Wireless and Satellite Systems. 9th International Conference, WiSATS 2017, Oxford, UK, September 14-15, 2017, Proceedings

Research Article

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

  • @INPROCEEDINGS{10.1007/978-3-319-76571-6_11,
        author={Patrizio Dazzi and Pietro Cassar\'{a}},
        title={How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs},
        proceedings={Wireless and Satellite Systems. 9th International Conference, WiSATS 2017, Oxford, UK, September 14-15, 2017, Proceedings},
        proceedings_a={WISATS},
        year={2018},
        month={3},
        keywords={Machine learning UAV Decentralized intelligence Machine-to-machine IoT},
        doi={10.1007/978-3-319-76571-6_11}
    }
    
  • Patrizio Dazzi
    Pietro Cassarà
    Year: 2018
    How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs
    WISATS
    Springer
    DOI: 10.1007/978-3-319-76571-6_11
Patrizio Dazzi1,*, Pietro Cassarà1,*
  • 1: National Research Council of Italy
*Contact email: patrizzio.dazzi@isti.cnr.it, pietro.cassara@isti.cnr.it

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.