Simulating uav’s movement for servicing user groups with a reference point in wireless networks

Emil M. Khayrov; Nikita A. Polyakov; Ekaterina G. Medvedeva; Jiri Pokorny; Yuliya V. Gaidamaka; Jiri Hosek

doi:10.1007/978-3-030-65729-1_37

Simulating uav’s movement for servicing user groups with a reference point in wireless networks

Emil M. Khayrov
, Nikita A. Polyakov
, Ekaterina G. Medvedeva^*
, Jiri Pokorny
, Yuliya V. Gaidamaka
, Jiri Hosek

^*Corresponding author for this work

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and flexible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones’ mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.

Original language	English
Title of host publication	Internet of Things, Smart Spaces, and Next Generation Networks and Systems
Subtitle of host publication	20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings
Editors	Olga Galinina, Sergey Andreev, Sergey Balandin, Yevgeni Koucheryavy
Publisher	Springer
Pages	415-425
Number of pages	11
ISBN (Print)	978-3-030-65728-4
DOIs	https://doi.org/10.1007/978-3-030-65729-1_37
Publication status	Published - 2020
Publication type	A4 Article in conference proceedings
Event	20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020 - St. Petersburg, Russian Federation Duration: 26 Aug 2020 → 28 Aug 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12526 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020
Country/Territory	Russian Federation
City	St. Petersburg
Period	26/08/20 → 28/08/20

Funding

The publication has been prepared with the support of the RUDN University Program “5–100” (E. Khayrov, validation; N. Polyakov, visualization). The reported study was funded by RFBR, project numbers 18-07-00576 (Yu. Gaidamaka, methodology) and 20-07-01064 (E. Medvedeva, numerical analysis). For the research, the infrastructure of the SIX Center was used.

Keywords

Coverage probability
Non-terrestrial networks
UAV mobility simulation
User group mobility

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-65729-1_37

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Khayrov, E. M., Polyakov, N. A., Medvedeva, E. G., Pokorny, J., Gaidamaka, Y. V., & Hosek, J. (2020). Simulating uav’s movement for servicing user groups with a reference point in wireless networks. In O. Galinina, S. Andreev, S. Balandin, & Y. Koucheryavy (Eds.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings (pp. 415-425). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12526 LNCS). Springer. https://doi.org/10.1007/978-3-030-65729-1_37

Khayrov, Emil M. ; Polyakov, Nikita A. ; Medvedeva, Ekaterina G. et al. / Simulating uav’s movement for servicing user groups with a reference point in wireless networks. Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings. editor / Olga Galinina ; Sergey Andreev ; Sergey Balandin ; Yevgeni Koucheryavy. Springer, 2020. pp. 415-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e03892d0a06c4820ab05dc0876d63fae,

title = "Simulating uav{\textquoteright}s movement for servicing user groups with a reference point in wireless networks",

abstract = "Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and flexible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones{\textquoteright} mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.",

keywords = "Coverage probability, Non-terrestrial networks, UAV mobility simulation, User group mobility",

author = "Khayrov, \{Emil M.\} and Polyakov, \{Nikita A.\} and Medvedeva, \{Ekaterina G.\} and Jiri Pokorny and Gaidamaka, \{Yuliya V.\} and Jiri Hosek",

note = "Funding Information: The publication has been prepared with the support of the RUDN University Program “5–100” (E. Khayrov, validation; N. Polyakov, visualization). The reported study was funded by RFBR, project numbers 18-07-00576 (Yu. Gaidamaka, methodology) and 20-07-01064 (E. Medvedeva, numerical analysis). For the research, the infrastructure of the SIX Center was used. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020 ; Conference date: 26-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-65729-1\_37",

language = "English",

isbn = "978-3-030-65728-4",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "415--425",

editor = "Olga Galinina and Sergey Andreev and Sergey Balandin and Yevgeni Koucheryavy",

booktitle = "Internet of Things, Smart Spaces, and Next Generation Networks and Systems",

}

Khayrov, EM, Polyakov, NA, Medvedeva, EG, Pokorny, J, Gaidamaka, YV & Hosek, J 2020, Simulating uav’s movement for servicing user groups with a reference point in wireless networks. in O Galinina, S Andreev, S Balandin & Y Koucheryavy (eds), Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12526 LNCS, Springer, pp. 415-425, 20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020, St. Petersburg, Russian Federation, 26/08/20. https://doi.org/10.1007/978-3-030-65729-1_37

Simulating uav’s movement for servicing user groups with a reference point in wireless networks. / Khayrov, Emil M.; Polyakov, Nikita A.; Medvedeva, Ekaterina G. et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings. ed. / Olga Galinina; Sergey Andreev; Sergey Balandin; Yevgeni Koucheryavy. Springer, 2020. p. 415-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12526 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Simulating uav’s movement for servicing user groups with a reference point in wireless networks

AU - Khayrov, Emil M.

AU - Polyakov, Nikita A.

AU - Medvedeva, Ekaterina G.

AU - Pokorny, Jiri

AU - Gaidamaka, Yuliya V.

AU - Hosek, Jiri

N1 - Funding Information: The publication has been prepared with the support of the RUDN University Program “5–100” (E. Khayrov, validation; N. Polyakov, visualization). The reported study was funded by RFBR, project numbers 18-07-00576 (Yu. Gaidamaka, methodology) and 20-07-01064 (E. Medvedeva, numerical analysis). For the research, the infrastructure of the SIX Center was used. Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and flexible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones’ mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.

AB - Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and flexible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones’ mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.

KW - Coverage probability

KW - Non-terrestrial networks

KW - UAV mobility simulation

KW - User group mobility

U2 - 10.1007/978-3-030-65729-1_37

DO - 10.1007/978-3-030-65729-1_37

M3 - Conference contribution

AN - SCOPUS:85101986196

SN - 978-3-030-65728-4

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 415

EP - 425

BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems

A2 - Galinina, Olga

A2 - Andreev, Sergey

A2 - Balandin, Sergey

A2 - Koucheryavy, Yevgeni

PB - Springer

T2 - 20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020

Y2 - 26 August 2020 through 28 August 2020

ER -

Khayrov EM, Polyakov NA, Medvedeva EG, Pokorny J, Gaidamaka YV, Hosek J. Simulating uav’s movement for servicing user groups with a reference point in wireless networks. In Galinina O, Andreev S, Balandin S, Koucheryavy Y, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 20th International Conference, NEW2AN 2020 and 13th Conference, ruSMART 2020, Proceedings. Springer. 2020. p. 415-425. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-65729-1_37

Simulating uav’s movement for servicing user groups with a reference point in wireless networks

Abstract

Publication series

Conference

Funding

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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Cite this