COMPARATIVE ANALYSIS OF METHODS FOR BUILDING HIGH-MOBILITY COMPUTER NETWORKS BASED ON A SWARM OF UAVS

Authors

DOI:

https://doi.org/10.35546/kntu2078-4481.2024.1.26

Keywords:

Unmanned Aerial Vehicles (UAVs), mobility, connectivity model, control station, network configuration, obstacle resilience, high-mobility network

Abstract

There are numerous advantages to using unmanned aerial vehicles (UAVs) for building networks over large territories compared to existing solutions. With the increasing popularity and utilization of UAVs, new projects are emerging to investigate current issues that theoretically can be addressed using drones or swarms of drones. UAVs possess great mobility as they can easily traverse vast distances. This means they can be swiftly deployed and relocated according to network requirements. For example, in case of topographical changes or new demands for communication coverage, UAVs can be easily relocated to new areas for optimal coverage. One of the key advantages of utilizing UAVs is their potential for autonomous operation. They can be programmed to perform various tasks without significant human intervention. For example, they can automatically monitor network status and, in case of issues or failures, autonomously rectify the situation by restoring communication or reorienting themselves. UAVs have the ability to move in three-dimensional space, allowing them to provide coverage over a wide area compared to traditional methods. This enables the creation of networks with broader coverage and facilitates access to communication in regions where it was previously difficult or impossible. However, the establishment of a network using UAVs may face several drawbacks and challenges. Initially, configuration complexity can be a significant challenge. Setting up a UAV network requires understanding the principles of operation of each drone, selecting appropriate communication technology, and configuring network parameters. This may require significant effort and time from qualified professionals. Additionally, limited bandwidth can be a problem, especially when transmitting large amounts of data. Depending on the communication technology and the number of drones in the network, there may be restrictions on the network's bandwidth, which can limit its effectiveness. Moreover, the cost of implementation and maintenance of a UAV network can be significant. Furthermore, insufficient resilience to obstacles must be taken into account. Depending on the environment in which drones operate, issues with communication stability may arise due to obstacles, electromagnetic distortions, or other factors that could degrade network performance. Additionally, there is the question of potential security threats. UAV networks may be vulnerable to cyber-attacks, signal interception, or physical damage, which could result in loss of control over the drones or leakage of confidential information. Therefore, although networks built by a swarm of UAVs face a number of challenges and limitations that require careful study and resolution, they have significant potential and advantages. Research into methods to optimize configuration, ensure reliability, and reduce the cost of implementing such networks will be beneficial for further development of this technology. Such an approach will allow for maximal utilization of the potential of UAV networks and ensure their successful implementation in various application areas.

References

Ткачов В.; Мітін Д.; Дух Я.; Підвищення живучості мережної складової рою БПЛА. Комп’ютерні інтелектуальні системи та мережі. Матеріали XІ Всеукраїнської науково практичної WEB конференції аспірантів, студентів та молодих вчених (21–23 березня 2018 р.), 98–100.

Tkachov V.; Hunko M.; Quest Method for Organizing Cloud Processing of Airborne Laser Scanning Data. 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL), 565–569.

Zeng, Y.; Zhang, R. Energy-efficient UAV communication with trajectory optimization. IEEE Trans. Wirel. Commun. 2017, 16, 3747–3760.

Yang, D.; Wu, Q.; Zeng, Y.; Zhang, R. Energy tradeoff in ground-to-UAV communication via trajectory design. IEEE Trans. Veh. Technol. 2018, 67, 6721–6726.

L.Pollini, M.Innocenti, R.Mati. Vision algorithms for formation flight and aerial refueling with optimal marker labeling. AIAA-2005-6010. AIAA Modeling and Simulation Technologies Conference and Exhibit, San Francisco, California, Aug. 15–18, 2005.

Yanmaz, E.; Yahyanejad, S.; Rinner, B.; Hellwanger, H.; Bettstetter, C. Drone networks: Communications, coordination, and sensing. Ad Hoc Netw. 2018, 68, 1–15.

Cheng, F.; Zhang, S.; Li, Z.; Chen, Y.; Zhao, N.; Yu, F.R.; Leung, V.C. UAV trajectory optimization for data offloading at the edge of multiple cells. IEEE Trans. Veh. Technol. 2018, 67, 6732–6736.

Mozaffari, M.; Saad, W.; Bennis, M.; Merouane, D. Mobile Internet of Things: Can UAVs provide an energyefficient mobile architecture? In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 4–8 December 2016; pp. 1–6.

Published

2024-05-01