Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment
Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/11/1077 |
| _version_ | 1798025873301962752 |
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| author | Denis Horvath Juraj Gazda Eugen Slapak Taras Maksymyuk |
| author_facet | Denis Horvath Juraj Gazda Eugen Slapak Taras Maksymyuk |
| author_sort | Denis Horvath |
| collection | DOAJ |
| description | Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations. |
| first_indexed | 2024-04-11T18:25:56Z |
| format | Article |
| id | doaj.art-fa8442096a9541da8014cef256518169 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T18:25:56Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-fa8442096a9541da8014cef2565181692022-12-22T04:09:38ZengMDPI AGEntropy1099-43002019-11-012111107710.3390/e21111077e21111077Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand DeploymentDenis Horvath0Juraj Gazda1Eugen Slapak2Taras Maksymyuk3Center for Interdisciplinary Biosciences, Technology and Innovation Park, University of Pavol Jozef Šafárik, Jesenná 5, 041 01 Košice, Slovak RepublicDepartment of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, Slovak RepublicDepartment of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, Slovak RepublicLviv Polytechnic National University, 12 S. Bandery St., 790 13 Lviv, UkraineAttempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations.https://www.mdpi.com/1099-4300/21/11/1077uav swarmstochastic dynamicsdisasterwireless connectionscoverageadaptivity |
| spellingShingle | Denis Horvath Juraj Gazda Eugen Slapak Taras Maksymyuk Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment Entropy uav swarm stochastic dynamics disaster wireless connections coverage adaptivity |
| title | Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment |
| title_full | Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment |
| title_fullStr | Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment |
| title_full_unstemmed | Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment |
| title_short | Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment |
| title_sort | modeling and analysis of self organizing uav assisted mobile networks with dynamic on demand deployment |
| topic | uav swarm stochastic dynamics disaster wireless connections coverage adaptivity |
| url | https://www.mdpi.com/1099-4300/21/11/1077 |
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