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Improving Pheromone Communication for UAV Swarm Mobility Management

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles’ routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage.

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Acknowledgments

This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg [20] – see https://hpc.uni.lu.

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Authors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Daniel H. Stolfi, Matthias R. Brust, Grégoire Danoy & Pascal Bouvry

  2. FSTM/DCS, University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Grégoire Danoy & Pascal Bouvry

Authors
  1. Daniel H. Stolfi
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  2. Matthias R. Brust
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  3. Grégoire Danoy
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  4. Pascal Bouvry
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Corresponding author

Correspondence to Daniel H. Stolfi .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Stolfi, D.H., Brust, M.R., Danoy, G., Bouvry, P. (2021). Improving Pheromone Communication for UAV Swarm Mobility Management. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_17

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  • Online ISBN: 978-3-030-88081-1

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