Energy Saving of Echelon Flocking Northern bald ibises with Variable wingtips spacing: Possibility of New Swarming for Drones

Mirzaeinia, Amir; Bradfield, Quincy A.; Bradley, Savannah; Hassanalian, Mostafa

doi:10.2514/6.2019-4307

Cited by 9 publications

(10 citation statements)

References 19 publications

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“…Sometimes applying multiple drones to perform a defined mission is more efficient than having one drone [26][27][28][29][30] . The flight of multiple drones which is inspired by nature is called swarming.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

et al. 2019

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Cellular network operators have problems to test their network without affecting their user experience. Testing network performance in a loaded situation is a challenge for the network operator because network performance differs when it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to load the cellular network and scan/test the network performance more realistically. Besides, manual swarming drone navigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to be deployed on swarming drone to find the regions where there are performance issues. Swarming drone communications helps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help to have almost non-stochastic received signal level as an objective function. Moreover, there are some situations that more than one network parameter should be used to find a problematic region in the cellular network. It is also proposed to apply multi-objective PSO to find more multi-parameter network optimization at the same time.Cellular networks have been growing rapidly over the last few decades. This technology is exceptionally beloved these days because people can be connected as they move around anyplace. Wireless communication was developed even before cellular networks, but radio communication always suffers from resource limitation (frequency spectrum as the main resource). Resource limitation was the main motivation to propose resource reuse in cellular networks [1] . For example, if a frequency band is used in a certain cell in the cellular network to cover a particular area, it also can be used in another cell which is farther in the distance from the first one. The idea of resource reuse helps to increase the number of users being serviced. This idea of resource reuse is the main difference between cellular network technology and other radio networks. However, this adds to the complexity of fine-tuning of the network to achieve high-performance cells. The reason is that radio interference can decrease the quality of service in these networks. First-generation cellular networks started in the 1980s as analog radio communication. Later on, the 2G network was commercially launched around the

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

et al. 2019

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“…The formation flight of migrating birds has been studied for more than fifty years. This behavior of the migrating birds has increased the interest of drones' designers to come with some new solutions for enhancing the drones' endurance and efficiency [3][4][5][6][7][8][9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

“…These researches have indicated that juvenile Northern bald Ibis can play a positive role during the flocking by precisely matching times they spend in the advantageous and disadvantageous positions and taking turns 8,15 . Moreover, it has been shown that the tendency of these migrating birds to lead the flock has a significant effect on the consistency and size of the formations.…”

Section: Introductionmentioning

confidence: 99%

“…Mirzaeinia et al 8 in 2019 studied the energy saving of echelon flocking Northern bald Ibises with variable wingtips spacing 8 . They showed that the wingtip spacing can play an important role in the energy saving of these migratory birds.…”

Section: Introductionmentioning

confidence: 99%

“…In both studies conducted by Mirzaeinia and Hassanalian 8,10 , it was assumed that all the birds have a similar wingspan, weight, and speed. They investigated the effects of replacing the lead and tail birds with middle ones.…”

Section: Introductionmentioning

confidence: 99%

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Flocking of V-shaped and Echelon Northern Bald Ibises with Different Wingspans: Repositioning and Energy Saving

Mirzaeinia

Hassanalian

2020

AIAA Scitech 2020 Forum

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V-shaped and echelon formations help migratory birds to consume less energy for migration. As the case study, the formation flight of the Northern Bald Ibises is considered to investigate different effects on their flight efficiency. The effects of the wingtip spacing and wingspan are examined on the individual drag of each Ibis in the flock. Two scenarios are considered in this study, (1) increasing and (2) decreasing wingspans toward the tail. An algorithm is applied for replacement mechanism and load balancing of the Ibises during their flight. In this replacing mechanism, the Ibises with the highest value of remained energy are replaced with the Ibises with the lowest energy, iteratively. The results indicate that depending on the positions of the birds with various sizes in the flock, they consume a different level of energy. Moreover, it is found that also small birds have the chance to take the lead during the flock.

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Die Schwarmintelligenz

Gellert

2023

Die Wildnis Und Wir

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Energy Saving of Echelon Flocking Northern bald ibises with Variable wingtips spacing: Possibility of New Swarming for Drones

Cited by 9 publications

References 19 publications

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

Flocking of V-shaped and Echelon Northern Bald Ibises with Different Wingspans: Repositioning and Energy Saving

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