Analytical Approach to UAV Cargo Delivery Processes Under Malicious Interference Conditions

Fazliddin Makhmudov, Andrey Privalov, Sergey Egorenkov, Andrey Pryadkin, Alpamis Kutlimuratov, Gamzatdin Bekbaev and Young Im Cho ()
Additional contact information
Fazliddin Makhmudov: Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea
Andrey Privalov: Department of Combat Use of Artillery Units, St. Petersburg Military Order of Zhukov Institute of National Guard Troops of the Russian Federation, 198206 St. Petersburg, Russia
Sergey Egorenkov: Department of Combat Use of Artillery Units, St. Petersburg Military Order of Zhukov Institute of National Guard Troops of the Russian Federation, 198206 St. Petersburg, Russia
Andrey Pryadkin: Department of Combat Application of Signal Troops, S.M. Budyonniy Military Academy of the Signal Corps, 194064 St. Petersburg, Russia
Alpamis Kutlimuratov: Department of Applied Informatics, Kimyo International University in Tashkent, Toshkent 100121, Uzbekistan
Gamzatdin Bekbaev: Department of Finance and Digital Economics, Tashkent State University of Economics, Toshkent 100066, Uzbekistan
Young Im Cho: Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Republic of Korea

Mathematics, 2025, vol. 13, issue 12, 1-14

Abstract: The instability of the geopolitical situation due to the high terrorist danger leads to the need to take into account at the planning stage the capabilities of intruders to perform UAV flight missions. A general method for analyzing the process of cargo delivery by UAVs (Unmanned Aerial Vehicles) to hard-to-reach areas during emergencies has been proposed. This method allows for the evaluation of UAV effectiveness based on the probability of successful cargo delivery within a specified time limit. The method is based on applying topological transformation techniques to stochastic networks. The cargo delivery process is modeled as a stochastic network, followed by the determination of its equivalent function and the use of Heaviside decomposition to calculate the distribution function and the expected delivery time. This presentation of the studied process for the first time made it possible to take into account the impact on the flight mission of the UAV of the destructive impact from the attacker. This approach allows the destructive effects on the UAV from malicious interference to be considered. The input data used for the analysis are parameters that characterize the properties of individual processes within the stochastic network, represented as branches, which are computed using methodologies published in earlier studies. It has been demonstrated that the resulting distribution function of the mission completion time can be accurately approximated by a gamma distribution with a level of precision suitable for practical applications. In this case, the gamma distribution parameters are determined using the equivalent function of the stochastic network. The proposed method can be used by flight planners when scheduling UAV operations in emergency zones, especially in scenarios where there is a risk of malicious interference.

Keywords: unmanned aerial vehicle; stochastic network; topological equation; e\Equivalent function; intruder; UAV detection and interception subsystem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/12/2008/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/12/2008/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:12:p:2008-:d:1681927

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().