TY - GEN
T1 - Network-Aware Genetic Algorithms for the Coordination of MALE UAV Networks
AU - Giagkos, Alexandros
AU - Wilson, Myra S.
AU - Bancroft, Ben
N1 - Copyright © Springer Nature Switzerland AG, 2021. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-89177-0_12.
PY - 2021
Y1 - 2021
N2 - Maintaining an ad hoc network infrastructure to cover multiple ground-based users can be achieved by autonomous groups of hydrocarbon powered medium-altitude, long-endurance (MALE) unmanned aerial vehicles (UAVs). This can be seen as an optimisation problem to maximise the number of users supported by a quality network while making efficient use of the available power. We present an architecture that combines genetic algorithms with a network simulator to evolve flying solutions for groups of UAVs. Results indicate that our system generates physical network topologies that are usable and offer consistent network quality. It offers a higher goodput than the non-network-aware equivalent when covering the communication demands of multiple ground-based users. Most importantly, the proposed architecture flies the UAVs at lower altitudes making sure that downstream links remain active throughout the duration of the mission.
AB - Maintaining an ad hoc network infrastructure to cover multiple ground-based users can be achieved by autonomous groups of hydrocarbon powered medium-altitude, long-endurance (MALE) unmanned aerial vehicles (UAVs). This can be seen as an optimisation problem to maximise the number of users supported by a quality network while making efficient use of the available power. We present an architecture that combines genetic algorithms with a network simulator to evolve flying solutions for groups of UAVs. Results indicate that our system generates physical network topologies that are usable and offer consistent network quality. It offers a higher goodput than the non-network-aware equivalent when covering the communication demands of multiple ground-based users. Most importantly, the proposed architecture flies the UAVs at lower altitudes making sure that downstream links remain active throughout the duration of the mission.
KW - Genetic algorithms
KW - Networks
KW - Unmanned aerial vehicles
KW - Wireless communication
UR - http://www.scopus.com/inward/record.url?scp=85119379745&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-030-89177-0_12
U2 - 10.1007/978-3-030-89177-0_12
DO - 10.1007/978-3-030-89177-0_12
M3 - Conference publication
AN - SCOPUS:85119379745
SN - 9783030891763
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 125
BT - Towards Autonomous Robotic Systems - 22nd Annual Conference, TAROS 2021, Proceedings
A2 - Fox, Charles
A2 - Gao, Junfeng
A2 - Ghalamzan Esfahani, Amir
A2 - Saaj, Mini
A2 - Hanheide, Marc
A2 - Parsons, Simon
PB - Springer
T2 - 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021
Y2 - 8 September 2021 through 10 September 2021
ER -