How to support the machine learning take-off: challenges and hints for achieving intelligent UAVS

Dazzi P., Cassara P.

Decentralized intelligence  Machine learning  Machine-to-machine  UAV  IoT 

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.

Source: WiSATS 2017 - 9th International Conference on Wireless and Satellite Systems, pp. 106–114, Oxford, UK, 14-15 September 2017

Publisher: Springer, Berlin, DEU

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