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How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs

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Wireless and Satellite Systems (WiSATS 2017)

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Abstract

Unmanned Aerial Vehicles (UAVs) are getting momentum. A growing number of industries and scientific institutions are focusing on these devices. UAVs can be used for a really wide spectrum of civilian and military applications. Usually these devices run on batteries, thus it is fundamental to efficiently exploit their hardware to reduce their energy footprint. A key aspect in facing the “energy issue” is the exploitation of properly designed solutions in order to target the energy- and hardware-constraints characterising these devices. However, there are not universal approaches easing the implementation of ad-hoc solutions for UAVs; it just depends on the class of problems to be faced. As matter of fact, targeting machine-learning solutions to UAVs could foster the development of a wide range of interesting application. This contribution is aimed at sketching the challenges deriving from the porting of machine-learning solutions, and the associated requirements, to highly distributed, constrained, inter-connected devices, highlighting the issues that could hinder their exploitation for UAVs.

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Authors and Affiliations

  1. Information Science and Technologies Institute, National Research Council of Italy, Pisa, Italy

    Patrizio Dazzi & Pietro Cassarà

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  1. Patrizio Dazzi
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  2. Pietro Cassarà
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Correspondence to Patrizio Dazzi .

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Editors and Affiliations

  1. University of Wolverhampton, Wolverhampton, United Kingdom

    Prashant Pillai

  2. RMIT University, Melbourne, Victoria, Australia

    Kandeepan Sithamparanathan

  3. University of Siena, Siena, Italy

    Giovanni Giambene

  4. Centre Tecnològic de Telecomunicacions de Catalunya, Barcelona, Spain

    Miguel Ángel Vázquez

  5. University of York, York, United Kingdom

    Paul Daniel Mitchell

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Dazzi, P., Cassarà, P. (2018). How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs. In: Pillai, P., Sithamparanathan, K., Giambene, G., Vázquez, M., Mitchell, P. (eds) Wireless and Satellite Systems. WiSATS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-319-76571-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-76571-6_11

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  • Online ISBN: 978-3-319-76571-6

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