Composable Energy Modeling for ML-Driven Drone Applications

Demetris Trihinas, Michalis Agathocleous, Karlen Avogian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We are now witnessing the extensive deployment of drones in a diverse set of applications with Machine Learning (ML) constituting a key enabler empowering the uptake of drone technology. With the advancements of robotics and edge computing, on-board ML is on the uprise. However, testing ML solutions for drones before release to production is a daunting task for ML practitioners. This usually involves the testing on a robotics emulator to collect various key performance indicators ranging from algorithm correctness to resource utilization. Thus, to thoroughly evaluate performance, a true understanding of the ML algorithm impact on the drones most scarce resource is required. Without a doubt, this is the drones battery, which entails continuously monitoring energy consumption. In this paper we introduce HornEt, a modular framework enabling the customization and composition of various monitorable components to produce realistic energy models that can be used during the testing of ML-driven drone applications. To show the wide applicability of our framework, we introduce a proof-of-concept use-case illustrating the energy profiling of a drone application at different levels of granularity.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages231-237
Number of pages7
ISBN (Electronic)9781665449700
DOIs
StatePublished - 2021
Externally publishedYes
Event9th IEEE International Conference on Cloud Engineering, IC2E 2021 - Virtual, Online, United States
Duration: Oct 4 2021Oct 8 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021

Conference

Conference9th IEEE International Conference on Cloud Engineering, IC2E 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/4/2110/8/21

Keywords

  • Drones
  • Energy Modeling
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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