• I. Kliushnikov Kharkiv National University of Air Force
  • H. Fesenko National Aerospace University “Kharkiv Aviation Institute”
Keywords: flying wireless network, load balancing, unmanned aerial vehicle, automatic replacement and charging station, one-dimensional array, ordered array, flight route


Today, multi-rotor UAVs (MUAVs) equipped with an electric motor are considered to be used as an affordable and cost-efficient tool to deploy flying wireless networks (FWNs). Nevertheless, the popular MUAVs have an endurance of about 30 minutes only. The last fact presents a significant barrier to use FWNs in complex, long-term missions. To overcome this problem, MUAVs can use shift schedule with a possibility for free schedule to be served at an automatic replacement and charging station (ARCS). After visiting the ARCS, MUAVs can either use the same route every new duty cycle or change the route.

The operation of a flying wireless network, consisting of five MUAVs and using one ARCS, is considered. The purpose of the flying wireless network is to organize the transmission of radiation monitoring data in the event of damage of the wired channel between a monitoring station and the crisis centre by creating: WiFi channel between the monitoring station and UAV of an aircraft type; LoRaWAN channel between AUAV and the crisis centre.

The following assumptions take place:  the UAV of an aircraft type has sufficient flight time to complete the mission; five MUAVs periodically visit the ARCS; the location of the MUAVs within the flying wireless network during each subsequent cycle may be changed.

The problem of MUAV flight planning using the maximin criterion is formulated. It is shown that this problem can be transformed to the problem of finding the shortest path for each individual MUAV for load balancing between them.

The stages of implementation of the method of load balancing between the MUAVs of the flying wireless network are considered. The method requires adjusting flights routes for the MUAVs between the ARCS and the flying wireless network for each duty cycle. An example of the proposed method application to adjust flights routes for each of the three duty cycles is given.

Author Biographies

I. Kliushnikov, Kharkiv National University of Air Force

Ph.D., Senior Research Fellow

H. Fesenko, National Aerospace University “Kharkiv Aviation Institute”

Ph.D., Associate Professor


Sachenko, A. A., Kochan, V. V., Kharchenko, V. S., Yastrebenetskii, M. A., Fesenko, H. V., & Yanovskii, M. E. (2017). NPP post-accident monitoring system based on unmanned aircraft vehicle: сoncept, design principles. Nuclear and Radiation Safety, 73(1), 24-29.

Kliushnikov, I. M., Fesenko, H. V., & Kharchenko, V. S. (2019). Using automated battery replacement stations for the persistent operation of UAV-enabled wireless networks during NPP post-accident monitoring. Radioelectronic and Computer Systems, 92(4), 30-38.

Fetisov, V. S., Artem'ev, A. E., & Mufazzalov, D. F. (2017). Automatic Service Stations for Servicing Electric Unmanned Aerial Vehicles : Monograph. – Moscow, 253.

Boggio-Dandry, A., & Soyata, T. (2018). Perpetual flight for UAV drone swarms using continuous energy replenishment. 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 478-484.

Shin, M., Kim, J., & Levorato, M. (2019). Auction-based charging scheduling with deep learning framework for multi-drone networks. IEEE Transactions on Vehicular Technology, 68(5), 4235-4248.

Michini, B., Toksoz, T., Redding, J., Michini, M., How, J., Vavrina, M., & Vian, J. (2011). Automated battery swap and recharge to enable persistent UAV missions. Infotech@Aerospace 2011. American Institute of Aeronautics and Astronautics. Retrieved from http://hdl.handle.net/1721.1/8. (Accessed 10.11.2019).

Fetisov, V., Dmitriyev, O., Neugodnikova, L., Bersenyov, S., & Sakayev, I. (2012). Continuous monitoring of terrestrial objects by means of duty group of multicopters. XX IMEKO World Congress, 85.

Bauer, R., Delling, D. S,anders, P., Schieferdecker, D., Schultes, D., & Wagner, D. Combining hierarchical and goal-directed speed-up techniques for Dijkstra’s algorithm. ACM Journal of Experimental Algorithmics, 15, 1-31.

Wang, Z. J., Han, W. Y., & Li, Y. J. (2010). Shortest path problem with multiple shortest paths. Journal of Harbin Institute of Technology, 42(9), 1428–1431.

Abraham, I., Delling, D., Goldberg, A. V., & Werneck, R. F. (2011). A hub-based labeling algorithm for shortest paths in road networks. Lecture Notes in Computer Science, 6630,. 230-241.

Silverbush, D., & Sharan, R. (2014). Network orientation via shortest paths. Bioinformatics, 30(10), 1449-1455.

How to Cite
KliushnikovI., & FesenkoH. (2020). LOAD BALANCING BETWEEN UNMANNED AERIAL VEHICLES OF A FLYING WIRELESS NETWORK USING AUTOMATIC REPLACEMENT AND CHARGING STATIONS. Municipal Economy of Cities, 1(154), 113-119. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/5540