model based on the principles of nature-inspired metaheuristics that uses Swarm Intelligence (SI) to test the effectiveness of using an autonomous and decentralized behaviour for a swarm of Unmanned Aerial Vehicles (UAVs) or drones to detect all distributed fire spots and extinguishing them cooperatively. To achieve this goal, we used the improved random walk algorithm to explore the distributed fire spots and a self-coordination mechanism based on the stigmergy as an indirect communication between the swarm drones, taking into account the collision avoidance factor, the amount of extinguishing fluid, and the flight range of the drones. Numerical analysis and extensive simulations were performed to investigate the behaviour of the proposed methods and analyze their performance in terms of the area-coverage rate and total energy required by the drone swarm to complete the task. Our quantitative tests show that the improved model has the best coverage (95.3%, 84.3% and 65.8%, respectively) compared to two other methods Levy Flight (LF) algorithm and Particle Swarm Optimization (PSO), which use the same initial parameter values. The simulation results show that the proposed model performs better than its competitors and saves energy, especially in more complicated situations. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3279416
dc.identifier.epage50983
dc.identifier.scopus2-s2.0-85161054187
dc.identifier.spage50962
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161054187&doi=10.1109%2fACCESS.2023.3279416&partnerID=40&md5=600446ca503df267c880c46ddaff1ed7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34608
dc.identifier.volume11
dc.pagecount21
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectRandom walk algorithm
dc.subjectstigmergy
dc.subjectswarm intelligence
dc.subjectUAVs
dc.subjectwildfires suppression
dc.subjectAgricultural robots
dc.subjectAntennas
dc.subjectDrones
dc.subjectEnergy utilization
dc.subjectFires
dc.subjectHigh resolution transmission electron microscopy
dc.subjectIntelligent robots
dc.subjectJob analysis
dc.subjectLosses
dc.subjectNumerical methods
dc.subjectParticle swarm optimization (PSO)
dc.subjectRandom processes
dc.subjectSwarm intelligence
dc.subjectAerial vehicle
dc.subjectClassification algorithm
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectRandom walk algorithms
dc.subjectStigmergy
dc.subjectSwarm optimization
dc.subjectTask analysis
dc.subjectUnmanned aerial vehicle
dc.subjectWildfire suppression
dc.subjectForestry
dc.titleNature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumptionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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