Distributed Topology Control based on Swarm Intelligence In Unmanned Aerial Vehicles Networks

Zhang, Q., Feng, G., Qin, S. and Sun, Y. (2020) Distributed Topology Control based on Swarm Intelligence In Unmanned Aerial Vehicles Networks. In: 2020 IEEE Wireless Communications and Networking Conference, Seoul, Korea, 06-10 Apr 2020, ISBN 9781728131061 (doi:10.1109/WCNC45663.2020.9120571)

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Abstract

Unmanned aerial vehicles (UAVs) have shown enormous potential in both public and civil domains. Although multi-UAV systems can collaboratively accomplish missions efficiently, UAV network(UAVNET) design faces many challenging issues, such as high mobility, dynamic topology, power constraints, and varying quality of communication links. Topology control plays a key role for providing high network connectivity while conserving power in UAVNETs. In this paper, we propose a distributed topology control algorithm based on discrete particle swarm optimization with articulation points(AP-DPSO). To reduce signaling overhead and facilitate distributed control, we first identify a set of articulation points (APs) to partition the network into multiple segments. The local topology control problem for individual segments is formulated as a degree-constrained minimum spanning tree problem. Each node collects local topology information and adjusts its transmit power to minimize power consumption. We conduct simulation experiments to evaluate the performance of the proposed AP-DPSO algorithm. Numerical results show that AP-DPSO outperforms some known algorithms including LMST and LSP, in terms of network connectivity, average link length and network robustness for a dynamic UAVNET.

Item Type:Conference Proceedings
Additional Information:This work has been supported by the Natural Science Foundation of China (Grant No. 61871099), the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2019J122).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Sun, Dr Yao and Feng, Professor Gang
Authors: Zhang, Q., Feng, G., Qin, S., and Sun, Y.
College/School:College of Science and Engineering > School of Engineering
ISSN:1558-2612
ISBN:9781728131061
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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