Performance Enhancement and Load Balancing of Swarming Drones through Position Reconfiguration

Mirzaeinia, Amir; Hassanalian, Mostafa; Lee, Kooktae; Mirzaeinia, Mehdi

doi:10.2514/6.2019-3463

Cited by 16 publications

(4 citation statements)

References 16 publications

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“…Over the past recent years, autonomous aerial vehicles have been growingly drawing attention and broadening their spectrum of specifications [13][14][15][16] .…”

Section: Equipped Drones With Network Test Handset To Load the Networmentioning

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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“…Over the past recent years, autonomous aerial vehicles have been growingly drawing attention and broadening their spectrum of specifications [13][14][15][16] .…”

Section: Equipped Drones With Network Test Handset To Load the Networmentioning

confidence: 99%

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

et al. 2019

Self Cite

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“…These methods can take full advantages of the information-rich drone guidance to fulfill swarm control. In the prior art, the drone embracing rich information is directly regarded as the leader or core node [12,13]. Besides, the core node acts a pivotal part and interacts with the other ones in drone swarm.…”

Section: Introductionmentioning

confidence: 99%

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Wang

Zhuang

Xie

2021

Neural Process Lett

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Accurately tracking the influential nodes in swarm is the key to improve the control or defense against the drone combat. But the identification of influential nodes is intractable due to hardly capturing the pattern information of the running drones based on the traditional Graph Theory. To tackle this issue, in this paper, we propose a novel framework to identify the influential nodes, which includes Latent Interaction Graph Extraction (LIGE), Degree Centrality based Influential Inference (DCII) and Formation Stability Coefficient based Re-ranking (FSCR). LIGE is employed to reconstruct the latent interaction graph from the dynamic trajectory data by exploiting the encoder in Neural Relational Inference. DCII invokes the traditional graph model and measures the significance of nodes. In the re-ranking module FSCR, we introduce a novel metric, the formation stability coefficient, to measure swarm formation stability and re-ranking the candidates. The experimental results on the Drone swarm datasets demonstrate that our framework can effectively identify the influential nodes of drone swarm. The extracting time in LIGE is relatively long, which makes it difficult to recognize the core node within the limit time. By optimizing the encoder structure, the extraction time can be reduced to at least twice the original.

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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%

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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Performance Enhancement and Load Balancing of Swarming Drones through Position Reconfiguration

Cited by 16 publications

References 16 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

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

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

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